diff --git "a/app.py.backup" "b/app.py.backup" deleted file mode 100644--- "a/app.py.backup" +++ /dev/null @@ -1,7269 +0,0 @@ -import os -import re -from http import HTTPStatus -from typing import Dict, List, Optional, Tuple -import base64 -import mimetypes -import numpy as np -from PIL import Image -import requests -from urllib.parse import urlparse, urljoin -from bs4 import BeautifulSoup -import html2text -import json -import time -import webbrowser -import urllib.parse -import copy -import html - -import gradio as gr -from huggingface_hub import InferenceClient -from huggingface_hub import HfApi -import tempfile -from openai import OpenAI -import uuid -import datetime -from mistralai import Mistral -import shutil -import urllib.parse -import mimetypes -import threading -import atexit -import asyncio -from datetime import datetime, timedelta -from typing import Optional -import dashscope - -# Gradio supported languages for syntax highlighting -GRADIO_SUPPORTED_LANGUAGES = [ - "python", "json", "html", "javascript" -] - -def get_gradio_language(language): - # Map composite options to a supported syntax highlighting - if language == "streamlit": - return "python" - if language == "gradio": - return "python" - if language == "comfyui": - return "json" - if language == "react": - return "javascript" - return language if language in GRADIO_SUPPORTED_LANGUAGES else None - -# Search/Replace Constants -SEARCH_START = "<<<<<<< SEARCH" -DIVIDER = "=======" -REPLACE_END = ">>>>>>> REPLACE" - -# Gradio Documentation Auto-Update System -GRADIO_LLMS_TXT_URL = "https://www.gradio.app/llms.txt" -GRADIO_DOCS_CACHE_FILE = ".gradio_docs_cache.txt" -GRADIO_DOCS_LAST_UPDATE_FILE = ".gradio_docs_last_update.txt" -GRADIO_DOCS_UPDATE_ON_APP_UPDATE = True # Only update when app is updated, not on a timer - -# Global variable to store the current Gradio documentation -_gradio_docs_content: str | None = None -_gradio_docs_last_fetched: Optional[datetime] = None - -# ComfyUI Documentation Auto-Update System -COMFYUI_LLMS_TXT_URL = "https://docs.comfy.org/llms.txt" -COMFYUI_DOCS_CACHE_FILE = ".comfyui_docs_cache.txt" -COMFYUI_DOCS_LAST_UPDATE_FILE = ".comfyui_docs_last_update.txt" -COMFYUI_DOCS_UPDATE_ON_APP_UPDATE = True # Only update when app is updated, not on a timer - -# Global variable to store the current ComfyUI documentation -_comfyui_docs_content: str | None = None -_comfyui_docs_last_fetched: Optional[datetime] = None - -# FastRTC Documentation Auto-Update System -FASTRTC_LLMS_TXT_URL = "https://fastrtc.org/llms.txt" -FASTRTC_DOCS_CACHE_FILE = ".fastrtc_docs_cache.txt" -FASTRTC_DOCS_LAST_UPDATE_FILE = ".fastrtc_docs_last_update.txt" -FASTRTC_DOCS_UPDATE_ON_APP_UPDATE = True # Only update when app is updated, not on a timer - -# Global variable to store the current FastRTC documentation -_fastrtc_docs_content: str | None = None -_fastrtc_docs_last_fetched: Optional[datetime] = None - -def fetch_gradio_docs() -> str | None: - """Fetch the latest Gradio documentation from llms.txt""" - try: - response = requests.get(GRADIO_LLMS_TXT_URL, timeout=10) - response.raise_for_status() - return response.text - except Exception as e: - print(f"Warning: Failed to fetch Gradio docs from {GRADIO_LLMS_TXT_URL}: {e}") - return None - -def fetch_comfyui_docs() -> str | None: - """Fetch the latest ComfyUI documentation from llms.txt""" - try: - response = requests.get(COMFYUI_LLMS_TXT_URL, timeout=10) - response.raise_for_status() - return response.text - except Exception as e: - print(f"Warning: Failed to fetch ComfyUI docs from {COMFYUI_LLMS_TXT_URL}: {e}") - return None - -def fetch_fastrtc_docs() -> str | None: - """Fetch the latest FastRTC documentation from llms.txt""" - try: - response = requests.get(FASTRTC_LLMS_TXT_URL, timeout=10) - response.raise_for_status() - return response.text - except Exception as e: - print(f"Warning: Failed to fetch FastRTC docs from {FASTRTC_LLMS_TXT_URL}: {e}") - return None - -def filter_problematic_instructions(content: str) -> str: - """Filter out problematic instructions that cause LLM to stop generation prematurely""" - if not content: - return content - - # List of problematic phrases that cause early termination when LLM encounters ``` in user code - problematic_patterns = [ - r"Output ONLY the code inside a ``` code block, and do not include any explanations or extra text", - r"output only the code inside a ```.*?``` code block", - r"Always output only the.*?code.*?inside.*?```.*?```.*?block", - r"Return ONLY the code inside a.*?```.*?``` code block", - r"Do NOT add the language name at the top of the code output", - r"do not include any explanations or extra text", - r"Always output only the.*?code blocks.*?shown above, and do not include any explanations", - r"Output.*?ONLY.*?code.*?inside.*?```.*?```", - r"Return.*?ONLY.*?code.*?inside.*?```.*?```", - r"Generate.*?ONLY.*?code.*?inside.*?```.*?```", - r"Provide.*?ONLY.*?code.*?inside.*?```.*?```", - ] - - # Remove problematic patterns - filtered_content = content - for pattern in problematic_patterns: - # Use case-insensitive matching - filtered_content = re.sub(pattern, "", filtered_content, flags=re.IGNORECASE | re.DOTALL) - - # Clean up any double newlines or extra whitespace left by removals - filtered_content = re.sub(r'\n\s*\n\s*\n', '\n\n', filtered_content) - filtered_content = re.sub(r'^\s+', '', filtered_content, flags=re.MULTILINE) - - return filtered_content - -def load_cached_gradio_docs() -> str | None: - """Load cached Gradio documentation from file""" - try: - if os.path.exists(GRADIO_DOCS_CACHE_FILE): - with open(GRADIO_DOCS_CACHE_FILE, 'r', encoding='utf-8') as f: - return f.read() - except Exception as e: - print(f"Warning: Failed to load cached Gradio docs: {e}") - return None - -def save_gradio_docs_cache(content: str): - """Save Gradio documentation to cache file""" - try: - with open(GRADIO_DOCS_CACHE_FILE, 'w', encoding='utf-8') as f: - f.write(content) - with open(GRADIO_DOCS_LAST_UPDATE_FILE, 'w', encoding='utf-8') as f: - f.write(datetime.now().isoformat()) - except Exception as e: - print(f"Warning: Failed to save Gradio docs cache: {e}") - -def load_comfyui_docs_cache() -> str | None: - """Load ComfyUI documentation from cache file""" - try: - if os.path.exists(COMFYUI_DOCS_CACHE_FILE): - with open(COMFYUI_DOCS_CACHE_FILE, 'r', encoding='utf-8') as f: - return f.read() - except Exception as e: - print(f"Warning: Failed to load cached ComfyUI docs: {e}") - return None - -def save_comfyui_docs_cache(content: str): - """Save ComfyUI documentation to cache file""" - try: - with open(COMFYUI_DOCS_CACHE_FILE, 'w', encoding='utf-8') as f: - f.write(content) - with open(COMFYUI_DOCS_LAST_UPDATE_FILE, 'w', encoding='utf-8') as f: - f.write(datetime.now().isoformat()) - except Exception as e: - print(f"Warning: Failed to save ComfyUI docs cache: {e}") - -def load_fastrtc_docs_cache() -> str | None: - """Load FastRTC documentation from cache file""" - try: - if os.path.exists(FASTRTC_DOCS_CACHE_FILE): - with open(FASTRTC_DOCS_CACHE_FILE, 'r', encoding='utf-8') as f: - return f.read() - except Exception as e: - print(f"Warning: Failed to load cached FastRTC docs: {e}") - return None - -def save_fastrtc_docs_cache(content: str): - """Save FastRTC documentation to cache file""" - try: - with open(FASTRTC_DOCS_CACHE_FILE, 'w', encoding='utf-8') as f: - f.write(content) - with open(FASTRTC_DOCS_LAST_UPDATE_FILE, 'w', encoding='utf-8') as f: - f.write(datetime.now().isoformat()) - except Exception as e: - print(f"Warning: Failed to save FastRTC docs cache: {e}") - -def get_last_update_time() -> Optional[datetime]: - """Get the last update time from file""" - try: - if os.path.exists(GRADIO_DOCS_LAST_UPDATE_FILE): - with open(GRADIO_DOCS_LAST_UPDATE_FILE, 'r', encoding='utf-8') as f: - return datetime.fromisoformat(f.read().strip()) - except Exception as e: - print(f"Warning: Failed to read last update time: {e}") - return None - -def should_update_gradio_docs() -> bool: - """Check if Gradio documentation should be updated""" - # Only update if we don't have cached content (first run or cache deleted) - return not os.path.exists(GRADIO_DOCS_CACHE_FILE) - -def should_update_comfyui_docs() -> bool: - """Check if ComfyUI documentation should be updated""" - # Only update if we don't have cached content (first run or cache deleted) - return not os.path.exists(COMFYUI_DOCS_CACHE_FILE) - -def should_update_fastrtc_docs() -> bool: - """Check if FastRTC documentation should be updated""" - # Only update if we don't have cached content (first run or cache deleted) - return not os.path.exists(FASTRTC_DOCS_CACHE_FILE) - -def force_update_gradio_docs(): - """ - Force an update of Gradio documentation (useful when app is updated). - - To manually refresh docs, you can call this function or simply delete the cache file: - rm .gradio_docs_cache.txt && restart the app - """ - global _gradio_docs_content, _gradio_docs_last_fetched - - print("🔄 Forcing Gradio documentation update...") - latest_content = fetch_gradio_docs() - - if latest_content: - # Filter out problematic instructions that cause early termination - filtered_content = filter_problematic_instructions(latest_content) - _gradio_docs_content = filtered_content - _gradio_docs_last_fetched = datetime.now() - save_gradio_docs_cache(filtered_content) - update_gradio_system_prompts() - print("✅ Gradio documentation updated successfully") - return True - else: - print("❌ Failed to update Gradio documentation") - return False - -def force_update_comfyui_docs(): - """ - Force an update of ComfyUI documentation (useful when app is updated). - - To manually refresh docs, you can call this function or simply delete the cache file: - rm .comfyui_docs_cache.txt && restart the app - """ - global _comfyui_docs_content, _comfyui_docs_last_fetched - - print("🔄 Forcing ComfyUI documentation update...") - latest_content = fetch_comfyui_docs() - - if latest_content: - # Filter out problematic instructions that cause early termination - filtered_content = filter_problematic_instructions(latest_content) - _comfyui_docs_content = filtered_content - _comfyui_docs_last_fetched = datetime.now() - save_comfyui_docs_cache(filtered_content) - update_json_system_prompts() - print("✅ ComfyUI documentation updated successfully") - return True - else: - print("❌ Failed to update ComfyUI documentation") - return False - -def force_update_fastrtc_docs(): - """ - Force an update of FastRTC documentation (useful when app is updated). - - To manually refresh docs, you can call this function or simply delete the cache file: - rm .fastrtc_docs_cache.txt && restart the app - """ - global _fastrtc_docs_content, _fastrtc_docs_last_fetched - - print("🔄 Forcing FastRTC documentation update...") - latest_content = fetch_fastrtc_docs() - - if latest_content: - # Filter out problematic instructions that cause early termination - filtered_content = filter_problematic_instructions(latest_content) - _fastrtc_docs_content = filtered_content - _fastrtc_docs_last_fetched = datetime.now() - save_fastrtc_docs_cache(filtered_content) - update_gradio_system_prompts() - print("✅ FastRTC documentation updated successfully") - return True - else: - print("❌ Failed to update FastRTC documentation") - return False - -def get_gradio_docs_content() -> str: - """Get the current Gradio documentation content, updating if necessary""" - global _gradio_docs_content, _gradio_docs_last_fetched - - # Check if we need to update - if (_gradio_docs_content is None or - _gradio_docs_last_fetched is None or - should_update_gradio_docs()): - - print("Updating Gradio documentation...") - - # Try to fetch latest content - latest_content = fetch_gradio_docs() - - if latest_content: - # Filter out problematic instructions that cause early termination - filtered_content = filter_problematic_instructions(latest_content) - _gradio_docs_content = filtered_content - _gradio_docs_last_fetched = datetime.now() - save_gradio_docs_cache(filtered_content) - print("✅ Gradio documentation updated successfully") - else: - # Fallback to cached content - cached_content = load_cached_gradio_docs() - if cached_content: - _gradio_docs_content = cached_content - _gradio_docs_last_fetched = datetime.now() - print("⚠️ Using cached Gradio documentation (network fetch failed)") - else: - # Fallback to minimal content - _gradio_docs_content = """ - # Gradio API Reference (Offline Fallback) - - This is a minimal fallback when documentation cannot be fetched. - Please check your internet connection for the latest API reference. - - Basic Gradio components: Button, Textbox, Slider, Image, Audio, Video, File, etc. - Use gr.Blocks() for custom layouts and gr.Interface() for simple apps. - """ - print("❌ Using minimal fallback documentation") - - return _gradio_docs_content or "" - -def get_comfyui_docs_content() -> str: - """Get the current ComfyUI documentation content, updating if necessary""" - global _comfyui_docs_content, _comfyui_docs_last_fetched - - # Check if we need to update - if (_comfyui_docs_content is None or - _comfyui_docs_last_fetched is None or - should_update_comfyui_docs()): - - print("Updating ComfyUI documentation...") - - # Try to fetch latest content - latest_content = fetch_comfyui_docs() - - if latest_content: - # Filter out problematic instructions that cause early termination - filtered_content = filter_problematic_instructions(latest_content) - _comfyui_docs_content = filtered_content - _comfyui_docs_last_fetched = datetime.now() - save_comfyui_docs_cache(filtered_content) - print("✅ ComfyUI documentation updated successfully") - else: - # Fallback to cached content - cached_content = load_comfyui_docs_cache() - if cached_content: - _comfyui_docs_content = cached_content - _comfyui_docs_last_fetched = datetime.now() - print("⚠️ Using cached ComfyUI documentation (network fetch failed)") - else: - # Fallback to minimal content - _comfyui_docs_content = """ - # ComfyUI API Reference (Offline Fallback) - - This is a minimal fallback when documentation cannot be fetched. - Please check your internet connection for the latest API reference. - - Basic ComfyUI workflow structure: nodes, connections, inputs, outputs. - Use CheckpointLoaderSimple, CLIPTextEncode, KSampler for basic workflows. - """ - print("❌ Using minimal fallback documentation") - - return _comfyui_docs_content or "" - -def get_fastrtc_docs_content() -> str: - """Get the current FastRTC documentation content, updating if necessary""" - global _fastrtc_docs_content, _fastrtc_docs_last_fetched - - # Check if we need to update - if (_fastrtc_docs_content is None or - _fastrtc_docs_last_fetched is None or - should_update_fastrtc_docs()): - - print("Updating FastRTC documentation...") - - # Try to fetch latest content - latest_content = fetch_fastrtc_docs() - - if latest_content: - # Filter out problematic instructions that cause early termination - filtered_content = filter_problematic_instructions(latest_content) - _fastrtc_docs_content = filtered_content - _fastrtc_docs_last_fetched = datetime.now() - save_fastrtc_docs_cache(filtered_content) - print("✅ FastRTC documentation updated successfully") - else: - # Fallback to cached content - cached_content = load_fastrtc_docs_cache() - if cached_content: - _fastrtc_docs_content = cached_content - _fastrtc_docs_last_fetched = datetime.now() - print("⚠️ Using cached FastRTC documentation (network fetch failed)") - else: - # Fallback to minimal content - _fastrtc_docs_content = """ - # FastRTC API Reference (Offline Fallback) - - This is a minimal fallback when documentation cannot be fetched. - Please check your internet connection for the latest API reference. - - Basic FastRTC usage: Stream class, handlers, real-time audio/video processing. - Use Stream(handler, modality, mode) for real-time communication apps. - """ - print("❌ Using minimal fallback documentation") - - return _fastrtc_docs_content or "" - -def update_gradio_system_prompts(): - """Update the global Gradio system prompts with latest documentation""" - global GRADIO_SYSTEM_PROMPT, GRADIO_SYSTEM_PROMPT_WITH_SEARCH - - docs_content = get_gradio_docs_content() - fastrtc_content = get_fastrtc_docs_content() - - # Base system prompt - base_prompt = """You are an expert Gradio developer. Create a complete, working Gradio application based on the user's request. Generate all necessary code to make the application functional and runnable. - -## Multi-File Application Structure - -When creating complex Gradio applications, organize your code into multiple files for better maintainability: - -**File Organization:** -- `app.py` - Main application entry point with Gradio interface -- `utils.py` - Utility functions and helpers -- `models.py` - Model loading and inference functions -- `config.py` - Configuration and constants -- `requirements.txt` - Python dependencies -- Additional modules as needed (e.g., `data_processing.py`, `ui_components.py`) - -**🚨 CRITICAL: DO NOT Generate README.md Files** -- NEVER generate README.md files under any circumstances -- A template README.md is automatically provided and will be overridden by the deployment system -- Generating a README.md will break the deployment process -- Only generate the code files listed above - -**Output Format for Multi-File Apps:** -When generating multi-file applications, use this exact format: - -``` -=== app.py === -[main application code] - -=== utils.py === -[utility functions] - -=== requirements.txt === -[dependencies] -``` - -**🚨 CRITICAL: Always Generate requirements.txt for New Applications** -- ALWAYS include requirements.txt when creating new Gradio applications -- Generate comprehensive, production-ready dependencies based on your code -- Include not just direct imports but also commonly needed companion packages -- Use correct PyPI package names (e.g., PIL → Pillow, sklearn → scikit-learn) -- For diffusers: use `git+https://github.com/huggingface/diffusers` -- For transformers: use `git+https://github.com/huggingface/transformers` -- Include supporting packages (accelerate, torch, tokenizers, etc.) when using ML libraries -- Your requirements.txt should ensure the application works smoothly in production - -**🚨 CRITICAL: requirements.txt Formatting Rules** -- Output ONLY plain text package names, one per line -- Do NOT use markdown formatting (no ```, no bold, no headings, no lists with * or -) -- Do NOT add explanatory text or descriptions -- Do NOT wrap in code blocks -- Just raw package names as they would appear in a real requirements.txt file -- Example of CORRECT format: - gradio - torch - transformers -- Example of INCORRECT format (DO NOT DO THIS): - ``` - gradio # For web interface - **Core dependencies:** - - torch - ``` - -**Single vs Multi-File Decision:** -- Use single file for simple applications (< 100 lines) - but still generate requirements.txt if dependencies exist -- Use multi-file structure for complex applications with: - - Multiple models or processing pipelines - - Extensive utility functions - - Complex UI with many components - - Data processing workflows - - When user specifically requests modular structure - -🚨 IMPORTANT: If the user is asking to use external APIs (like OpenRouter, OpenAI API, Hugging Face Inference API, etc.), DO NOT use @spaces.GPU decorators or any ZeroGPU features. External APIs handle the model inference remotely, so GPU allocation on the Spaces instance is not needed. - -🚨 CRITICAL REQUIREMENT: If the user provides ANY diffusion model code (FLUX, Stable Diffusion, etc.) that runs locally (not via API), you MUST implement ZeroGPU ahead-of-time (AoT) compilation. This is mandatory and provides 1.3x-1.8x performance improvements. Do not create basic Gradio apps without AoT optimization for diffusion models. - -## ZeroGPU Integration (MANDATORY) - -ALWAYS use ZeroGPU for GPU-dependent functions in Gradio apps: - -1. Import the spaces module: `import spaces` -2. Decorate GPU-dependent functions with `@spaces.GPU` -3. Specify appropriate duration based on expected runtime: - - Quick inference (< 30s): `@spaces.GPU(duration=30)` - - Standard generation (30-60s): `@spaces.GPU` (default 60s) - - Complex generation (60-120s): `@spaces.GPU(duration=120)` - - Heavy processing (120-180s): `@spaces.GPU(duration=180)` - -Example usage: -```python -import spaces -from diffusers import DiffusionPipeline - -pipe = DiffusionPipeline.from_pretrained(...) -pipe.to('cuda') - -@spaces.GPU(duration=120) -def generate(prompt): - return pipe(prompt).images - -gr.Interface( - fn=generate, - inputs=gr.Text(), - outputs=gr.Gallery(), -).launch() -``` - -Duration Guidelines: -- Shorter durations improve queue priority for users -- Text-to-image: typically 30-60 seconds -- Image-to-image: typically 20-40 seconds -- Video generation: typically 60-180 seconds -- Audio/music generation: typically 30-90 seconds -- Model loading + inference: add 10-30s buffer -- AoT compilation during startup: use @spaces.GPU(duration=1500) for maximum allowed duration - -Functions that typically need @spaces.GPU: -- Image generation (text-to-image, image-to-image) -- Video generation -- Audio/music generation -- Model inference with transformers, diffusers -- Any function using .to('cuda') or GPU operations - -## CRITICAL: Use ZeroGPU AoT Compilation for ALL Diffusion Models - -FOR ANY DIFFUSION MODEL (FLUX, Stable Diffusion, etc.), YOU MUST IMPLEMENT AHEAD-OF-TIME COMPILATION. -This is NOT optional - it provides 1.3x-1.8x speedup and is essential for production ZeroGPU Spaces. - -ALWAYS implement this pattern for diffusion models: - -### MANDATORY: Basic AoT Compilation Pattern -YOU MUST USE THIS EXACT PATTERN for any diffusion model (FLUX, Stable Diffusion, etc.): - -1. ALWAYS add AoT compilation function with @spaces.GPU(duration=1500) -2. ALWAYS use spaces.aoti_capture to capture inputs -3. ALWAYS use torch.export.export to export the transformer -4. ALWAYS use spaces.aoti_compile to compile -5. ALWAYS use spaces.aoti_apply to apply to pipeline - -### Required AoT Implementation -```python -import spaces -import torch -from diffusers import DiffusionPipeline - -MODEL_ID = 'black-forest-labs/FLUX.1-dev' -pipe = DiffusionPipeline.from_pretrained(MODEL_ID, torch_dtype=torch.bfloat16) -pipe.to('cuda') - -@spaces.GPU(duration=1500) # Maximum duration allowed during startup -def compile_transformer(): - # 1. Capture example inputs - with spaces.aoti_capture(pipe.transformer) as call: - pipe("arbitrary example prompt") - - # 2. Export the model - exported = torch.export.export( - pipe.transformer, - args=call.args, - kwargs=call.kwargs, - ) - - # 3. Compile the exported model - return spaces.aoti_compile(exported) - -# 4. Apply compiled model to pipeline -compiled_transformer = compile_transformer() -spaces.aoti_apply(compiled_transformer, pipe.transformer) - -@spaces.GPU -def generate(prompt): - return pipe(prompt).images -``` - -### Advanced Optimizations - -#### FP8 Quantization (Additional 1.2x speedup on H200) -```python -from torchao.quantization import quantize_, Float8DynamicActivationFloat8WeightConfig - -@spaces.GPU(duration=1500) -def compile_transformer_with_quantization(): - # Quantize before export for FP8 speedup - quantize_(pipe.transformer, Float8DynamicActivationFloat8WeightConfig()) - - with spaces.aoti_capture(pipe.transformer) as call: - pipe("arbitrary example prompt") - - exported = torch.export.export( - pipe.transformer, - args=call.args, - kwargs=call.kwargs, - ) - return spaces.aoti_compile(exported) -``` - -#### Dynamic Shapes (Variable input sizes) -```python -from torch.utils._pytree import tree_map - -@spaces.GPU(duration=1500) -def compile_transformer_dynamic(): - with spaces.aoti_capture(pipe.transformer) as call: - pipe("arbitrary example prompt") - - # Define dynamic dimension ranges (model-dependent) - transformer_hidden_dim = torch.export.Dim('hidden', min=4096, max=8212) - - # Map argument names to dynamic dimensions - transformer_dynamic_shapes = { - "hidden_states": {1: transformer_hidden_dim}, - "img_ids": {0: transformer_hidden_dim}, - } - - # Create dynamic shapes structure - dynamic_shapes = tree_map(lambda v: None, call.kwargs) - dynamic_shapes.update(transformer_dynamic_shapes) - - exported = torch.export.export( - pipe.transformer, - args=call.args, - kwargs=call.kwargs, - dynamic_shapes=dynamic_shapes, - ) - return spaces.aoti_compile(exported) -``` - -#### Multi-Compile for Different Resolutions -```python -@spaces.GPU(duration=1500) -def compile_multiple_resolutions(): - compiled_models = {} - resolutions = [(512, 512), (768, 768), (1024, 1024)] - - for width, height in resolutions: - # Capture inputs for specific resolution - with spaces.aoti_capture(pipe.transformer) as call: - pipe(f"test prompt {width}x{height}", width=width, height=height) - - exported = torch.export.export( - pipe.transformer, - args=call.args, - kwargs=call.kwargs, - ) - compiled_models[f"{width}x{height}"] = spaces.aoti_compile(exported) - - return compiled_models - -# Usage with resolution dispatch -compiled_models = compile_multiple_resolutions() - -@spaces.GPU -def generate_with_resolution(prompt, width=1024, height=1024): - resolution_key = f"{width}x{height}" - if resolution_key in compiled_models: - # Temporarily apply the right compiled model - spaces.aoti_apply(compiled_models[resolution_key], pipe.transformer) - return pipe(prompt, width=width, height=height).images -``` - -#### FlashAttention-3 Integration -```python -from kernels import get_kernel - -# Load pre-built FA3 kernel compatible with H200 -try: - vllm_flash_attn3 = get_kernel("kernels-community/vllm-flash-attn3") - print("✅ FlashAttention-3 kernel loaded successfully") -except Exception as e: - print(f"⚠️ FlashAttention-3 not available: {e}") - -# Custom attention processor example -class FlashAttention3Processor: - def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None): - # Use FA3 kernel for attention computation - return vllm_flash_attn3(hidden_states, encoder_hidden_states, attention_mask) - -# Apply FA3 processor to model -if 'vllm_flash_attn3' in locals(): - for name, module in pipe.transformer.named_modules(): - if hasattr(module, 'processor'): - module.processor = FlashAttention3Processor() -``` - -### Complete Optimized Example -```python -import spaces -import torch -from diffusers import DiffusionPipeline -from torchao.quantization import quantize_, Float8DynamicActivationFloat8WeightConfig - -MODEL_ID = 'black-forest-labs/FLUX.1-dev' -pipe = DiffusionPipeline.from_pretrained(MODEL_ID, torch_dtype=torch.bfloat16) -pipe.to('cuda') - -@spaces.GPU(duration=1500) -def compile_optimized_transformer(): - # Apply FP8 quantization - quantize_(pipe.transformer, Float8DynamicActivationFloat8WeightConfig()) - - # Capture inputs - with spaces.aoti_capture(pipe.transformer) as call: - pipe("optimization test prompt") - - # Export and compile - exported = torch.export.export( - pipe.transformer, - args=call.args, - kwargs=call.kwargs, - ) - return spaces.aoti_compile(exported) - -# Compile during startup -compiled_transformer = compile_optimized_transformer() -spaces.aoti_apply(compiled_transformer, pipe.transformer) - -@spaces.GPU -def generate(prompt): - return pipe(prompt).images -``` - -**Expected Performance Gains:** -- Basic AoT: 1.3x-1.8x speedup -- + FP8 Quantization: Additional 1.2x speedup -- + FlashAttention-3: Additional attention speedup -- Total potential: 2x-3x faster inference -**Hardware Requirements:** -- FP8 quantization requires CUDA compute capability ≥ 9.0 (H200 ✅) -- FlashAttention-3 works on H200 hardware via kernels library -- Dynamic shapes add flexibility for variable input sizes -## MCP Server Integration - -When the user requests an MCP-enabled Gradio app or asks for tool calling capabilities, you MUST enable MCP server functionality. - -**🚨 CRITICAL: Enabling MCP Server** -To make your Gradio app function as an MCP (Model Control Protocol) server: -1. Set `mcp_server=True` in the `.launch()` method -2. Add `"gradio[mcp]"` to requirements.txt (not just `gradio`) -3. Ensure all functions have detailed docstrings with proper Args sections -4. Use type hints for all function parameters - -**Example:** -``` -import gradio as gr - -def letter_counter(word: str, letter: str) -> int: - \"\"\" - Count the number of occurrences of a letter in a word or text. - - Args: - word (str): The input text to search through - letter (str): The letter to search for - - Returns: - int: The number of times the letter appears - \"\"\" - return word.lower().count(letter.lower()) - -demo = gr.Interface( - fn=letter_counter, - inputs=[gr.Textbox("strawberry"), gr.Textbox("r")], - outputs=[gr.Number()], - title="Letter Counter", - description="Count letter occurrences in text." -) - -if __name__ == "__main__": - demo.launch(mcp_server=True) -``` - -**When to Enable MCP:** -- User explicitly requests "MCP server" or "MCP-enabled app" -- User wants tool calling capabilities for LLMs -- User mentions Claude Desktop, Cursor, or Cline integration -- User wants to expose functions as tools for AI assistants - -**MCP Requirements:** -1. **Dependencies:** Always use `gradio[mcp]` in requirements.txt (not plain `gradio`) -2. **Docstrings:** Every function must have a detailed docstring with: - - Brief description on first line - - Args section listing each parameter with type and description - - Returns section (optional but recommended) -3. **Type Hints:** All parameters must have type hints (e.g., `word: str`, `count: int`) -4. **Default Values:** Use default values in components to provide examples - -**Best Practices for MCP Tools:** -- Use descriptive function names (they become tool names) -- Keep functions focused and single-purpose -- Accept string parameters when possible for better compatibility -- Return simple types (str, int, float, list, dict) rather than complex objects -- Use gr.Header for authentication headers when needed -- Use gr.Progress() for long-running operations - -**Multiple Tools Example:** -``` -import gradio as gr - -def add_numbers(a: str, b: str) -> str: - \"\"\" - Add two numbers together. - - Args: - a (str): First number - b (str): Second number - - Returns: - str: Sum of the two numbers - \"\"\" - return str(int(a) + int(b)) - -def multiply_numbers(a: str, b: str) -> str: - \"\"\" - Multiply two numbers. - - Args: - a (str): First number - b (str): Second number - - Returns: - str: Product of the two numbers - \"\"\" - return str(int(a) * int(b)) - -with gr.Blocks() as demo: - gr.Markdown("# Math Tools MCP Server") - - with gr.Tab("Add"): - gr.Interface(add_numbers, [gr.Textbox("5"), gr.Textbox("3")], gr.Textbox()) - - with gr.Tab("Multiply"): - gr.Interface(multiply_numbers, [gr.Textbox("4"), gr.Textbox("7")], gr.Textbox()) - -if __name__ == "__main__": - demo.launch(mcp_server=True) -``` - -**REMEMBER:** If MCP is requested, ALWAYS: -1. Set `mcp_server=True` in `.launch()` -2. Use `gradio[mcp]` in requirements.txt -3. Include complete docstrings with Args sections -4. Add type hints to all parameters - -## Complete Gradio API Reference - -This reference is automatically synced from https://www.gradio.app/llms.txt to ensure accuracy. - -""" - - # Search-enabled prompt - search_prompt = """You are an expert Gradio developer with access to real-time web search. Create a complete, working Gradio application based on the user's request. When needed, use web search to find current best practices or verify latest Gradio features. Generate all necessary code to make the application functional and runnable. - -## Multi-File Application Structure - -When creating complex Gradio applications, organize your code into multiple files for better maintainability: - -**File Organization:** -- `app.py` - Main application entry point with Gradio interface -- `utils.py` - Utility functions and helpers -- `models.py` - Model loading and inference functions -- `config.py` - Configuration and constants -- `requirements.txt` - Python dependencies -- Additional modules as needed (e.g., `data_processing.py`, `ui_components.py`) - -**🚨 CRITICAL: DO NOT Generate README.md Files** -- NEVER generate README.md files under any circumstances -- A template README.md is automatically provided and will be overridden by the deployment system -- Generating a README.md will break the deployment process -- Only generate the code files listed above - -**Output Format for Multi-File Apps:** -When generating multi-file applications, use this exact format: - -``` -=== app.py === -[main application code] - -=== utils.py === -[utility functions] - -=== requirements.txt === -[dependencies] -``` - -**🚨 CRITICAL: requirements.txt Formatting Rules** -- Output ONLY plain text package names, one per line -- Do NOT use markdown formatting (no ```, no bold, no headings, no lists with * or -) -- Do NOT add explanatory text or descriptions -- Do NOT wrap in code blocks -- Just raw package names as they would appear in a real requirements.txt file -- Example of CORRECT format: - gradio - torch - transformers -- Example of INCORRECT format (DO NOT DO THIS): - ``` - gradio # For web interface - **Core dependencies:** - - torch - ``` - -**Single vs Multi-File Decision:** -- Use single file for simple applications (< 100 lines) - but still generate requirements.txt if dependencies exist -- Use multi-file structure for complex applications with: - - Multiple models or processing pipelines - - Extensive utility functions - - Complex UI with many components - - Data processing workflows - - When user specifically requests modular structure - -🚨 IMPORTANT: If the user is asking to use external APIs (like OpenRouter, OpenAI API, Hugging Face Inference API, etc.), DO NOT use @spaces.GPU decorators or any ZeroGPU features. External APIs handle the model inference remotely, so GPU allocation on the Spaces instance is not needed. - -🚨 CRITICAL REQUIREMENT: If the user provides ANY diffusion model code (FLUX, Stable Diffusion, etc.) that runs locally (not via API), you MUST implement ZeroGPU ahead-of-time (AoT) compilation. This is mandatory and provides 1.3x-1.8x performance improvements. Do not create basic Gradio apps without AoT optimization for diffusion models. - -## ZeroGPU Integration (MANDATORY) - -ALWAYS use ZeroGPU for GPU-dependent functions in Gradio apps: - -1. Import the spaces module: `import spaces` -2. Decorate GPU-dependent functions with `@spaces.GPU` -3. Specify appropriate duration based on expected runtime: - - Quick inference (< 30s): `@spaces.GPU(duration=30)` - - Standard generation (30-60s): `@spaces.GPU` (default 60s) - - Complex generation (60-120s): `@spaces.GPU(duration=120)` - - Heavy processing (120-180s): `@spaces.GPU(duration=180)` - -Example usage: -``` -import spaces -from diffusers import DiffusionPipeline - -pipe = DiffusionPipeline.from_pretrained(...) -pipe.to('cuda') - -@spaces.GPU(duration=120) -def generate(prompt): - return pipe(prompt).images - -gr.Interface( - fn=generate, - inputs=gr.Text(), - outputs=gr.Gallery(), -).launch() -``` - -Duration Guidelines: -- Shorter durations improve queue priority for users -- Text-to-image: typically 30-60 seconds -- Image-to-image: typically 20-40 seconds -- Video generation: typically 60-180 seconds -- Audio/music generation: typically 30-90 seconds -- Model loading + inference: add 10-30s buffer -- AoT compilation during startup: use @spaces.GPU(duration=1500) for maximum allowed duration - -Functions that typically need @spaces.GPU: -- Image generation (text-to-image, image-to-image) -- Video generation -- Audio/music generation -- Model inference with transformers, diffusers -- Any function using .to('cuda') or GPU operations - -## CRITICAL: Use ZeroGPU AoT Compilation for ALL Diffusion Models - -FOR ANY DIFFUSION MODEL (FLUX, Stable Diffusion, etc.), YOU MUST IMPLEMENT AHEAD-OF-TIME COMPILATION. -This is NOT optional - it provides 1.3x-1.8x speedup and is essential for production ZeroGPU Spaces. - -ALWAYS implement this pattern for diffusion models: - -### MANDATORY: Basic AoT Compilation Pattern -YOU MUST USE THIS EXACT PATTERN for any diffusion model (FLUX, Stable Diffusion, etc.): - -1. ALWAYS add AoT compilation function with @spaces.GPU(duration=1500) -2. ALWAYS use spaces.aoti_capture to capture inputs -3. ALWAYS use torch.export.export to export the transformer -4. ALWAYS use spaces.aoti_compile to compile -5. ALWAYS use spaces.aoti_apply to apply to pipeline - -### Required AoT Implementation - -For production Spaces with heavy models, use ahead-of-time (AoT) compilation for 1.3x-1.8x speedups: - -### Basic AoT Compilation -``` -import spaces -import torch -from diffusers import DiffusionPipeline - -MODEL_ID = 'black-forest-labs/FLUX.1-dev' -pipe = DiffusionPipeline.from_pretrained(MODEL_ID, torch_dtype=torch.bfloat16) -pipe.to('cuda') - -@spaces.GPU(duration=1500) # Maximum duration allowed during startup -def compile_transformer(): - # 1. Capture example inputs - with spaces.aoti_capture(pipe.transformer) as call: - pipe("arbitrary example prompt") - - # 2. Export the model - exported = torch.export.export( - pipe.transformer, - args=call.args, - kwargs=call.kwargs, - ) - - # 3. Compile the exported model - return spaces.aoti_compile(exported) - -# 4. Apply compiled model to pipeline -compiled_transformer = compile_transformer() -spaces.aoti_apply(compiled_transformer, pipe.transformer) - -@spaces.GPU -def generate(prompt): - return pipe(prompt).images -``` - -### Advanced Optimizations - -#### FP8 Quantization (Additional 1.2x speedup on H200) -``` -from torchao.quantization import quantize_, Float8DynamicActivationFloat8WeightConfig - -@spaces.GPU(duration=1500) -def compile_transformer_with_quantization(): - # Quantize before export for FP8 speedup - quantize_(pipe.transformer, Float8DynamicActivationFloat8WeightConfig()) - - with spaces.aoti_capture(pipe.transformer) as call: - pipe("arbitrary example prompt") - - exported = torch.export.export( - pipe.transformer, - args=call.args, - kwargs=call.kwargs, - ) - return spaces.aoti_compile(exported) -``` - -#### Dynamic Shapes (Variable input sizes) -``` -from torch.utils._pytree import tree_map - -@spaces.GPU(duration=1500) -def compile_transformer_dynamic(): - with spaces.aoti_capture(pipe.transformer) as call: - pipe("arbitrary example prompt") - - # Define dynamic dimension ranges (model-dependent) - transformer_hidden_dim = torch.export.Dim('hidden', min=4096, max=8212) - - # Map argument names to dynamic dimensions - transformer_dynamic_shapes = { - "hidden_states": {1: transformer_hidden_dim}, - "img_ids": {0: transformer_hidden_dim}, - } - - # Create dynamic shapes structure - dynamic_shapes = tree_map(lambda v: None, call.kwargs) - dynamic_shapes.update(transformer_dynamic_shapes) - - exported = torch.export.export( - pipe.transformer, - args=call.args, - kwargs=call.kwargs, - dynamic_shapes=dynamic_shapes, - ) - return spaces.aoti_compile(exported) -``` - -#### Multi-Compile for Different Resolutions -``` -@spaces.GPU(duration=1500) -def compile_multiple_resolutions(): - compiled_models = {} - resolutions = [(512, 512), (768, 768), (1024, 1024)] - - for width, height in resolutions: - # Capture inputs for specific resolution - with spaces.aoti_capture(pipe.transformer) as call: - pipe(f"test prompt {width}x{height}", width=width, height=height) - - exported = torch.export.export( - pipe.transformer, - args=call.args, - kwargs=call.kwargs, - ) - compiled_models[f"{width}x{height}"] = spaces.aoti_compile(exported) - - return compiled_models - -# Usage with resolution dispatch -compiled_models = compile_multiple_resolutions() - -@spaces.GPU -def generate_with_resolution(prompt, width=1024, height=1024): - resolution_key = f"{width}x{height}" - if resolution_key in compiled_models: - # Temporarily apply the right compiled model - spaces.aoti_apply(compiled_models[resolution_key], pipe.transformer) - return pipe(prompt, width=width, height=height).images -``` - -#### FlashAttention-3 Integration -``` -from kernels import get_kernel - -# Load pre-built FA3 kernel compatible with H200 -try: - vllm_flash_attn3 = get_kernel("kernels-community/vllm-flash-attn3") - print("✅ FlashAttention-3 kernel loaded successfully") -except Exception as e: - print(f"⚠️ FlashAttention-3 not available: {e}") - -# Custom attention processor example -class FlashAttention3Processor: - def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None): - # Use FA3 kernel for attention computation - return vllm_flash_attn3(hidden_states, encoder_hidden_states, attention_mask) - -# Apply FA3 processor to model -if 'vllm_flash_attn3' in locals(): - for name, module in pipe.transformer.named_modules(): - if hasattr(module, 'processor'): - module.processor = FlashAttention3Processor() -``` - -### Complete Optimized Example -``` -import spaces -import torch -from diffusers import DiffusionPipeline -from torchao.quantization import quantize_, Float8DynamicActivationFloat8WeightConfig - -MODEL_ID = 'black-forest-labs/FLUX.1-dev' -pipe = DiffusionPipeline.from_pretrained(MODEL_ID, torch_dtype=torch.bfloat16) -pipe.to('cuda') - -@spaces.GPU(duration=1500) -def compile_optimized_transformer(): - # Apply FP8 quantization - quantize_(pipe.transformer, Float8DynamicActivationFloat8WeightConfig()) - - # Capture inputs - with spaces.aoti_capture(pipe.transformer) as call: - pipe("optimization test prompt") - - # Export and compile - exported = torch.export.export( - pipe.transformer, - args=call.args, - kwargs=call.kwargs, - ) - return spaces.aoti_compile(exported) - -# Compile during startup -compiled_transformer = compile_optimized_transformer() -spaces.aoti_apply(compiled_transformer, pipe.transformer) - -@spaces.GPU -def generate(prompt): - return pipe(prompt).images -``` - -**Expected Performance Gains:** -- Basic AoT: 1.3x-1.8x speedup -- + FP8 Quantization: Additional 1.2x speedup -- + FlashAttention-3: Additional attention speedup -- Total potential: 2x-3x faster inference - -**Hardware Requirements:** -- FP8 quantization requires CUDA compute capability ≥ 9.0 (H200 ✅) -- FlashAttention-3 works on H200 hardware via kernels library -- Dynamic shapes add flexibility for variable input sizes - -## MCP Server Integration - -When the user requests an MCP-enabled Gradio app or asks for tool calling capabilities, you MUST enable MCP server functionality. - -**🚨 CRITICAL: Enabling MCP Server** -To make your Gradio app function as an MCP (Model Control Protocol) server: -1. Set `mcp_server=True` in the `.launch()` method -2. Add `"gradio[mcp]"` to requirements.txt (not just `gradio`) -3. Ensure all functions have detailed docstrings with proper Args sections -4. Use type hints for all function parameters - -**Example:** -``` -import gradio as gr - -def letter_counter(word: str, letter: str) -> int: - \"\"\" - Count the number of occurrences of a letter in a word or text. - - Args: - word (str): The input text to search through - letter (str): The letter to search for - - Returns: - int: The number of times the letter appears - \"\"\" - return word.lower().count(letter.lower()) - -demo = gr.Interface( - fn=letter_counter, - inputs=[gr.Textbox("strawberry"), gr.Textbox("r")], - outputs=[gr.Number()], - title="Letter Counter", - description="Count letter occurrences in text." -) - -if __name__ == "__main__": - demo.launch(mcp_server=True) -``` - -**When to Enable MCP:** -- User explicitly requests "MCP server" or "MCP-enabled app" -- User wants tool calling capabilities for LLMs -- User mentions Claude Desktop, Cursor, or Cline integration -- User wants to expose functions as tools for AI assistants - -**MCP Requirements:** -1. **Dependencies:** Always use `gradio[mcp]` in requirements.txt (not plain `gradio`) -2. **Docstrings:** Every function must have a detailed docstring with: - - Brief description on first line - - Args section listing each parameter with type and description - - Returns section (optional but recommended) -3. **Type Hints:** All parameters must have type hints (e.g., `word: str`, `count: int`) -4. **Default Values:** Use default values in components to provide examples - -**Best Practices for MCP Tools:** -- Use descriptive function names (they become tool names) -- Keep functions focused and single-purpose -- Accept string parameters when possible for better compatibility -- Return simple types (str, int, float, list, dict) rather than complex objects -- Use gr.Header for authentication headers when needed -- Use gr.Progress() for long-running operations - -**Multiple Tools Example:** -``` -import gradio as gr - -def add_numbers(a: str, b: str) -> str: - \"\"\" - Add two numbers together. - - Args: - a (str): First number - b (str): Second number - - Returns: - str: Sum of the two numbers - \"\"\" - return str(int(a) + int(b)) - -def multiply_numbers(a: str, b: str) -> str: - \"\"\" - Multiply two numbers. - - Args: - a (str): First number - b (str): Second number - - Returns: - str: Product of the two numbers - \"\"\" - return str(int(a) * int(b)) - -with gr.Blocks() as demo: - gr.Markdown("# Math Tools MCP Server") - - with gr.Tab("Add"): - gr.Interface(add_numbers, [gr.Textbox("5"), gr.Textbox("3")], gr.Textbox()) - - with gr.Tab("Multiply"): - gr.Interface(multiply_numbers, [gr.Textbox("4"), gr.Textbox("7")], gr.Textbox()) - -if __name__ == "__main__": - demo.launch(mcp_server=True) -``` - -**REMEMBER:** If MCP is requested, ALWAYS: -1. Set `mcp_server=True` in `.launch()` -2. Use `gradio[mcp]` in requirements.txt -3. Include complete docstrings with Args sections -4. Add type hints to all parameters - -## Complete Gradio API Reference - -This reference is automatically synced from https://www.gradio.app/llms.txt to ensure accuracy. - -""" - - # Add FastRTC documentation if available - if fastrtc_content.strip(): - fastrtc_section = f""" -## FastRTC Reference Documentation - -When building real-time audio/video applications with Gradio, use this FastRTC reference: - -{fastrtc_content} - -This reference is automatically synced from https://fastrtc.org/llms.txt to ensure accuracy. - -""" - base_prompt += fastrtc_section - search_prompt += fastrtc_section - - # Update the prompts - GRADIO_SYSTEM_PROMPT = base_prompt + docs_content + "\n\nAlways use the exact function signatures from this API reference and follow modern Gradio patterns.\n\nIMPORTANT: Always include \"Built with anycoder\" as clickable text in the header/top section of your application that links to https://huggingface.co/spaces/akhaliq/anycoder" - GRADIO_SYSTEM_PROMPT_WITH_SEARCH = search_prompt + docs_content + "\n\nAlways use the exact function signatures from this API reference and follow modern Gradio patterns.\n\nIMPORTANT: Always include \"Built with anycoder\" as clickable text in the header/top section of your application that links to https://huggingface.co/spaces/akhaliq/anycoder" - -def update_json_system_prompts(): - """Update the global JSON system prompts with latest ComfyUI documentation""" - global JSON_SYSTEM_PROMPT, JSON_SYSTEM_PROMPT_WITH_SEARCH - - docs_content = get_comfyui_docs_content() - - # Base system prompt for regular JSON - base_prompt = """You are an expert JSON developer. Generate clean, valid JSON data based on the user's request. Follow JSON syntax rules strictly: -- Use double quotes for strings -- No trailing commas -- Proper nesting and structure -- Valid data types (string, number, boolean, null, object, array) - -Generate ONLY the JSON data requested - no HTML, no applications, no explanations outside the JSON. The output should be pure, valid JSON that can be parsed directly. - -""" - - # Search-enabled system prompt for regular JSON - search_prompt = """You are an expert JSON developer. You have access to real-time web search. When needed, use web search to find the latest information or data structures for your JSON generation. - -Generate clean, valid JSON data based on the user's request. Follow JSON syntax rules strictly: -- Use double quotes for strings -- No trailing commas -- Proper nesting and structure -- Valid data types (string, number, boolean, null, object, array) - -Generate ONLY the JSON data requested - no HTML, no applications, no explanations outside the JSON. The output should be pure, valid JSON that can be parsed directly. - -""" - - # Add ComfyUI documentation if available - if docs_content.strip(): - comfyui_section = f""" -## ComfyUI Reference Documentation - -When generating JSON data related to ComfyUI workflows, nodes, or configurations, use this reference: - -{docs_content} - -This reference is automatically synced from https://docs.comfy.org/llms.txt to ensure accuracy. - -""" - base_prompt += comfyui_section - search_prompt += comfyui_section - - # Update the prompts - JSON_SYSTEM_PROMPT = base_prompt - JSON_SYSTEM_PROMPT_WITH_SEARCH = search_prompt - -def get_comfyui_system_prompt(): - """Get ComfyUI-specific system prompt with enhanced guidance""" - docs_content = get_comfyui_docs_content() - - base_prompt = """You are an expert ComfyUI developer. Generate clean, valid JSON workflows for ComfyUI based on the user's request. - -ComfyUI workflows are JSON structures that define: -- Nodes: Individual processing units with specific functions -- Connections: Links between nodes that define data flow -- Parameters: Configuration values for each node -- Inputs/Outputs: Data flow between nodes - -Follow JSON syntax rules strictly: -- Use double quotes for strings -- No trailing commas -- Proper nesting and structure -- Valid data types (string, number, boolean, null, object, array) - -Generate ONLY the ComfyUI workflow JSON - no HTML, no applications, no explanations outside the JSON. The output should be a complete, valid ComfyUI workflow that can be loaded directly into ComfyUI. - -""" - - # Add ComfyUI documentation if available - if docs_content.strip(): - comfyui_section = f""" -## ComfyUI Reference Documentation - -Use this reference for accurate node types, parameters, and workflow structures: - -{docs_content} - -This reference is automatically synced from https://docs.comfy.org/llms.txt to ensure accuracy. - -""" - base_prompt += comfyui_section - - base_prompt += """ -IMPORTANT: Always include "Built with anycoder" as a comment or metadata field in your ComfyUI workflow JSON that references https://huggingface.co/spaces/akhaliq/anycoder -""" - - return base_prompt - -# Initialize Gradio documentation on startup -def initialize_gradio_docs(): - """Initialize Gradio documentation on application startup""" - try: - update_gradio_system_prompts() - if should_update_gradio_docs(): - print("🚀 Gradio documentation system initialized (fetched fresh content)") - else: - print("🚀 Gradio documentation system initialized (using cached content)") - except Exception as e: - print(f"Warning: Failed to initialize Gradio documentation: {e}") - -# Initialize ComfyUI documentation on startup -def initialize_comfyui_docs(): - """Initialize ComfyUI documentation on application startup""" - try: - update_json_system_prompts() - if should_update_comfyui_docs(): - print("🚀 ComfyUI documentation system initialized (fetched fresh content)") - else: - print("🚀 ComfyUI documentation system initialized (using cached content)") - except Exception as e: - print(f"Warning: Failed to initialize ComfyUI documentation: {e}") - -# Initialize FastRTC documentation on startup -def initialize_fastrtc_docs(): - """Initialize FastRTC documentation on application startup""" - try: - # FastRTC docs are integrated into Gradio system prompts - # So we call update_gradio_system_prompts to include FastRTC content - update_gradio_system_prompts() - if should_update_fastrtc_docs(): - print("🚀 FastRTC documentation system initialized (fetched fresh content)") - else: - print("🚀 FastRTC documentation system initialized (using cached content)") - except Exception as e: - print(f"Warning: Failed to initialize FastRTC documentation: {e}") - -# Configuration -HTML_SYSTEM_PROMPT = """ONLY USE HTML, CSS AND JAVASCRIPT. If you want to use ICON make sure to import the library first. Try to create the best UI possible by using only HTML, CSS and JAVASCRIPT. MAKE IT RESPONSIVE USING MODERN CSS. Use as much as you can modern CSS for the styling, if you can't do something with modern CSS, then use custom CSS. Also, try to elaborate as much as you can, to create something unique. ALWAYS GIVE THE RESPONSE INTO A SINGLE HTML FILE - -**🚨 CRITICAL: DO NOT Generate README.md Files** -- NEVER generate README.md files under any circumstances -- A template README.md is automatically provided and will be overridden by the deployment system -- Generating a README.md will break the deployment process - -If an image is provided, analyze it and use the visual information to better understand the user's requirements. - -Always respond with code that can be executed or rendered directly. - -Generate complete, working HTML code that can be run immediately. - -IMPORTANT: Always include "Built with anycoder" as clickable text in the header/top section of your application that links to https://huggingface.co/spaces/akhaliq/anycoder""" - - - -# Stricter prompt for GLM-4.5V to ensure a complete, runnable HTML document with no escaped characters -GLM45V_HTML_SYSTEM_PROMPT = """You are an expert front-end developer. - -**🚨 CRITICAL: DO NOT Generate README.md Files** -- NEVER generate README.md files under any circumstances -- A template README.md is automatically provided and will be overridden by the deployment system -- Generating a README.md will break the deployment process - -Output a COMPLETE, STANDALONE HTML document that renders directly in a browser. - -Hard constraints: -- DO NOT use React, ReactDOM, JSX, Babel, Vue, Angular, or any SPA framework. -- Use ONLY plain HTML, CSS, and vanilla JavaScript. -- Allowed external resources: Tailwind CSS CDN, Font Awesome CDN, Google Fonts. -- Do NOT escape characters (no \\n, \\t, or escaped quotes). Output raw HTML/JS/CSS. -Structural requirements: -- Include , , , and with proper nesting -- Include required tags for any CSS you reference (e.g., Tailwind, Font Awesome, Google Fonts) -- Keep everything in ONE file; inline CSS/JS as needed - -Generate complete, working HTML code that can be run immediately. - -IMPORTANT: Always include "Built with anycoder" as clickable text in the header/top section of your application that links to https://huggingface.co/spaces/akhaliq/anycoder -""" - -TRANSFORMERS_JS_SYSTEM_PROMPT = """You are an expert web developer creating a transformers.js application. You will generate THREE separate files: index.html, index.js, and style.css. - -**🚨 CRITICAL: DO NOT Generate README.md Files** -- NEVER generate README.md files under any circumstances -- A template README.md is automatically provided and will be overridden by the deployment system -- Generating a README.md will break the deployment process - -IMPORTANT: You MUST output ALL THREE files in the following format: - -```html - -``` - -```javascript -// index.js content here -``` - -```css -/* style.css content here */ -``` - -Requirements: -1. Create a modern, responsive web application using transformers.js -2. Use the transformers.js library for AI/ML functionality -3. Create a clean, professional UI with good user experience -4. Make the application fully responsive for mobile devices -5. Use modern CSS practices and JavaScript ES6+ features -6. Include proper error handling and loading states -7. Follow accessibility best practices - -Library import (required): Add the following snippet to index.html to import transformers.js: - - -Device Options: By default, transformers.js runs on CPU (via WASM). For better performance, you can run models on GPU using WebGPU: -- CPU (default): const pipe = await pipeline('task', 'model-name'); -- GPU (WebGPU): const pipe = await pipeline('task', 'model-name', { device: 'webgpu' }); - -Consider providing users with a toggle option to choose between CPU and GPU execution based on their browser's WebGPU support. - -The index.html should contain the basic HTML structure and link to the CSS and JS files. -The index.js should contain all the JavaScript logic including transformers.js integration. -The style.css should contain all the styling for the application. - -Generate complete, working code files as shown above. - -IMPORTANT: Always include "Built with anycoder" as clickable text in the header/top section of your application that links to https://huggingface.co/spaces/akhaliq/anycoder""" - -STREAMLIT_SYSTEM_PROMPT = """You are an expert Streamlit developer. Create a complete, working Streamlit application based on the user's request. Generate all necessary code to make the application functional and runnable. - -## Multi-File Application Structure - -When creating complex Streamlit applications, organize your code into multiple files for better maintainability: - -**File Organization:** -- `app.py` or `streamlit_app.py` - Main application entry point -- `utils.py` - Utility functions and helpers -- `models.py` - Model loading and inference functions -- `config.py` - Configuration and constants -- `requirements.txt` - Python dependencies -- `pages/` - Additional pages for multi-page apps -- Additional modules as needed (e.g., `data_processing.py`, `components.py`) - -**🚨 CRITICAL: DO NOT Generate README.md Files** -- NEVER generate README.md files under any circumstances -- A template README.md is automatically provided and will be overridden by the deployment system -- Generating a README.md will break the deployment process -- Only generate the code files listed above - -**Output Format for Multi-File Apps:** -When generating multi-file applications, use this exact format: - -``` -=== streamlit_app.py === -[main application code] - -=== utils.py === -[utility functions] - -=== requirements.txt === -[dependencies] -``` - -**🚨 CRITICAL: requirements.txt Formatting Rules** -- Output ONLY plain text package names, one per line -- Do NOT use markdown formatting (no ```, no bold, no headings, no lists with * or -) -- Do NOT add explanatory text or descriptions -- Do NOT wrap in code blocks -- Just raw package names as they would appear in a real requirements.txt file -- Example of CORRECT format: - streamlit - pandas - numpy -- Example of INCORRECT format (DO NOT DO THIS): - ``` - streamlit # For web interface - **Core dependencies:** - - pandas - ``` - -**Single vs Multi-File Decision:** -- Use single file for simple applications (< 100 lines) - but still generate requirements.txt if dependencies exist -- Use multi-file structure for complex applications with: - - Multiple pages or sections - - Extensive data processing - - Complex UI components - - Multiple models or APIs - - When user specifically requests modular structure - -**Multi-Page Apps:** -For multi-page Streamlit apps, use the pages/ directory structure: -``` -=== streamlit_app.py === -[main page] - -=== pages/1_📊_Analytics.py === -[analytics page] - -=== pages/2_⚙️_Settings.py === -[settings page] -``` - -Requirements: -1. Create a modern, responsive Streamlit application -2. Use appropriate Streamlit components and layouts -3. Include proper error handling and loading states -4. Follow Streamlit best practices for performance -5. Use caching (@st.cache_data, @st.cache_resource) appropriately -6. Include proper session state management when needed -7. Make the UI intuitive and user-friendly -8. Add helpful tooltips and documentation - -IMPORTANT: Always include "Built with anycoder" as clickable text in the header/top section of your application that links to https://huggingface.co/spaces/akhaliq/anycoder -""" - -REACT_SYSTEM_PROMPT = """You are an expert React and Next.js developer creating a modern Next.js application. - -**🚨 CRITICAL: DO NOT Generate README.md Files** -|- NEVER generate README.md files under any circumstances -|- A template README.md is automatically provided and will be overridden by the deployment system -|- Generating a README.md will break the deployment process - -You will generate a Next.js project with TypeScript/JSX components. Follow this exact structure: - -Project Structure: -- Dockerfile (Docker configuration for deployment) -- package.json (dependencies and scripts) -- next.config.js (Next.js configuration) -- postcss.config.js (PostCSS configuration) -- tailwind.config.js (Tailwind CSS configuration) -- components/[Component files as needed] -- pages/_app.js (Next.js app wrapper) -- pages/index.js (home page) -- pages/api/[API routes as needed] -- styles/globals.css (global styles) - -Output format (CRITICAL): -- Return ONLY a series of file sections, each starting with a filename line: - === Dockerfile === - ...file content... - - === package.json === - ...file content... - - (repeat for all files) -- Do NOT wrap files in Markdown code fences or use === markers inside file content - -CRITICAL Requirements: -1. Always include a Dockerfile configured for Node.js deployment (see Dockerfile Requirements below) -2. Use Next.js with TypeScript/JSX (.jsx files for components) -3. Include Tailwind CSS for styling (in postcss.config.js and tailwind.config.js) -4. Create necessary components in the components/ directory -5. Create API routes in pages/api/ directory for backend logic -6. pages/_app.js should import and use globals.css -7. pages/index.js should be the main entry point -8. Keep package.json with essential dependencies -9. Use modern React patterns and best practices -10. Make the application fully responsive -11. Include proper error handling and loading states -12. Follow accessibility best practices -13. Configure next.config.js properly for HuggingFace Spaces deployment - -next.config.js Requirements: -- Must be configured to work on any host (0.0.0.0) -- Should not have hardcoded localhost references -- Example minimal configuration: -```javascript -/** @type {import('next').NextConfig} */ -const nextConfig = { - reactStrictMode: true, - // Allow the app to work on HuggingFace Spaces - output: 'standalone', -} - -module.exports = nextConfig -``` - -Dockerfile Requirements (CRITICAL for HuggingFace Spaces): -- Use Node.js 18+ base image (e.g., FROM node:18-slim) -- Set up a user with ID 1000 for proper permissions: - ``` - RUN useradd -m -u 1000 user - USER user - ENV HOME=/home/user \\ - PATH=/home/user/.local/bin:$PATH - WORKDIR $HOME/app - ``` -- ALWAYS use --chown=user with COPY and ADD commands: - ``` - COPY --chown=user package*.json ./ - COPY --chown=user . . - ``` -- Install dependencies: RUN npm install -- Build the app: RUN npm run build -- Expose port 7860 (HuggingFace Spaces default): EXPOSE 7860 -- Start with: CMD ["npm", "start", "--", "-p", "7860"] -- If using a different port, make sure to set app_port in the README.md YAML frontmatter - -Example Dockerfile structure: -```dockerfile -FROM node:18-slim - -# Set up user with ID 1000 -RUN useradd -m -u 1000 user -USER user -ENV HOME=/home/user \\ - PATH=/home/user/.local/bin:$PATH - -# Set working directory -WORKDIR $HOME/app - -# Copy package files with proper ownership -COPY --chown=user package*.json ./ - -# Install dependencies -RUN npm install - -# Copy rest of the application with proper ownership -COPY --chown=user . . - -# Build the Next.js app -RUN npm run build - -# Expose port 7860 -EXPOSE 7860 - -# Start the application on port 7860 -CMD ["npm", "start", "--", "-p", "7860"] -``` - -IMPORTANT: Always include "Built with anycoder" as clickable text in the header/top section of your application that links to https://huggingface.co/spaces/akhaliq/anycoder -""" - -REACT_FOLLOW_UP_SYSTEM_PROMPT = """You are an expert React and Next.js developer modifying an existing Next.js application. -The user wants to apply changes based on their request. -You MUST output ONLY the changes required using the following SEARCH/REPLACE block format. Do NOT output the entire file. -Explain the changes briefly *before* the blocks if necessary, but the code changes THEMSELVES MUST be within the blocks. - -Format Rules: -1. Start with <<<<<<< SEARCH -2. Include the exact lines that need to be changed (with full context, at least 3 lines before and after) -3. Follow with ======= -4. Include the replacement lines -5. End with >>>>>>> REPLACE -6. Generate multiple blocks if multiple sections need changes - -IMPORTANT: Always include "Built with anycoder" as clickable text in the header/top section of your application that links to https://huggingface.co/spaces/akhaliq/anycoder""" - - - -# Gradio system prompts will be dynamically populated by update_gradio_system_prompts() -GRADIO_SYSTEM_PROMPT = "" -GRADIO_SYSTEM_PROMPT_WITH_SEARCH = "" - -# GRADIO_SYSTEM_PROMPT_WITH_SEARCH will be dynamically populated by update_gradio_system_prompts() - -# All Gradio API documentation is now dynamically loaded from https://www.gradio.app/llms.txt - -# JSON system prompts will be dynamically populated by update_json_system_prompts() -JSON_SYSTEM_PROMPT = "" -JSON_SYSTEM_PROMPT_WITH_SEARCH = "" - -# All ComfyUI API documentation is now dynamically loaded from https://docs.comfy.org/llms.txt - -GENERIC_SYSTEM_PROMPT = """You are an expert {language} developer. Write clean, idiomatic, and runnable {language} code for the user's request. If possible, include comments and best practices. Generate complete, working code that can be run immediately. If the user provides a file or other context, use it as a reference. If the code is for a script or app, make it as self-contained as possible. - -**🚨 CRITICAL: DO NOT Generate README.md Files** -- NEVER generate README.md files under any circumstances -- A template README.md is automatically provided and will be overridden by the deployment system -- Generating a README.md will break the deployment process - -IMPORTANT: Always include "Built with anycoder" as clickable text in the header/top section of your application that links to https://huggingface.co/spaces/akhaliq/anycoder""" - - -# Multi-page static HTML project prompt (generic, production-style structure) -MULTIPAGE_HTML_SYSTEM_PROMPT = """You are an expert front-end developer. - -**🚨 CRITICAL: DO NOT Generate README.md Files** -- NEVER generate README.md files under any circumstances -- A template README.md is automatically provided and will be overridden by the deployment system -- Generating a README.md will break the deployment process - -Create a production-ready MULTI-PAGE website using ONLY HTML, CSS, and vanilla JavaScript. Do NOT use SPA frameworks. - -Output MUST be a multi-file project with at least: -- index.html (home) -- about.html (secondary page) -- contact.html (secondary page) -- assets/css/styles.css (global styles) -- assets/js/main.js (site-wide JS) - -Navigation requirements: -- A consistent header with a nav bar on every page -- Highlight current nav item -- Responsive layout and accessibility best practices - -Output format requirements (CRITICAL): -- Return ONLY a series of file sections, each starting with a filename line: - === index.html === - ...file content... - - === about.html === - ...file content... - - (repeat for all files) -- Do NOT wrap files in Markdown code fences -- Use relative paths between files (e.g., assets/css/styles.css) - -General requirements: -- Use modern, semantic HTML -- Mobile-first responsive design -- Include basic SEO meta tags in -- Include a footer on all pages -- Avoid external CSS/JS frameworks (optional: CDN fonts/icons allowed) - -IMPORTANT: Always include "Built with anycoder" as clickable text in the header/top section of your application that links to https://huggingface.co/spaces/akhaliq/anycoder -""" - - -# Dynamic multi-page (model decides files) prompts -DYNAMIC_MULTIPAGE_HTML_SYSTEM_PROMPT = """You are an expert front-end developer. - -**🚨 CRITICAL: DO NOT Generate README.md Files** -- NEVER generate README.md files under any circumstances -- A template README.md is automatically provided and will be overridden by the deployment system -- Generating a README.md will break the deployment process - -Create a production-ready website using ONLY HTML, CSS, and vanilla JavaScript. Do NOT use SPA frameworks. - -File selection policy: -- Generate ONLY the files actually needed for the user's request. -- Include at least one HTML entrypoint (default: index.html) unless the user explicitly requests a non-HTML asset only. -- If any local asset (CSS/JS/image) is referenced, include that file in the output. -- Use relative paths between files (e.g., assets/css/styles.css). - -Output format (CRITICAL): -- Return ONLY a series of file sections, each starting with a filename line: - === index.html === - ...file content... - - === assets/css/styles.css === - ...file content... - - (repeat for all files) -- Do NOT wrap files in Markdown code fences - -General requirements: -- Use modern, semantic HTML -- Mobile-first responsive design -- Include basic SEO meta tags in for the entrypoint -- Include a footer on all major pages when multiple pages are present -- Avoid external CSS/JS frameworks (optional: CDN fonts/icons allowed) - -IMPORTANT: Always include "Built with anycoder" as clickable text in the header/top section of your application that links to https://huggingface.co/spaces/akhaliq/anycoder -""" - - - -# Follow-up system prompt for modifying existing HTML files -FollowUpSystemPrompt = f"""You are an expert web developer modifying an existing project. -The user wants to apply changes based on their request. -You MUST output ONLY the changes required using the following SEARCH/REPLACE block format. Do NOT output the entire file. -Explain the changes briefly *before* the blocks if necessary, but the code changes THEMSELVES MUST be within the blocks. - -IMPORTANT: When the user reports an ERROR MESSAGE, analyze it carefully to determine which file needs fixing: -- ImportError/ModuleNotFoundError → Fix requirements.txt by adding missing packages -- Syntax errors in Python code → Fix app.py or the main Python file -- HTML/CSS/JavaScript errors → Fix the respective HTML/CSS/JS files -- Configuration errors → Fix config files, Docker files, etc. - -For Python applications (Gradio/Streamlit), the project structure typically includes: -- app.py or streamlit_app.py (main application file) -- requirements.txt (dependencies) -- utils.py (utility functions) -- models.py (model loading and inference) -- config.py (configuration) -- pages/ (for multi-page Streamlit apps) -- Other supporting files as needed - -**🚨 CRITICAL: DO NOT Generate README.md Files** -- NEVER generate README.md files under any circumstances -- A template README.md is automatically provided and will be overridden by the deployment system -- Generating a README.md will break the deployment process - -For multi-file projects, identify which specific file needs modification based on the user's request: -- Main application logic → app.py or streamlit_app.py -- Helper functions → utils.py -- Model-related code → models.py -- Configuration changes → config.py -- Dependencies → requirements.txt -- New pages → pages/filename.py - -Format Rules: -1. Start with {SEARCH_START} -2. Provide the exact lines from the current code that need to be replaced. -3. Use {DIVIDER} to separate the search block from the replacement. -4. Provide the new lines that should replace the original lines. -5. End with {REPLACE_END} -6. You can use multiple SEARCH/REPLACE blocks if changes are needed in different parts of the file. -7. To insert code, use an empty SEARCH block (only {SEARCH_START} and {DIVIDER} on their lines) if inserting at the very beginning, otherwise provide the line *before* the insertion point in the SEARCH block and include that line plus the new lines in the REPLACE block. -8. To delete code, provide the lines to delete in the SEARCH block and leave the REPLACE block empty (only {DIVIDER} and {REPLACE_END} on their lines). -9. IMPORTANT: The SEARCH block must *exactly* match the current code, including indentation and whitespace. -10. For multi-file projects, specify which file you're modifying by starting with the filename before the search/replace block. - - CSS Changes Guidance: - - When changing a CSS property that conflicts with other properties (e.g., replacing a gradient text with a solid color), replace the entire CSS rule for that selector instead of only adding the new property. For example, replace the full `.hero h1 { ... }` block, removing `background-clip` and `color: transparent` when setting `color: #fff`. - - Ensure search blocks match the current code exactly (spaces, indentation, and line breaks) so replacements apply correctly. - -Example Modifying Code: -``` -Some explanation... -{SEARCH_START} -

Old Title

-{DIVIDER} -

New Title

-{REPLACE_END} -{SEARCH_START} - -{DIVIDER} - - -{REPLACE_END} -``` - -Example Fixing Dependencies (requirements.txt): -``` -Adding missing dependency to fix ImportError... -=== requirements.txt === -{SEARCH_START} -gradio -streamlit -{DIVIDER} -gradio -streamlit -mistral-common -{REPLACE_END} -``` - -Example Deleting Code: -``` -Removing the paragraph... -{SEARCH_START} -

This paragraph will be deleted.

-{DIVIDER} -{REPLACE_END} -``` - -IMPORTANT: Always ensure "Built with anycoder" appears as clickable text in the header/top section linking to https://huggingface.co/spaces/akhaliq/anycoder - if it's missing from the existing code, add it; if it exists, preserve it. - -CRITICAL: For imported spaces that lack anycoder attribution, you MUST add it as part of your modifications. Add it to the header/navigation area as clickable text linking to https://huggingface.co/spaces/akhaliq/anycoder""" - -# Follow-up system prompt for modifying existing Gradio applications -GradioFollowUpSystemPrompt = """You are an expert Gradio developer modifying an existing Gradio application. -The user wants to apply changes based on their request. - -🚨 CRITICAL INSTRUCTION: You MUST maintain the original multi-file structure when making modifications. -❌ Do NOT use SEARCH/REPLACE blocks. -❌ Do NOT output everything in one combined block. -✅ Instead, output the complete modified files using the EXACT same multi-file format as the original generation. - -**MANDATORY Output Format for Modified Gradio Apps:** -You MUST use this exact format with file separators. DO NOT deviate from this format: - -=== app.py === -[complete modified app.py content] - -**CRITICAL FORMATTING RULES:** -- ALWAYS start each file with exactly "=== filename ===" (three equals signs before and after) -- NEVER combine files into one block -- NEVER use SEARCH/REPLACE blocks like <<<<<<< SEARCH -- ALWAYS include app.py if it needs changes -- Only include other files (utils.py, models.py, etc.) if they exist and need changes -- Each file section must be complete and standalone -- The format MUST match the original multi-file structure exactly - -**🚨 CRITICAL: DO NOT GENERATE requirements.txt or README.md** -- requirements.txt is automatically generated from your app.py imports -- README.md is automatically provided by the template -- Do NOT include requirements.txt or README.md in your output unless the user specifically asks to modify them -- The system will automatically extract imports from app.py and generate requirements.txt -- Generating a README.md will break the deployment process -- This prevents unnecessary changes to dependencies and documentation - -**IF User Specifically Asks to Modify requirements.txt:** -- Output ONLY plain text package names, one per line -- Do NOT use markdown formatting (no ```, no bold, no headings, no lists with * or -) -- Do NOT add explanatory text or descriptions -- Do NOT wrap in code blocks -- Just raw package names as they would appear in a real requirements.txt file -- Example of CORRECT format: - gradio - torch - transformers -- Example of INCORRECT format (DO NOT DO THIS): - ``` - gradio # For web interface - **Core dependencies:** - - torch - ``` - -**File Modification Guidelines:** -- Only output files that actually need changes -- If a file doesn't need modification, don't include it in the output -- Maintain the exact same file structure as the original -- Preserve all existing functionality unless specifically asked to change it -- Keep all imports, dependencies, and configurations intact unless modification is requested - -**Common Modification Scenarios:** -- Adding new features → Modify app.py and possibly utils.py -- Fixing bugs → Modify the relevant file (usually app.py) -- Adding dependencies → Modify requirements.txt -- UI improvements → Modify app.py -- Performance optimizations → Modify app.py and/or utils.py - -**ZeroGPU and Performance:** -- Maintain all existing @spaces.GPU decorators -- Keep AoT compilation if present -- Preserve all performance optimizations -- Add ZeroGPU decorators for new GPU-dependent functions - -**MCP Server Support:** -- If the user requests MCP functionality or tool calling capabilities: - 1. Add `mcp_server=True` to the `.launch()` method if not present - 2. Ensure `gradio[mcp]` is in requirements.txt (not just `gradio`) - 3. Add detailed docstrings with Args sections to all functions - 4. Add type hints to all function parameters -- Preserve existing MCP configurations if already present -- When adding new tools, follow MCP docstring format with Args and Returns sections - -IMPORTANT: Always ensure "Built with anycoder" appears as clickable text in the header/top section linking to https://huggingface.co/spaces/akhaliq/anycoder - if it's missing from the existing code, add it; if it exists, preserve it. - -CRITICAL: For imported spaces that lack anycoder attribution, you MUST add it as part of your modifications. Add it to the header/navigation area as clickable text linking to https://huggingface.co/spaces/akhaliq/anycoder""" - -# Follow-up system prompt for modifying existing transformers.js applications -TransformersJSFollowUpSystemPrompt = f"""You are an expert web developer modifying an existing transformers.js application. -The user wants to apply changes based on their request. -You MUST output ONLY the changes required using the following SEARCH/REPLACE block format. Do NOT output the entire file. -Explain the changes briefly *before* the blocks if necessary, but the code changes THEMSELVES MUST be within the blocks. - -IMPORTANT: When the user reports an ERROR MESSAGE, analyze it carefully to determine which file needs fixing: -- JavaScript errors/module loading issues → Fix index.js -- HTML rendering/DOM issues → Fix index.html -- Styling/visual issues → Fix style.css -- CDN/library loading errors → Fix script tags in index.html - -The transformers.js application consists of three files: index.html, index.js, and style.css. -When making changes, specify which file you're modifying by starting your search/replace blocks with the file name. - -**🚨 CRITICAL: DO NOT Generate README.md Files** -- NEVER generate README.md files under any circumstances -- A template README.md is automatically provided and will be overridden by the deployment system -- Generating a README.md will break the deployment process - -Format Rules: -1. Start with {SEARCH_START} -2. Provide the exact lines from the current code that need to be replaced. -3. Use {DIVIDER} to separate the search block from the replacement. -4. Provide the new lines that should replace the original lines. -5. End with {REPLACE_END} -6. You can use multiple SEARCH/REPLACE blocks if changes are needed in different parts of the file. -7. To insert code, use an empty SEARCH block (only {SEARCH_START} and {DIVIDER} on their lines) if inserting at the very beginning, otherwise provide the line *before* the insertion point in the SEARCH block and include that line plus the new lines in the REPLACE block. -8. To delete code, provide the lines to delete in the SEARCH block and leave the REPLACE block empty (only {DIVIDER} and {REPLACE_END} on their lines). -9. IMPORTANT: The SEARCH block must *exactly* match the current code, including indentation and whitespace. - -Example Modifying HTML: -``` -Changing the title in index.html... -=== index.html === -{SEARCH_START} - Old Title -{DIVIDER} - New Title -{REPLACE_END} -``` - -Example Modifying JavaScript: -``` -Adding a new function to index.js... -=== index.js === -{SEARCH_START} -// Existing code -{DIVIDER} -// Existing code - -function newFunction() {{ - console.log("New function added"); -}} -{REPLACE_END} -``` - -Example Modifying CSS: -``` -Changing background color in style.css... -=== style.css === -{SEARCH_START} -body {{ - background-color: white; -}} -{DIVIDER} -body {{ - background-color: #f0f0f0; -}} -{REPLACE_END} -``` -Example Fixing Library Loading Error: -``` -Fixing transformers.js CDN loading error... -=== index.html === -{SEARCH_START} - -{DIVIDER} - -{REPLACE_END} -``` - -IMPORTANT: Always ensure "Built with anycoder" appears as clickable text in the header/top section linking to https://huggingface.co/spaces/akhaliq/anycoder - if it's missing from the existing code, add it; if it exists, preserve it. - -CRITICAL: For imported spaces that lack anycoder attribution, you MUST add it as part of your modifications. Add it to the header/navigation area as clickable text linking to https://huggingface.co/spaces/akhaliq/anycoder""" - -# Available models -AVAILABLE_MODELS = [ - { - "name": "DeepSeek V3.2-Exp", - "id": "deepseek-ai/DeepSeek-V3.2-Exp", - "description": "DeepSeek V3.2 Experimental model for cutting-edge code generation and reasoning" - }, - { - "name": "DeepSeek R1", - "id": "deepseek-ai/DeepSeek-R1-0528", - "description": "DeepSeek R1 model for code generation" - }, - { - "name": "GLM-4.6", - "id": "zai-org/GLM-4.6", - "description": "GLM-4.6 model for advanced code generation and general tasks" - }, - { - "name": "Gemini Flash Latest", - "id": "gemini-flash-latest", - "description": "Google Gemini Flash Latest model via native Gemini API" - }, - { - "name": "Gemini Flash Lite Latest", - "id": "gemini-flash-lite-latest", - "description": "Google Gemini Flash Lite Latest model via OpenAI-compatible API" - }, - { - "name": "GPT-5", - "id": "gpt-5", - "description": "OpenAI GPT-5 model for advanced code generation and general tasks" - }, - { - "name": "Grok-4", - "id": "grok-4", - "description": "Grok-4 model via Poe (OpenAI-compatible) for advanced tasks" - }, - { - "name": "Grok-Code-Fast-1", - "id": "Grok-Code-Fast-1", - "description": "Grok-Code-Fast-1 model via Poe (OpenAI-compatible) for fast code generation" - }, - { - "name": "Claude-Opus-4.1", - "id": "claude-opus-4.1", - "description": "Anthropic Claude Opus 4.1 via Poe (OpenAI-compatible)" - }, - { - "name": "Claude-Sonnet-4.5", - "id": "claude-sonnet-4.5", - "description": "Anthropic Claude Sonnet 4.5 via Poe (OpenAI-compatible)" - }, - { - "name": "Claude-Haiku-4.5", - "id": "claude-haiku-4.5", - "description": "Anthropic Claude Haiku 4.5 via Poe (OpenAI-compatible)" - }, - { - "name": "Qwen3 Max Preview", - "id": "qwen3-max-preview", - "description": "Qwen3 Max Preview model via DashScope International API" - }, - { - "name": "MiniMax M2", - "id": "MiniMaxAI/MiniMax-M2", - "description": "MiniMax M2 model via HuggingFace InferenceClient with Novita provider" - }, - { - "name": "Kimi K2 Thinking", - "id": "moonshotai/Kimi-K2-Thinking", - "description": "Moonshot Kimi K2 Thinking model for advanced reasoning and code generation" - } -] -k2_model_name_tag = "moonshotai/Kimi-K2-Thinking" -# Default model selection -DEFAULT_MODEL_NAME = "Kimi K2 Thinking" -DEFAULT_MODEL = None -for _m in AVAILABLE_MODELS: - if _m.get("name") == DEFAULT_MODEL_NAME: - DEFAULT_MODEL = _m - break -if DEFAULT_MODEL is None and AVAILABLE_MODELS: - DEFAULT_MODEL = AVAILABLE_MODELS[0] - -# HF Inference Client -HF_TOKEN = os.getenv('HF_TOKEN') -if not HF_TOKEN: - raise RuntimeError("HF_TOKEN environment variable is not set. Please set it to your Hugging Face API token.") - -def get_inference_client(model_id, provider="auto"): - """Return an InferenceClient with provider based on model_id and user selection.""" - if model_id == "qwen3-30b-a3b-instruct-2507": - # Use DashScope OpenAI client - return OpenAI( - api_key=os.getenv("DASHSCOPE_API_KEY"), - base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", - ) - elif model_id == "qwen3-30b-a3b-thinking-2507": - # Use DashScope OpenAI client for Thinking model - return OpenAI( - api_key=os.getenv("DASHSCOPE_API_KEY"), - base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", - ) - elif model_id == "qwen3-coder-30b-a3b-instruct": - # Use DashScope OpenAI client for Coder model - return OpenAI( - api_key=os.getenv("DASHSCOPE_API_KEY"), - base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", - ) - elif model_id == "gpt-5": - # Use Poe (OpenAI-compatible) client for GPT-5 model - return OpenAI( - api_key=os.getenv("POE_API_KEY"), - base_url="https://api.poe.com/v1" - ) - elif model_id == "grok-4": - # Use Poe (OpenAI-compatible) client for Grok-4 model - return OpenAI( - api_key=os.getenv("POE_API_KEY"), - base_url="https://api.poe.com/v1" - ) - elif model_id == "Grok-Code-Fast-1": - # Use Poe (OpenAI-compatible) client for Grok-Code-Fast-1 model - return OpenAI( - api_key=os.getenv("POE_API_KEY"), - base_url="https://api.poe.com/v1" - ) - elif model_id == "claude-opus-4.1": - # Use Poe (OpenAI-compatible) client for Claude-Opus-4.1 - return OpenAI( - api_key=os.getenv("POE_API_KEY"), - base_url="https://api.poe.com/v1" - ) - elif model_id == "claude-sonnet-4.5": - # Use Poe (OpenAI-compatible) client for Claude-Sonnet-4.5 - return OpenAI( - api_key=os.getenv("POE_API_KEY"), - base_url="https://api.poe.com/v1" - ) - elif model_id == "claude-haiku-4.5": - # Use Poe (OpenAI-compatible) client for Claude-Haiku-4.5 - return OpenAI( - api_key=os.getenv("POE_API_KEY"), - base_url="https://api.poe.com/v1" - ) - elif model_id == "qwen3-max-preview": - # Use DashScope International OpenAI client for Qwen3 Max Preview - return OpenAI( - api_key=os.getenv("DASHSCOPE_API_KEY"), - base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", - ) - elif model_id == "openrouter/sonoma-dusk-alpha": - # Use OpenRouter client for Sonoma Dusk Alpha model - return OpenAI( - api_key=os.getenv("OPENROUTER_API_KEY"), - base_url="https://openrouter.ai/api/v1", - ) - elif model_id == "openrouter/sonoma-sky-alpha": - # Use OpenRouter client for Sonoma Sky Alpha model - return OpenAI( - api_key=os.getenv("OPENROUTER_API_KEY"), - base_url="https://openrouter.ai/api/v1", - ) - elif model_id == "MiniMaxAI/MiniMax-M2": - # Use HuggingFace InferenceClient with Novita provider for MiniMax M2 model - provider = "novita" - elif model_id == "step-3": - # Use StepFun API client for Step-3 model - return OpenAI( - api_key=os.getenv("STEP_API_KEY"), - base_url="https://api.stepfun.com/v1" - ) - elif model_id == "codestral-2508" or model_id == "mistral-medium-2508": - # Use Mistral client for Mistral models - return Mistral(api_key=os.getenv("MISTRAL_API_KEY")) - elif model_id == "gemini-2.5-flash": - # Use Google Gemini (OpenAI-compatible) client - return OpenAI( - api_key=os.getenv("GEMINI_API_KEY"), - base_url="https://generativelanguage.googleapis.com/v1beta/openai/", - ) - elif model_id == "gemini-2.5-pro": - # Use Google Gemini Pro (OpenAI-compatible) client - return OpenAI( - api_key=os.getenv("GEMINI_API_KEY"), - base_url="https://generativelanguage.googleapis.com/v1beta/openai/", - ) - elif model_id == "gemini-flash-latest": - # Use Google Gemini Flash Latest (OpenAI-compatible) client - return OpenAI( - api_key=os.getenv("GEMINI_API_KEY"), - base_url="https://generativelanguage.googleapis.com/v1beta/openai/", - ) - elif model_id == "gemini-flash-lite-latest": - # Use Google Gemini Flash Lite Latest (OpenAI-compatible) client - return OpenAI( - api_key=os.getenv("GEMINI_API_KEY"), - base_url="https://generativelanguage.googleapis.com/v1beta/openai/", - ) - elif model_id == "kimi-k2-turbo-preview": - # Use Moonshot AI (OpenAI-compatible) client for Kimi K2 Turbo (Preview) - return OpenAI( - api_key=os.getenv("MOONSHOT_API_KEY"), - base_url="https://api.moonshot.ai/v1", - ) - elif model_id == "moonshotai/Kimi-K2-Thinking": - # Use HuggingFace InferenceClient with Novita provider for Kimi K2 Thinking - provider = "novita" - elif model_id == "stealth-model-1": - # Use stealth model with generic configuration - api_key = os.getenv("STEALTH_MODEL_1_API_KEY") - if not api_key: - raise ValueError("STEALTH_MODEL_1_API_KEY environment variable is required for Carrot model") - - base_url = os.getenv("STEALTH_MODEL_1_BASE_URL") - if not base_url: - raise ValueError("STEALTH_MODEL_1_BASE_URL environment variable is required for Carrot model") - - return OpenAI( - api_key=api_key, - base_url=base_url, - ) - elif model_id == "moonshotai/Kimi-K2-Instruct": - provider = "groq" - elif model_id == "deepseek-ai/DeepSeek-V3.1": - provider = "novita" - elif model_id == "deepseek-ai/DeepSeek-V3.1-Terminus": - provider = "novita" - elif model_id == "deepseek-ai/DeepSeek-V3.2-Exp": - provider = "novita" - elif model_id == "zai-org/GLM-4.5": - provider = "fireworks-ai" - elif model_id == "zai-org/GLM-4.6": - provider = "zai-org" - return InferenceClient( - provider=provider, - api_key=HF_TOKEN, - bill_to="huggingface" - ) - -# Helper function to get real model ID for stealth models -def get_real_model_id(model_id: str) -> str: - """Get the real model ID, checking environment variables for stealth models""" - if model_id == "stealth-model-1": - # Get the real model ID from environment variable - real_model_id = os.getenv("STEALTH_MODEL_1_ID") - if not real_model_id: - raise ValueError("STEALTH_MODEL_1_ID environment variable is required for Carrot model") - - return real_model_id - return model_id - -# Type definitions -History = List[Tuple[str, str]] -Messages = List[Dict[str, str]] - -def history_to_messages(history: History, system: str) -> Messages: - messages = [{'role': 'system', 'content': system}] - for h in history: - # Handle multimodal content in history - user_content = h[0] - if isinstance(user_content, list): - # Extract text from multimodal content - text_content = "" - for item in user_content: - if isinstance(item, dict) and item.get("type") == "text": - text_content += item.get("text", "") - user_content = text_content if text_content else str(user_content) - - messages.append({'role': 'user', 'content': user_content}) - messages.append({'role': 'assistant', 'content': h[1]}) - return messages - -def history_to_chatbot_messages(history: History) -> List[Dict[str, str]]: - """Convert history tuples to chatbot message format""" - messages = [] - for user_msg, assistant_msg in history: - # Handle multimodal content - if isinstance(user_msg, list): - text_content = "" - for item in user_msg: - if isinstance(item, dict) and item.get("type") == "text": - text_content += item.get("text", "") - user_msg = text_content if text_content else str(user_msg) - - messages.append({"role": "user", "content": user_msg}) - messages.append({"role": "assistant", "content": assistant_msg}) - return messages - -def remove_code_block(text): - # Try to match code blocks with language markers - patterns = [ - r'```(?:html|HTML)\n([\s\S]+?)\n```', # Match ```html or ```HTML - r'```\n([\s\S]+?)\n```', # Match code blocks without language markers - r'```([\s\S]+?)```' # Match code blocks without line breaks - ] - for pattern in patterns: - match = re.search(pattern, text, re.DOTALL) - if match: - extracted = match.group(1).strip() - # Remove a leading language marker line (e.g., 'python') if present - if extracted.split('\n', 1)[0].strip().lower() in ['python', 'html', 'css', 'javascript', 'json', 'c', 'cpp', 'markdown', 'latex', 'jinja2', 'typescript', 'yaml', 'dockerfile', 'shell', 'r', 'sql', 'sql-mssql', 'sql-mysql', 'sql-mariadb', 'sql-sqlite', 'sql-cassandra', 'sql-plSQL', 'sql-hive', 'sql-pgsql', 'sql-gql', 'sql-gpsql', 'sql-sparksql', 'sql-esper']: - return extracted.split('\n', 1)[1] if '\n' in extracted else '' - # If HTML markup starts later in the block (e.g., Poe injected preface), trim to first HTML root - html_root_idx = None - for tag in [' 0: - return extracted[html_root_idx:].strip() - return extracted - # If no code block is found, check if the entire text is HTML - stripped = text.strip() - if stripped.startswith('') or stripped.startswith(' 0: - return stripped[idx:].strip() - return stripped - # Special handling for python: remove python marker - if text.strip().startswith('```python'): - return text.strip()[9:-3].strip() - # Remove a leading language marker line if present (fallback) - lines = text.strip().split('\n', 1) - if lines[0].strip().lower() in ['python', 'html', 'css', 'javascript', 'json', 'c', 'cpp', 'markdown', 'latex', 'jinja2', 'typescript', 'yaml', 'dockerfile', 'shell', 'r', 'sql', 'sql-mssql', 'sql-mysql', 'sql-mariadb', 'sql-sqlite', 'sql-cassandra', 'sql-plSQL', 'sql-hive', 'sql-pgsql', 'sql-gql', 'sql-gpsql', 'sql-sparksql', 'sql-esper']: - return lines[1] if len(lines) > 1 else '' - return text.strip() - -## React CDN compatibility fixer removed per user preference - -def strip_placeholder_thinking(text: str) -> str: - """Remove placeholder 'Thinking...' status lines from streamed text.""" - if not text: - return text - # Matches lines like: "Thinking..." or "Thinking... (12s elapsed)" - return re.sub(r"(?mi)^[\t ]*Thinking\.\.\.(?:\s*\(\d+s elapsed\))?[\t ]*$\n?", "", text) - -def is_placeholder_thinking_only(text: str) -> bool: - """Return True if text contains only 'Thinking...' placeholder lines (with optional elapsed).""" - if not text: - return False - stripped = text.strip() - if not stripped: - return False - return re.fullmatch(r"(?s)(?:\s*Thinking\.\.\.(?:\s*\(\d+s elapsed\))?\s*)+", stripped) is not None - -def extract_last_thinking_line(text: str) -> str: - """Extract the last 'Thinking...' line to display as status.""" - matches = list(re.finditer(r"Thinking\.\.\.(?:\s*\(\d+s elapsed\))?", text)) - return matches[-1].group(0) if matches else "Thinking..." - -def parse_transformers_js_output(text): - """Parse transformers.js output and extract the three files (index.html, index.js, style.css)""" - files = { - 'index.html': '', - 'index.js': '', - 'style.css': '' - } - - # Multiple patterns to match the three code blocks with different variations - html_patterns = [ - r'```html\s*\n([\s\S]*?)(?:```|\Z)', - r'```htm\s*\n([\s\S]*?)(?:```|\Z)', - r'```\s*(?:index\.html|html)\s*\n([\s\S]*?)(?:```|\Z)' - ] - - js_patterns = [ - r'```javascript\s*\n([\s\S]*?)(?:```|\Z)', - r'```js\s*\n([\s\S]*?)(?:```|\Z)', - r'```\s*(?:index\.js|javascript|js)\s*\n([\s\S]*?)(?:```|\Z)' - ] - - css_patterns = [ - r'```css\s*\n([\s\S]*?)(?:```|\Z)', - r'```\s*(?:style\.css|css)\s*\n([\s\S]*?)(?:```|\Z)' - ] - - # Extract HTML content - for pattern in html_patterns: - html_match = re.search(pattern, text, re.IGNORECASE) - if html_match: - files['index.html'] = html_match.group(1).strip() - break - - # Extract JavaScript content - for pattern in js_patterns: - js_match = re.search(pattern, text, re.IGNORECASE) - if js_match: - files['index.js'] = js_match.group(1).strip() - break - - # Extract CSS content - for pattern in css_patterns: - css_match = re.search(pattern, text, re.IGNORECASE) - if css_match: - files['style.css'] = css_match.group(1).strip() - break - - # Fallback: support === index.html === format if any file is missing - if not (files['index.html'] and files['index.js'] and files['style.css']): - # Use regex to extract sections - html_fallback = re.search(r'===\s*index\.html\s*===\s*\n([\s\S]+?)(?=\n===|$)', text, re.IGNORECASE) - js_fallback = re.search(r'===\s*index\.js\s*===\s*\n([\s\S]+?)(?=\n===|$)', text, re.IGNORECASE) - css_fallback = re.search(r'===\s*style\.css\s*===\s*\n([\s\S]+?)(?=\n===|$)', text, re.IGNORECASE) - - if html_fallback: - files['index.html'] = html_fallback.group(1).strip() - if js_fallback: - files['index.js'] = js_fallback.group(1).strip() - if css_fallback: - files['style.css'] = css_fallback.group(1).strip() - - # Additional fallback: extract from numbered sections or file headers - if not (files['index.html'] and files['index.js'] and files['style.css']): - # Try patterns like "1. index.html:" or "**index.html**" - patterns = [ - (r'(?:^\d+\.\s*|^##\s*|^\*\*\s*)index\.html(?:\s*:|\*\*:?)\s*\n([\s\S]+?)(?=\n(?:\d+\.|##|\*\*|===)|$)', 'index.html'), - (r'(?:^\d+\.\s*|^##\s*|^\*\*\s*)index\.js(?:\s*:|\*\*:?)\s*\n([\s\S]+?)(?=\n(?:\d+\.|##|\*\*|===)|$)', 'index.js'), - (r'(?:^\d+\.\s*|^##\s*|^\*\*\s*)style\.css(?:\s*:|\*\*:?)\s*\n([\s\S]+?)(?=\n(?:\d+\.|##|\*\*|===)|$)', 'style.css') - ] - - for pattern, file_key in patterns: - if not files[file_key]: - match = re.search(pattern, text, re.IGNORECASE | re.MULTILINE) - if match: - # Clean up the content by removing any code block markers - content = match.group(1).strip() - content = re.sub(r'^```\w*\s*\n', '', content) - content = re.sub(r'\n```\s*$', '', content) - files[file_key] = content.strip() - - return files - -def format_transformers_js_output(files): - """Format the three files into a single display string""" - output = [] - output.append("=== index.html ===") - output.append(files['index.html']) - output.append("\n=== index.js ===") - output.append(files['index.js']) - output.append("\n=== style.css ===") - output.append(files['style.css']) - return '\n'.join(output) - -def build_transformers_inline_html(files: dict) -> str: - """Merge transformers.js three-file output into a single self-contained HTML document. - - - Inlines style.css into a " if css else "" - if style_tag: - if '' in doc.lower(): - # Preserve original casing by finding closing head case-insensitively - match = _re.search(r"", doc, flags=_re.IGNORECASE) - if match: - idx = match.start() - doc = doc[:idx] + style_tag + doc[idx:] - else: - # No head; insert at top of body - match = _re.search(r"]*>", doc, flags=_re.IGNORECASE) - if match: - idx = match.end() - doc = doc[:idx] + "\n" + style_tag + doc[idx:] - else: - # Append at beginning - doc = style_tag + doc - - # Inline JS: insert before - script_tag = f"" if js else "" - # Lightweight debug console overlay to surface runtime errors inside the iframe - debug_overlay = ( - "\n" - "
\n" - "" - ) - # Cleanup script to clear Cache Storage and IndexedDB on unload to free model weights - cleanup_tag = ( - "" - ) - if script_tag: - match = _re.search(r"", doc, flags=_re.IGNORECASE) - if match: - idx = match.start() - doc = doc[:idx] + debug_overlay + script_tag + cleanup_tag + doc[idx:] - else: - # Append at end - doc = doc + debug_overlay + script_tag + cleanup_tag - - return doc - -def send_transformers_to_sandbox(files: dict) -> str: - """Build a self-contained HTML document from transformers.js files and return an iframe preview.""" - merged_html = build_transformers_inline_html(files) - return send_to_sandbox(merged_html) - -def parse_multipage_html_output(text: str) -> Dict[str, str]: - """Parse multi-page HTML output formatted as repeated "=== filename ===" sections. - - Returns a mapping of filename → file content. Supports nested paths like assets/css/styles.css. - """ - if not text: - return {} - # First, strip any markdown fences - cleaned = remove_code_block(text) - files: Dict[str, str] = {} - import re as _re - pattern = _re.compile(r"^===\s*([^=\n]+?)\s*===\s*\n([\s\S]*?)(?=\n===\s*[^=\n]+?\s*===|\Z)", _re.MULTILINE) - for m in pattern.finditer(cleaned): - name = m.group(1).strip() - content = m.group(2).strip() - # Remove accidental trailing fences if present - content = _re.sub(r"^```\w*\s*\n|\n```\s*$", "", content) - files[name] = content - return files - -def format_multipage_output(files: Dict[str, str]) -> str: - """Format a dict of files back into === filename === sections. - - Ensures `index.html` appears first if present; others follow sorted by path. - """ - if not isinstance(files, dict) or not files: - return "" - ordered_paths = [] - if 'index.html' in files: - ordered_paths.append('index.html') - for path in sorted(files.keys()): - if path == 'index.html': - continue - ordered_paths.append(path) - parts: list[str] = [] - for path in ordered_paths: - parts.append(f"=== {path} ===") - # Avoid trailing extra newlines to keep blocks compact - parts.append((files.get(path) or '').rstrip()) - return "\n".join(parts) - -def validate_and_autofix_files(files: Dict[str, str]) -> Dict[str, str]: - """Ensure minimal contract for multi-file sites; auto-fix missing pieces. - - Rules: - - Ensure at least one HTML entrypoint (index.html). If none, synthesize a simple index.html linking discovered pages. - - For each HTML file, ensure referenced local assets exist in files; if missing, add minimal stubs. - - Normalize relative paths (strip leading '/'). - """ - if not isinstance(files, dict) or not files: - return files or {} - import re as _re - - normalized: Dict[str, str] = {} - for k, v in files.items(): - safe_key = k.strip().lstrip('/') - normalized[safe_key] = v - - html_files = [p for p in normalized.keys() if p.lower().endswith('.html')] - has_index = 'index.html' in normalized - - # If no index.html but some HTML pages exist, create a simple hub index linking to them - if not has_index and html_files: - links = '\n'.join([f"
  • {p}
    • " for p in html_files]) - normalized['index.html'] = ( - "\n\n\n\n" - "\n" - "Site Index\n\n\n

    Site

    \n\n\n" - ) - - # Collect references from HTML files - asset_refs: set[str] = set() - link_href = _re.compile(r"]+href=\"([^\"]+)\"") - script_src = _re.compile(r"]+src=\"([^\"]+)\"") - img_src = _re.compile(r"]+src=\"([^\"]+)\"") - a_href = _re.compile(r"]+href=\"([^\"]+)\"") - - for path, content in list(normalized.items()): - if not path.lower().endswith('.html'): - continue - for patt in (link_href, script_src, img_src, a_href): - for m in patt.finditer(content or ""): - ref = (m.group(1) or "").strip() - if not ref or ref.startswith('http://') or ref.startswith('https://') or ref.startswith('data:') or '#' in ref: - continue - asset_refs.add(ref.lstrip('/')) - - # Add minimal stubs for missing local references (CSS/JS/pages only, not images) - for ref in list(asset_refs): - if ref not in normalized: - if ref.lower().endswith('.css'): - normalized[ref] = "/* generated stub */\n" - elif ref.lower().endswith('.js'): - normalized[ref] = "// generated stub\n" - elif ref.lower().endswith('.html'): - normalized[ref] = ( - "\n\nPage\n" - "

    Placeholder page

    This page was auto-created to satisfy an internal link.

    \n" - ) - # Note: We no longer create placeholder image files automatically - # This prevents unwanted SVG stub files from being generated during image generation - - return normalized -def inline_multipage_into_single_preview(files: Dict[str, str]) -> str: - """Inline local CSS/JS referenced by index.html for preview inside a data: iframe. - - - Uses index.html as the base document - - Inlines if the target exists in files - - Inlines " - return match.group(0) - doc = _re.sub(r"]+src=\"([^\"]+)\"[^>]*>\s*", _inline_js, doc, flags=_re.IGNORECASE) - - # Inject a lightweight in-iframe client-side navigator to load other HTML files - try: - import json as _json - import base64 as _b64 - import re as _re - html_pages = {k: v for k, v in files.items() if k.lower().endswith('.html')} - # Ensure index.html entry restores the current body's HTML - _m_body = _re.search(r"]*>([\s\S]*?)", doc, flags=_re.IGNORECASE) - _index_body = _m_body.group(1) if _m_body else doc - html_pages['index.html'] = _index_body - encoded = _b64.b64encode(_json.dumps(html_pages).encode('utf-8')).decode('ascii') - nav_script = ( - "" - ) - m = _re.search(r"", doc, flags=_re.IGNORECASE) - if m: - i = m.start() - doc = doc[:i] + nav_script + doc[i:] - else: - doc = doc + nav_script - except Exception: - # Non-fatal in preview - pass - - return doc - -def extract_html_document(text: str) -> str: - """Return substring starting from the first or if present, else original text. - - This ignores prose or planning notes before the actual HTML so previews don't break. - """ - if not text: - return text - lower = text.lower() - idx = lower.find(" str: - """Apply search/replace changes to content (HTML, Python, etc.)""" - if not changes_text.strip(): - return original_content - - # If the model didn't use the block markers, try a CSS-rule fallback where - # provided blocks like `.selector { ... }` replace matching CSS rules. - if (SEARCH_START not in changes_text) and (DIVIDER not in changes_text) and (REPLACE_END not in changes_text): - try: - import re # Local import to avoid global side effects - updated_content = original_content - replaced_any_rule = False - # Find CSS-like rule blocks in the changes_text - # This is a conservative matcher that looks for `selector { ... }` - css_blocks = re.findall(r"([^{]+)\{([\s\S]*?)\}", changes_text, flags=re.MULTILINE) - for selector_raw, body_raw in css_blocks: - selector = selector_raw.strip() - body = body_raw.strip() - if not selector: - continue - # Build a regex to find the existing rule for this selector - # Capture opening `{` and closing `}` to preserve them; replace inner body. - pattern = re.compile(rf"({re.escape(selector)}\s*\{{)([\s\S]*?)(\}})") - def _replace_rule(match): - nonlocal replaced_any_rule - replaced_any_rule = True - prefix, existing_body, suffix = match.groups() - # Preserve indentation of the existing first body line if present - first_line_indent = "" - for line in existing_body.splitlines(): - stripped = line.lstrip(" \t") - if stripped: - first_line_indent = line[: len(line) - len(stripped)] - break - # Re-indent provided body with the detected indent - if body: - new_body_lines = [first_line_indent + line if line.strip() else line for line in body.splitlines()] - new_body_text = "\n" + "\n".join(new_body_lines) + "\n" - else: - new_body_text = existing_body # If empty body provided, keep existing - return f"{prefix}{new_body_text}{suffix}" - updated_content, num_subs = pattern.subn(_replace_rule, updated_content, count=1) - if replaced_any_rule: - return updated_content - except Exception: - # Fallback silently to the standard block-based application - pass - - # Split the changes text into individual search/replace blocks - blocks = [] - current_block = "" - lines = changes_text.split('\n') - - for line in lines: - if line.strip() == SEARCH_START: - if current_block.strip(): - blocks.append(current_block.strip()) - current_block = line + '\n' - elif line.strip() == REPLACE_END: - current_block += line + '\n' - blocks.append(current_block.strip()) - current_block = "" - else: - current_block += line + '\n' - - if current_block.strip(): - blocks.append(current_block.strip()) - - modified_content = original_content - - for block in blocks: - if not block.strip(): - continue - - # Parse the search/replace block - lines = block.split('\n') - search_lines = [] - replace_lines = [] - in_search = False - in_replace = False - - for line in lines: - if line.strip() == SEARCH_START: - in_search = True - in_replace = False - elif line.strip() == DIVIDER: - in_search = False - in_replace = True - elif line.strip() == REPLACE_END: - in_replace = False - elif in_search: - search_lines.append(line) - elif in_replace: - replace_lines.append(line) - - # Apply the search/replace - if search_lines: - search_text = '\n'.join(search_lines).strip() - replace_text = '\n'.join(replace_lines).strip() - - if search_text in modified_content: - modified_content = modified_content.replace(search_text, replace_text) - else: - # If exact block match fails, attempt a CSS-rule fallback using the replace_text - try: - import re - updated_content = modified_content - replaced_any_rule = False - css_blocks = re.findall(r"([^{]+)\{([\s\S]*?)\}", replace_text, flags=re.MULTILINE) - for selector_raw, body_raw in css_blocks: - selector = selector_raw.strip() - body = body_raw.strip() - if not selector: - continue - pattern = re.compile(rf"({re.escape(selector)}\s*\{{)([\s\S]*?)(\}})") - def _replace_rule(match): - nonlocal replaced_any_rule - replaced_any_rule = True - prefix, existing_body, suffix = match.groups() - first_line_indent = "" - for line in existing_body.splitlines(): - stripped = line.lstrip(" \t") - if stripped: - first_line_indent = line[: len(line) - len(stripped)] - break - if body: - new_body_lines = [first_line_indent + line if line.strip() else line for line in body.splitlines()] - new_body_text = "\n" + "\n".join(new_body_lines) + "\n" - else: - new_body_text = existing_body - return f"{prefix}{new_body_text}{suffix}" - updated_content, num_subs = pattern.subn(_replace_rule, updated_content, count=1) - if replaced_any_rule: - modified_content = updated_content - else: - print(f"Warning: Search text not found in content: {search_text[:100]}...") - except Exception: - print(f"Warning: Search text not found in content: {search_text[:100]}...") - - return modified_content - -def apply_transformers_js_search_replace_changes(original_formatted_content: str, changes_text: str) -> str: - """Apply search/replace changes to transformers.js formatted content (three files)""" - if not changes_text.strip(): - return original_formatted_content - - # Parse the original formatted content to get the three files - files = parse_transformers_js_output(original_formatted_content) - - # Split the changes text into individual search/replace blocks - blocks = [] - current_block = "" - lines = changes_text.split('\n') - - for line in lines: - if line.strip() == SEARCH_START: - if current_block.strip(): - blocks.append(current_block.strip()) - current_block = line + '\n' - elif line.strip() == REPLACE_END: - current_block += line + '\n' - blocks.append(current_block.strip()) - current_block = "" - else: - current_block += line + '\n' - - if current_block.strip(): - blocks.append(current_block.strip()) - - # Process each block and apply changes to the appropriate file - for block in blocks: - if not block.strip(): - continue - - # Parse the search/replace block - lines = block.split('\n') - search_lines = [] - replace_lines = [] - in_search = False - in_replace = False - target_file = None - - for line in lines: - if line.strip() == SEARCH_START: - in_search = True - in_replace = False - elif line.strip() == DIVIDER: - in_search = False - in_replace = True - elif line.strip() == REPLACE_END: - in_replace = False - elif in_search: - search_lines.append(line) - elif in_replace: - replace_lines.append(line) - - # Determine which file this change targets based on the search content - if search_lines: - search_text = '\n'.join(search_lines).strip() - replace_text = '\n'.join(replace_lines).strip() - - # Check which file contains the search text - if search_text in files['index.html']: - target_file = 'index.html' - elif search_text in files['index.js']: - target_file = 'index.js' - elif search_text in files['style.css']: - target_file = 'style.css' - - # Apply the change to the target file - if target_file and search_text in files[target_file]: - files[target_file] = files[target_file].replace(search_text, replace_text) - else: - print(f"Warning: Search text not found in any transformers.js file: {search_text[:100]}...") - - # Reformat the modified files - return format_transformers_js_output(files) - -def send_to_sandbox(code): - """Render HTML in a sandboxed iframe. Assumes full HTML is provided by prompts.""" - html_doc = (code or "").strip() - # For preview only: inline local file URLs as data URIs so the - # data: iframe can load them. The original code (shown to the user) still contains file URLs. - try: - import re - import base64 as _b64 - import mimetypes as _mtypes - import urllib.parse as _uparse - def _file_url_to_data_uri(file_url: str) -> str | None: - try: - parsed = _uparse.urlparse(file_url) - path = _uparse.unquote(parsed.path) - if not path: - return None - with open(path, 'rb') as _f: - raw = _f.read() - mime = _mtypes.guess_type(path)[0] or 'application/octet-stream' - - b64 = _b64.b64encode(raw).decode() - return f"data:{mime};base64,{b64}" - except Exception as e: - print(f"[Sandbox] Failed to convert file URL to data URI: {str(e)}") - return None - def _repl_double(m): - url = m.group(1) - data_uri = _file_url_to_data_uri(url) - return f'src="{data_uri}"' if data_uri else m.group(0) - def _repl_single(m): - url = m.group(1) - data_uri = _file_url_to_data_uri(url) - return f"src='{data_uri}'" if data_uri else m.group(0) - html_doc = re.sub(r'src="(file:[^"]+)"', _repl_double, html_doc) - html_doc = re.sub(r"src='(file:[^']+)'", _repl_single, html_doc) - - except Exception: - # Best-effort; continue without inlining - pass - encoded_html = base64.b64encode(html_doc.encode('utf-8')).decode('utf-8') - data_uri = f"data:text/html;charset=utf-8;base64,{encoded_html}" - iframe = f'' - return iframe - -def is_streamlit_code(code: str) -> bool: - """Heuristic check to determine if Python code is a Streamlit app.""" - if not code: - return False - lowered = code.lower() - return ("import streamlit" in lowered) or ("from streamlit" in lowered) or ("st." in code and "streamlit" in lowered) - -def clean_requirements_txt_content(content: str) -> str: - """ - Clean up requirements.txt content to remove markdown formatting. - This function removes code blocks, markdown lists, headers, and other formatting - that might be mistakenly included by LLMs. - """ - if not content: - return content - - # First, remove code blocks if present - if '```' in content: - content = remove_code_block(content) - - # Process line by line to remove markdown formatting - lines = content.split('\n') - clean_lines = [] - - for line in lines: - stripped_line = line.strip() - - # Skip empty lines - if not stripped_line: - continue - - # Skip lines that are markdown formatting - if (stripped_line == '```' or - stripped_line.startswith('```') or - # Skip markdown headers (## Header) but keep comments (# comment) - (stripped_line.startswith('#') and len(stripped_line) > 1 and stripped_line[1] != ' ') or - stripped_line.startswith('**') or # Skip bold text - stripped_line.startswith('===') or # Skip section dividers - stripped_line.startswith('---') or # Skip horizontal rules - # Skip common explanatory text patterns - stripped_line.lower().startswith('here') or - stripped_line.lower().startswith('this') or - stripped_line.lower().startswith('the ') or - stripped_line.lower().startswith('based on') or - stripped_line.lower().startswith('dependencies') or - stripped_line.lower().startswith('requirements')): - continue - - # Handle markdown list items (- item or * item) - if (stripped_line.startswith('- ') or stripped_line.startswith('* ')): - # Extract the package name after the list marker - stripped_line = stripped_line[2:].strip() - if not stripped_line: - continue - - # Keep lines that look like valid package specifications - # Valid lines: package names, git+https://, comments starting with "# " - if (stripped_line.startswith('# ') or # Valid comments - stripped_line.startswith('git+') or # Git dependencies - stripped_line[0].isalnum() or # Package names start with alphanumeric - '==' in stripped_line or # Version specifications - '>=' in stripped_line or # Version specifications - '<=' in stripped_line or # Version specifications - '~=' in stripped_line): # Version specifications - clean_lines.append(stripped_line) - - result = '\n'.join(clean_lines) - - # Ensure it ends with a newline - if result and not result.endswith('\n'): - result += '\n' - - return result if result else "# No additional dependencies required\n" - -def parse_multi_file_python_output(code: str) -> dict: - """Parse multi-file Python output (Gradio/Streamlit) into separate files""" - files = {} - if not code: - return files - - # Look for file separators like === filename.py === - import re - file_pattern = r'=== ([^=]+) ===' - parts = re.split(file_pattern, code) - - if len(parts) > 1: - # Multi-file format detected - for i in range(1, len(parts), 2): - if i + 1 < len(parts): - filename = parts[i].strip() - content = parts[i + 1].strip() - - # Clean up requirements.txt to remove markdown formatting - if filename == 'requirements.txt': - content = clean_requirements_txt_content(content) - - files[filename] = content - else: - # Single file - check if it's a space import or regular code - if "IMPORTED PROJECT FROM HUGGING FACE SPACE" in code: - # This is already a multi-file import, try to parse it - lines = code.split('\n') - current_file = None - current_content = [] - - for line in lines: - if line.startswith('=== ') and line.endswith(' ==='): - # Save previous file - if current_file and current_content: - content = '\n'.join(current_content) - # Clean up requirements.txt to remove markdown formatting - if current_file == 'requirements.txt': - content = clean_requirements_txt_content(content) - files[current_file] = content - # Start new file - current_file = line[4:-4].strip() - current_content = [] - elif current_file: - current_content.append(line) - - # Save last file - if current_file and current_content: - content = '\n'.join(current_content) - # Clean up requirements.txt to remove markdown formatting - if current_file == 'requirements.txt': - content = clean_requirements_txt_content(content) - files[current_file] = content - else: - # Single file code - determine appropriate filename - if is_streamlit_code(code): - files['streamlit_app.py'] = code - elif 'import gradio' in code.lower() or 'from gradio' in code.lower(): - files['app.py'] = code - else: - files['app.py'] = code - - return files - -def format_multi_file_python_output(files: dict) -> str: - """Format multiple Python files into the standard multi-file format""" - if not files: - return "" - - if len(files) == 1: - # Single file - return as is - return list(files.values())[0] - - # Multi-file format - output = [] - - # Order files: main app first, then utils, models, config, requirements - file_order = ['app.py', 'streamlit_app.py', 'main.py', 'utils.py', 'models.py', 'config.py', 'requirements.txt'] - ordered_files = [] - - # Add files in preferred order - for preferred_file in file_order: - if preferred_file in files: - ordered_files.append(preferred_file) - - # Add remaining files - for filename in sorted(files.keys()): - if filename not in ordered_files: - ordered_files.append(filename) - - # Format output - for filename in ordered_files: - output.append(f"=== {filename} ===") - - # Clean up requirements.txt content if it's being formatted - content = files[filename] - if filename == 'requirements.txt': - content = clean_requirements_txt_content(content) - - output.append(content) - output.append("") # Empty line between files - - return '\n'.join(output) - -def send_streamlit_to_stlite(code: str) -> str: - """Render Streamlit code using stlite inside a sandboxed iframe for preview.""" - # Build an HTML document that loads stlite and mounts the Streamlit app defined inline - html_doc = ( - """ - - - - - - Streamlit Preview - - - - - - -""" - + (code or "") - + """ - - - -""" - ) - encoded_html = base64.b64encode(html_doc.encode('utf-8')).decode('utf-8') - data_uri = f"data:text/html;charset=utf-8;base64,{encoded_html}" - iframe = f'' - return iframe - -def is_gradio_code(code: str) -> bool: - """Heuristic check to determine if Python code is a Gradio app.""" - if not code: - return False - lowered = code.lower() - return ( - "import gradio" in lowered - or "from gradio" in lowered - or "gr.Interface(" in code - or "gr.Blocks(" in code - ) - -def send_gradio_to_lite(code: str) -> str: - """Render Gradio code using gradio-lite inside a sandboxed iframe for preview.""" - html_doc = ( - """ - - - - - - Gradio Preview - - - - - - -""" - + (code or "") - + """ - - - -""" - ) - encoded_html = base64.b64encode(html_doc.encode('utf-8')).decode('utf-8') - data_uri = f"data:text/html;charset=utf-8;base64,{encoded_html}" - iframe = f'' - return iframe - -stop_generation = False - - -def check_authentication(profile: gr.OAuthProfile | None = None, token: gr.OAuthToken | None = None) -> tuple[bool, str]: - """Check if user is authenticated and return status with message.""" - if not profile or not token: - return False, "Please log in with your Hugging Face account to use AnyCoder." - - if not token.token: - return False, "Authentication token is invalid. Please log in again." - - return True, f"Authenticated as {profile.username}" - - -def update_ui_for_auth_status(profile: gr.OAuthProfile | None = None, token: gr.OAuthToken | None = None): - """Update UI components based on authentication status.""" - is_authenticated, auth_message = check_authentication(profile, token) - - if is_authenticated: - # User is authenticated - enable all components - return { - # Enable main input and button - input: gr.update(interactive=True, placeholder="Describe your application..."), - btn: gr.update(interactive=True, variant="primary") - } - else: - # User not authenticated - disable main components - return { - # Disable main input and button with clear messaging - input: gr.update( - interactive=False, - placeholder="🔒 Click Sign in with Hugging Face button to use AnyCoder for free" - ), - btn: gr.update(interactive=False, variant="secondary") - } - - -def generation_code(query: str | None, vlm_image: Optional[gr.Image], _setting: Dict[str, str], _history: Optional[History], _current_model: Dict, language: str = "html", provider: str = "auto", profile: gr.OAuthProfile | None = None, token: gr.OAuthToken | None = None): - # Check authentication first - is_authenticated, auth_message = check_authentication(profile, token) - if not is_authenticated: - error_message = f"🔒 Authentication Required\n\n{auth_message}\n\nPlease click the 'Sign in with Hugging Face' button in the sidebar to continue." - yield { - code_output: error_message, - history_output: history_to_chatbot_messages(_history or []), - } - return - - if query is None: - query = '' - if _history is None: - _history = [] - # Ensure _history is always a list of lists with at least 2 elements per item - if not isinstance(_history, list): - _history = [] - _history = [h for h in _history if isinstance(h, list) and len(h) == 2] - - # Check if there's existing content in history to determine if this is a modification request - has_existing_content = False - last_assistant_msg = "" - if _history and len(_history[-1]) > 1: - last_assistant_msg = _history[-1][1] - # Check for various content types that indicate an existing project - if ('' in last_assistant_msg or - '>>>>>> REPLACE -2. The SEARCH block must match the existing code EXACTLY (including whitespace, indentation, line breaks) -3. The REPLACE block should contain the modified version -4. Only include the specific lines that need to change, with enough context to make them unique -5. Generate multiple search/replace blocks if needed for different changes -6. Do NOT include any explanations or comments outside the blocks - -Example format: -<<<<<<< SEARCH - function oldFunction() { - return "old"; - } -======= - function newFunction() { - return "new"; - } ->>>>>>> REPLACE""" - - user_prompt = f"""Existing code: -{last_assistant_msg} -Modification instructions: -{query} - -Generate the exact search/replace blocks needed to make these changes.""" - - messages = [ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": user_prompt} - ] - - # Generate search/replace instructions - if _current_model.get('type') == 'openai': - response = client.chat.completions.create( - model=get_real_model_id(_current_model['id']), - messages=messages, - max_tokens=4000, - temperature=0.1 - ) - changes_text = response.choices[0].message.content - elif _current_model.get('type') == 'mistral': - response = client.chat.complete( - model=get_real_model_id(_current_model['id']), - messages=messages, - max_tokens=4000, - temperature=0.1 - ) - changes_text = response.choices[0].message.content - else: # Hugging Face or other - completion = client.chat.completions.create( - model=get_real_model_id(_current_model['id']), - messages=messages, - max_tokens=4000, - temperature=0.1 - ) - changes_text = completion.choices[0].message.content - - # Apply the search/replace changes - if language == "transformers.js" and ('=== index.html ===' in last_assistant_msg): - modified_content = apply_transformers_js_search_replace_changes(last_assistant_msg, changes_text) - else: - modified_content = apply_search_replace_changes(last_assistant_msg, changes_text) - - # If changes were successfully applied, return the modified content - if modified_content != last_assistant_msg: - _history.append([query, modified_content]) - - # Generate deployment message instead of preview - deploy_message = f""" -
    -

    ✅ Code Updated Successfully!

    -

    Your {language.upper()} code has been modified and is ready for deployment.

    -

    👉 Use the Deploy button in the sidebar to publish your app!

    -
    - """ - - yield { - code_output: modified_content, - history: _history, - history_output: history_to_chatbot_messages(_history), - } - return - - except Exception as e: - print(f"Search/replace failed, falling back to normal generation: {e}") - # If search/replace fails, continue with normal generation - - # Create/lookup a session id for temp-file tracking and cleanup - if _setting is not None and isinstance(_setting, dict): - session_id = _setting.get("__session_id__") - if not session_id: - session_id = str(uuid.uuid4()) - _setting["__session_id__"] = session_id - else: - session_id = str(uuid.uuid4()) - - # Update Gradio system prompts if needed - if language == "gradio": - update_gradio_system_prompts() - - # Choose system prompt based on context - # Special case: If user is asking about model identity, use neutral prompt - if query and any(phrase in query.lower() for phrase in ["what model are you", "who are you", "identify yourself", "what ai are you", "which model"]): - system_prompt = "You are a helpful AI assistant. Please respond truthfully about your identity and capabilities." - elif has_existing_content: - # Use follow-up prompt for modifying existing content - if language == "transformers.js": - system_prompt = TransformersJSFollowUpSystemPrompt - elif language == "gradio": - system_prompt = GradioFollowUpSystemPrompt - elif language == "react": - system_prompt = REACT_FOLLOW_UP_SYSTEM_PROMPT - else: - system_prompt = FollowUpSystemPrompt - else: - # Use language-specific prompt - if language == "html": - # Dynamic file selection always enabled - system_prompt = DYNAMIC_MULTIPAGE_HTML_SYSTEM_PROMPT - elif language == "transformers.js": - system_prompt = TRANSFORMERS_JS_SYSTEM_PROMPT - elif language == "react": - system_prompt = REACT_SYSTEM_PROMPT - elif language == "gradio": - system_prompt = GRADIO_SYSTEM_PROMPT - elif language == "streamlit": - system_prompt = STREAMLIT_SYSTEM_PROMPT - elif language == "json": - system_prompt = JSON_SYSTEM_PROMPT - elif language == "comfyui": - system_prompt = get_comfyui_system_prompt() - else: - system_prompt = GENERIC_SYSTEM_PROMPT.format(language=language) - - messages = history_to_messages(_history, system_prompt) - - - - # Use the original query without search enhancement - enhanced_query = query - - # Check if this is GLM-4.5 model and handle with simple HuggingFace InferenceClient - if _current_model["id"] == "zai-org/GLM-4.5": - if vlm_image is not None: - messages.append(create_multimodal_message(enhanced_query, vlm_image)) - else: - messages.append({'role': 'user', 'content': enhanced_query}) - - try: - client = InferenceClient( - provider="auto", - api_key=os.environ["HF_TOKEN"], - bill_to="huggingface", - ) - - stream = client.chat.completions.create( - model="zai-org/GLM-4.5", - messages=messages, - stream=True, - max_tokens=16384, - ) - - content = "" - for chunk in stream: - if chunk.choices[0].delta.content: - content += chunk.choices[0].delta.content - clean_code = remove_code_block(content) - # Show generation progress message - progress_message = f""" -
    -

    ⚡ Generating Your {language.upper()} App...

    -

    Code is being generated in real-time!

    -
    -

    Get ready to deploy once generation completes!

    -
    -
    - """ - yield { - code_output: gr.update(value=clean_code, language=get_gradio_language(language)), - history_output: history_to_chatbot_messages(_history), - } - - except Exception as e: - content = f"Error with GLM-4.5: {str(e)}\n\nPlease make sure HF_TOKEN environment variable is set." - - clean_code = remove_code_block(content) - - # Use clean code as final content without media generation - final_content = clean_code - - _history.append([query, final_content]) - - if language == "transformers.js": - files = parse_transformers_js_output(clean_code) - if files['index.html'] and files['index.js'] and files['style.css']: - formatted_output = format_transformers_js_output(files) - yield { - code_output: formatted_output, - history: _history, - history_output: history_to_chatbot_messages(_history), - } - else: - yield { - code_output: clean_code, - history: _history, - history_output: history_to_chatbot_messages(_history), - } - else: - if has_existing_content and not (clean_code.strip().startswith("") or clean_code.strip().startswith(" 1 else "" - modified_content = apply_search_replace_changes(last_content, clean_code) - clean_content = remove_code_block(modified_content) - - # Use clean content without media generation - - yield { - code_output: clean_content, - history: _history, - history_output: history_to_chatbot_messages(_history), - } - else: - # Use clean code as final content without media generation - final_content = clean_code - - # Generate deployment message instead of preview - deploy_message = f""" -
    -

    🎉 Code Generated Successfully!

    -

    Your {language.upper()} application is ready to deploy!

    - -
    -

    🚀 Next Steps:

    -
    -

    - 1 - Use the Deploy button in the sidebar -

    -

    - 2 - Enter your app name below -

    -

    - 3 - Click "Publish" -

    -

    - 4 - Share your creation! 🌍 -

    -
    -
    - -

    - 💡 Your app will be live on Hugging Face Spaces in seconds! -

    -
    - """ - - yield { - code_output: final_content, - history: _history, - history_output: history_to_chatbot_messages(_history), - } - return - - # Handle GLM-4.5V (multimodal vision) - if _current_model["id"] == "zai-org/GLM-4.5V": - # Build structured messages with a strong system prompt to enforce full HTML output - structured = [ - {"role": "system", "content": GLM45V_HTML_SYSTEM_PROMPT} - ] - if vlm_image is not None: - user_msg = { - "role": "user", - "content": [ - {"type": "text", "text": enhanced_query}, - ], - } - try: - import io, base64 - from PIL import Image - import numpy as np - if isinstance(vlm_image, np.ndarray): - vlm_image = Image.fromarray(vlm_image) - buf = io.BytesIO() - vlm_image.save(buf, format="PNG") - b64 = base64.b64encode(buf.getvalue()).decode() - user_msg["content"].append({ - "type": "image_url", - "image_url": {"url": f"data:image/png;base64,{b64}"} - }) - structured.append(user_msg) - except Exception: - structured.append({"role": "user", "content": enhanced_query}) - else: - structured.append({"role": "user", "content": enhanced_query}) - - try: - client = InferenceClient( - provider="auto", - api_key=os.environ["HF_TOKEN"], - bill_to="huggingface", - ) - stream = client.chat.completions.create( - model="zai-org/GLM-4.5V", - messages=structured, - stream=True, - ) - content = "" - for chunk in stream: - if getattr(chunk, "choices", None) and chunk.choices and getattr(chunk.choices[0], "delta", None) and getattr(chunk.choices[0].delta, "content", None): - content += chunk.choices[0].delta.content - clean_code = remove_code_block(content) - # Ensure escaped newlines/tabs from model are rendered correctly - if "\\n" in clean_code: - clean_code = clean_code.replace("\\n", "\n") - if "\\t" in clean_code: - clean_code = clean_code.replace("\\t", "\t") - preview_val = None - if language == "html": - _mpc = parse_multipage_html_output(clean_code) - _mpc = validate_and_autofix_files(_mpc) - preview_val = send_to_sandbox(inline_multipage_into_single_preview(_mpc)) if _mpc.get('index.html') else send_to_sandbox(clean_code) - elif language == "python" and is_streamlit_code(clean_code): - preview_val = send_streamlit_to_stlite(clean_code) - yield { - code_output: gr.update(value=clean_code, language=get_gradio_language(language)), - history_output: history_to_chatbot_messages(_history), - } - except Exception as e: - content = f"Error with GLM-4.5V: {str(e)}\n\nPlease make sure HF_TOKEN environment variable is set." - - clean_code = remove_code_block(content) - if "\\n" in clean_code: - clean_code = clean_code.replace("\\n", "\n") - if "\\t" in clean_code: - clean_code = clean_code.replace("\\t", "\t") - _history.append([query, clean_code]) - preview_val = None - if language == "html": - _mpc2 = parse_multipage_html_output(clean_code) - _mpc2 = validate_and_autofix_files(_mpc2) - preview_val = send_to_sandbox(inline_multipage_into_single_preview(_mpc2)) if _mpc2.get('index.html') else send_to_sandbox(clean_code) - elif language == "python" and is_streamlit_code(clean_code): - preview_val = send_streamlit_to_stlite(clean_code) - yield { - code_output: clean_code, - history: _history, - history_output: history_to_chatbot_messages(_history), - } - return - - # Use dynamic client based on selected model (for non-GLM-4.5 models) - client = get_inference_client(_current_model["id"], provider) - - if vlm_image is not None: - messages.append(create_multimodal_message(enhanced_query, vlm_image)) - else: - messages.append({'role': 'user', 'content': enhanced_query}) - try: - # Handle Mistral API method difference - if _current_model["id"] in ("codestral-2508", "mistral-medium-2508"): - completion = client.chat.stream( - model=get_real_model_id(_current_model["id"]), - messages=messages, - max_tokens=16384 - ) - - else: - # Poe expects model id "GPT-5" and uses max_tokens - if _current_model["id"] == "gpt-5": - completion = client.chat.completions.create( - model="GPT-5", - messages=messages, - stream=True, - max_tokens=16384 - ) - elif _current_model["id"] == "grok-4": - completion = client.chat.completions.create( - model="Grok-4", - messages=messages, - stream=True, - max_tokens=16384 - ) - elif _current_model["id"] == "claude-opus-4.1": - completion = client.chat.completions.create( - model="Claude-Opus-4.1", - messages=messages, - stream=True, - max_tokens=16384 - ) - elif _current_model["id"] == "claude-sonnet-4.5": - completion = client.chat.completions.create( - model="Claude-Sonnet-4.5", - messages=messages, - stream=True, - max_tokens=16384 - ) - elif _current_model["id"] == "claude-haiku-4.5": - completion = client.chat.completions.create( - model="Claude-Haiku-4.5", - messages=messages, - stream=True, - max_tokens=16384 - ) - else: - completion = client.chat.completions.create( - model=get_real_model_id(_current_model["id"]), - messages=messages, - stream=True, - max_tokens=16384 - ) - content = "" - # For Poe/GPT-5, maintain a simple code-fence state machine to only accumulate code - poe_inside_code_block = False - poe_partial_buffer = "" - for chunk in completion: - # Handle different response formats for Mistral vs others - chunk_content = None - if _current_model["id"] in ("codestral-2508", "mistral-medium-2508"): - # Mistral format: chunk.data.choices[0].delta.content - if ( - hasattr(chunk, "data") and chunk.data and - hasattr(chunk.data, "choices") and chunk.data.choices and - hasattr(chunk.data.choices[0], "delta") and - hasattr(chunk.data.choices[0].delta, "content") and - chunk.data.choices[0].delta.content is not None - ): - chunk_content = chunk.data.choices[0].delta.content - else: - # OpenAI format: chunk.choices[0].delta.content - if ( - hasattr(chunk, "choices") and chunk.choices and - hasattr(chunk.choices[0], "delta") and - hasattr(chunk.choices[0].delta, "content") and - chunk.choices[0].delta.content is not None - ): - chunk_content = chunk.choices[0].delta.content - - if chunk_content: - # Ensure chunk_content is always a string to avoid regex errors - if not isinstance(chunk_content, str): - # Handle structured thinking chunks (like ThinkChunk objects from magistral) - chunk_str = str(chunk_content) if chunk_content is not None else "" - if '[ThinkChunk(' in chunk_str: - # This is a structured thinking chunk, skip it to avoid polluting output - continue - chunk_content = chunk_str - if _current_model["id"] == "gpt-5": - # If this chunk is only placeholder thinking, surface a status update without polluting content - if is_placeholder_thinking_only(chunk_content): - status_line = extract_last_thinking_line(chunk_content) - yield { - code_output: gr.update(value=(content or "") + "\n", language="html"), - history_output: history_to_chatbot_messages(_history), - } - continue - # Filter placeholders - incoming = strip_placeholder_thinking(chunk_content) - # Process code fences incrementally, only keep content inside fences - s = poe_partial_buffer + incoming - append_text = "" - i = 0 - # Find all triple backticks positions - for m in re.finditer(r"```", s): - if not poe_inside_code_block: - # Opening fence. Require a newline to confirm full opener so we can skip optional language line - nl = s.find("\n", m.end()) - if nl == -1: - # Incomplete opener; buffer from this fence and wait for more - poe_partial_buffer = s[m.start():] - s = None - break - # Enter code, skip past newline after optional language token - poe_inside_code_block = True - i = nl + 1 - else: - # Closing fence, append content inside and exit code - append_text += s[i:m.start()] - poe_inside_code_block = False - i = m.end() - if s is not None: - if poe_inside_code_block: - append_text += s[i:] - poe_partial_buffer = "" - else: - poe_partial_buffer = s[i:] - if append_text: - content += append_text - else: - # Append content, filtering out placeholder thinking lines - content += strip_placeholder_thinking(chunk_content) - search_status = "" - - # Handle transformers.js output differently - if language == "transformers.js": - files = parse_transformers_js_output(content) - - # Stream ALL code by merging current parts into a single HTML (inline CSS & JS) - has_any_part = any([files.get('index.html'), files.get('index.js'), files.get('style.css')]) - if has_any_part: - merged_html = build_transformers_inline_html(files) - preview_val = None - if files['index.html'] and files['index.js'] and files['style.css']: - preview_val = send_transformers_to_sandbox(files) - yield { - code_output: gr.update(value=merged_html, language="html"), - history_output: history_to_chatbot_messages(_history), - } - elif has_existing_content: - # Model is returning search/replace changes for transformers.js - apply them - last_content = _history[-1][1] if _history and len(_history[-1]) > 1 else "" - modified_content = apply_transformers_js_search_replace_changes(last_content, content) - _mf = parse_transformers_js_output(modified_content) - yield { - code_output: gr.update(value=modified_content, language="html"), - history_output: history_to_chatbot_messages(_history), - } - else: - # Still streaming, show partial content - yield { - code_output: gr.update(value=content, language="html"), - history_output: history_to_chatbot_messages(_history), - } - else: - clean_code = remove_code_block(content) - if has_existing_content: - # Handle modification of existing content - if clean_code.strip().startswith("") or clean_code.strip().startswith(" 1 else "" - modified_content = apply_search_replace_changes(last_content, clean_code) - clean_content = remove_code_block(modified_content) - preview_val = None - if language == "html": - _mpc4 = parse_multipage_html_output(clean_content) - _mpc4 = validate_and_autofix_files(_mpc4) - preview_val = send_to_sandbox(inline_multipage_into_single_preview(_mpc4)) if _mpc4.get('index.html') else send_to_sandbox(clean_content) - elif language == "python" and is_streamlit_code(clean_content): - preview_val = send_streamlit_to_stlite(clean_content) - elif language == "gradio" or (language == "python" and is_gradio_code(clean_content)): - preview_val = send_gradio_to_lite(clean_content) - yield { - code_output: gr.update(value=clean_content, language=get_gradio_language(language)), - history_output: history_to_chatbot_messages(_history), - } - else: - preview_val = None - if language == "html": - _mpc5 = parse_multipage_html_output(clean_code) - _mpc5 = validate_and_autofix_files(_mpc5) - preview_val = send_to_sandbox(inline_multipage_into_single_preview(_mpc5)) if _mpc5.get('index.html') else send_to_sandbox(clean_code) - elif language == "python" and is_streamlit_code(clean_code): - preview_val = send_streamlit_to_stlite(clean_code) - elif language == "gradio" or (language == "python" and is_gradio_code(clean_code)): - preview_val = send_gradio_to_lite(clean_code) - yield { - code_output: gr.update(value=clean_code, language=get_gradio_language(language)), - history_output: history_to_chatbot_messages(_history), - } - # Skip chunks with empty choices (end of stream) - # Do not treat as error - # Handle response based on whether this is a modification or new generation - if language == "transformers.js": - # Handle transformers.js output - files = parse_transformers_js_output(content) - if files['index.html'] and files['index.js'] and files['style.css']: - # Model returned complete transformers.js output - formatted_output = format_transformers_js_output(files) - _history.append([query, formatted_output]) - yield { - code_output: formatted_output, - history: _history, - history_output: history_to_chatbot_messages(_history), - } - elif has_existing_content: - # Model returned search/replace changes for transformers.js - apply them - last_content = _history[-1][1] if _history and len(_history[-1]) > 1 else "" - modified_content = apply_transformers_js_search_replace_changes(last_content, content) - _history.append([query, modified_content]) - _mf = parse_transformers_js_output(modified_content) - yield { - code_output: modified_content, - history: _history, - history_output: history_to_chatbot_messages(_history), - } - else: - # Fallback if parsing failed - _history.append([query, content]) - yield { - code_output: content, - history: _history, - history_output: history_to_chatbot_messages(_history), - } - elif language == "gradio": - # Handle Gradio output - check if it's multi-file format or single file - if ('=== app.py ===' in content or '=== requirements.txt ===' in content): - # Model returned multi-file Gradio output - ensure requirements.txt is present - files = parse_multi_file_python_output(content) - if files and 'app.py' in files: - # Check if requirements.txt is missing and auto-generate it - if 'requirements.txt' not in files: - import_statements = extract_import_statements(files['app.py']) - requirements_content = generate_requirements_txt_with_llm(import_statements) - files['requirements.txt'] = requirements_content - - # Reformat with the auto-generated requirements.txt - content = format_multi_file_python_output(files) - - _history.append([query, content]) - yield { - code_output: content, - history: _history, - history_output: history_to_chatbot_messages(_history), - } - elif has_existing_content: - # Check if this is a followup that should maintain multi-file structure - last_content = _history[-1][1] if _history and len(_history[-1]) > 1 else "" - - # If the original was multi-file but the response isn't, try to convert it - if ('=== app.py ===' in last_content or '=== requirements.txt ===' in last_content): - # Original was multi-file, but response is single block - need to convert - if not ('=== app.py ===' in content or '=== requirements.txt ===' in content): - # Try to parse as single-block Gradio code and convert to multi-file format - clean_content = remove_code_block(content) - if 'import gradio' in clean_content or 'from gradio' in clean_content: - # This looks like Gradio code, convert to multi-file format - files = parse_multi_file_python_output(clean_content) - if not files: - # Single file - create multi-file structure - files = {'app.py': clean_content} - - # Extract requirements from imports - import_statements = extract_import_statements(clean_content) - requirements_content = generate_requirements_txt_with_llm(import_statements) - files['requirements.txt'] = requirements_content - - # Format as multi-file output - formatted_content = format_multi_file_python_output(files) - _history.append([query, formatted_content]) - yield { - code_output: formatted_content, - history: _history, - history_output: history_to_chatbot_messages(_history), - } - else: - # Not Gradio code, apply search/replace - modified_content = apply_search_replace_changes(last_content, content) - _history.append([query, modified_content]) - yield { - code_output: modified_content, - history: _history, - history_output: history_to_chatbot_messages(_history), - } - else: - # Response is already multi-file format - _history.append([query, content]) - yield { - code_output: content, - history: _history, - history_output: history_to_chatbot_messages(_history), - } - else: - # Original was single file, apply search/replace - modified_content = apply_search_replace_changes(last_content, content) - _history.append([query, modified_content]) - yield { - code_output: modified_content, - history: _history, - history_output: history_to_chatbot_messages(_history), - } - else: - # Fallback - treat as single file Gradio app - _history.append([query, content]) - yield { - code_output: content, - history: _history, - history_output: history_to_chatbot_messages(_history), - } - elif has_existing_content: - # Handle modification of existing content - final_code = remove_code_block(content) - if final_code.strip().startswith("") or final_code.strip().startswith(" 1 else "" - modified_content = apply_search_replace_changes(last_content, final_code) - clean_content = remove_code_block(modified_content) - - # Use clean content without media generation - - # Update history with the cleaned content - _history.append([query, clean_content]) - yield { - code_output: clean_content, - history: _history, - history_output: history_to_chatbot_messages(_history), - } - else: - # Regular generation - use the content as is - final_content = remove_code_block(content) - - # Use final content without media generation - - _history.append([query, final_content]) - - # Generate deployment message instead of preview - deploy_message = f""" -
    -

    🎉 Code Generated Successfully!

    -

    Your {language.upper()} application is ready to deploy!

    - -
    -

    🚀 Next Steps:

    -
    -

    - 1 - Use the Deploy button in the sidebar -

    -

    - 2 - Enter your app name below -

    -

    - 3 - Click "Publish" -

    -

    - 4 - Share your creation! 🌍 -

    -
    -
    - -

    - 💡 Your app will be live on Hugging Face Spaces in seconds! -

    -
    - """ - - yield { - code_output: final_content, - history: _history, - history_output: history_to_chatbot_messages(_history), - } - except Exception as e: - error_message = f"Error: {str(e)}" - yield { - code_output: error_message, - history_output: history_to_chatbot_messages(_history), - } - -# Deploy to Spaces logic - -def add_anycoder_tag_to_readme(api, repo_id, app_port=None): - """Download existing README, add anycoder tag and app_port if needed, and upload back. - - Args: - api: HuggingFace API client - repo_id: Repository ID - app_port: Optional port number to set for Docker spaces (e.g., 7860 for React apps) - """ - try: - import tempfile - import re - - # Download the existing README - readme_path = api.hf_hub_download( - repo_id=repo_id, - filename="README.md", - repo_type="space" - ) - - # Read the existing README content - with open(readme_path, 'r', encoding='utf-8') as f: - content = f.read() - - # Parse frontmatter and content - if content.startswith('---'): - # Split frontmatter and body - parts = content.split('---', 2) - if len(parts) >= 3: - frontmatter = parts[1].strip() - body = parts[2] if len(parts) > 2 else "" - - # Check if tags already exist - if 'tags:' in frontmatter: - # Add anycoder to existing tags if not present - if '- anycoder' not in frontmatter: - frontmatter = re.sub(r'(tags:\s*\n(?:\s*-\s*[^\n]+\n)*)', r'\1- anycoder\n', frontmatter) - else: - # Add tags section with anycoder - frontmatter += '\ntags:\n- anycoder' - - # Add app_port if specified and not already present - if app_port is not None and 'app_port:' not in frontmatter: - frontmatter += f'\napp_port: {app_port}' - - # Reconstruct the README - new_content = f"---\n{frontmatter}\n---{body}" - else: - # Malformed frontmatter, just add tags at the end of frontmatter - new_content = content.replace('---', '---\ntags:\n- anycoder\n---', 1) - else: - # No frontmatter, add it at the beginning - app_port_line = f'\napp_port: {app_port}' if app_port else '' - new_content = f"---\ntags:\n- anycoder{app_port_line}\n---\n\n{content}" - - # Upload the modified README - with tempfile.NamedTemporaryFile("w", suffix=".md", delete=False, encoding='utf-8') as f: - f.write(new_content) - temp_path = f.name - - api.upload_file( - path_or_fileobj=temp_path, - path_in_repo="README.md", - repo_id=repo_id, - repo_type="space" - ) - - import os - os.unlink(temp_path) - - except Exception as e: - print(f"Warning: Could not modify README.md to add anycoder tag: {e}") - -def extract_import_statements(code): - """Extract import statements from generated code.""" - import ast - import re - - import_statements = [] - - # Built-in Python modules to exclude - builtin_modules = { - 'os', 'sys', 'json', 'time', 'datetime', 'random', 'math', 're', 'collections', - 'itertools', 'functools', 'pathlib', 'urllib', 'http', 'email', 'html', 'xml', - 'csv', 'tempfile', 'shutil', 'subprocess', 'threading', 'multiprocessing', - 'asyncio', 'logging', 'typing', 'base64', 'hashlib', 'secrets', 'uuid', - 'copy', 'pickle', 'io', 'contextlib', 'warnings', 'sqlite3', 'gzip', 'zipfile', - 'tarfile', 'socket', 'ssl', 'platform', 'getpass', 'pwd', 'grp', 'stat', - 'glob', 'fnmatch', 'linecache', 'traceback', 'inspect', 'keyword', 'token', - 'tokenize', 'ast', 'code', 'codeop', 'dis', 'py_compile', 'compileall', - 'importlib', 'pkgutil', 'modulefinder', 'runpy', 'site', 'sysconfig' - } - - try: - # Try to parse as Python AST - tree = ast.parse(code) - - for node in ast.walk(tree): - if isinstance(node, ast.Import): - for alias in node.names: - module_name = alias.name.split('.')[0] - if module_name not in builtin_modules and not module_name.startswith('_'): - import_statements.append(f"import {alias.name}") - - elif isinstance(node, ast.ImportFrom): - if node.module: - module_name = node.module.split('.')[0] - if module_name not in builtin_modules and not module_name.startswith('_'): - names = [alias.name for alias in node.names] - import_statements.append(f"from {node.module} import {', '.join(names)}") - - except SyntaxError: - # Fallback: use regex to find import statements - for line in code.split('\n'): - line = line.strip() - if line.startswith('import ') or line.startswith('from '): - # Check if it's not a builtin module - if line.startswith('import '): - module_name = line.split()[1].split('.')[0] - elif line.startswith('from '): - module_name = line.split()[1].split('.')[0] - - if module_name not in builtin_modules and not module_name.startswith('_'): - import_statements.append(line) - - return list(set(import_statements)) # Remove duplicates - -def generate_requirements_txt_with_llm(import_statements): - """Generate requirements.txt content using LLM based on import statements.""" - if not import_statements: - return "# No additional dependencies required\n" - - # Use a lightweight model for this task - try: - client = get_inference_client("zai-org/GLM-4.6", "auto") - - imports_text = '\n'.join(import_statements) - - prompt = f"""Based on the following Python import statements, generate a comprehensive requirements.txt file with all necessary and commonly used related packages: - -{imports_text} - -Instructions: -- Include the direct packages needed for the imports -- Include commonly used companion packages and dependencies for better functionality -- Use correct PyPI package names (e.g., PIL -> Pillow, sklearn -> scikit-learn) -- IMPORTANT: For diffusers, ALWAYS use: git+https://github.com/huggingface/diffusers -- IMPORTANT: For transformers, ALWAYS use: git+https://github.com/huggingface/transformers -- IMPORTANT: If diffusers is installed, also include transformers and sentencepiece as they usually go together -- Examples of comprehensive dependencies: - * diffusers often needs: git+https://github.com/huggingface/transformers, sentencepiece, accelerate, torch, tokenizers - * transformers often needs: accelerate, torch, tokenizers, datasets - * gradio often needs: requests, Pillow for image handling - * pandas often needs: numpy, openpyxl for Excel files - * matplotlib often needs: numpy, pillow for image saving - * sklearn often needs: numpy, scipy, joblib - * streamlit often needs: pandas, numpy, requests - * opencv-python often needs: numpy, pillow - * fastapi often needs: uvicorn, pydantic - * torch often needs: torchvision, torchaudio (if doing computer vision/audio) -- Include packages for common file formats if relevant (openpyxl, python-docx, PyPDF2) -- Do not include Python built-in modules -- Do not specify versions unless there are known compatibility issues -- One package per line -- If no external packages are needed, return "# No additional dependencies required" - -🚨 CRITICAL OUTPUT FORMAT: -- Output ONLY the package names, one per line (plain text format) -- Do NOT use markdown formatting (no ```, no bold, no headings, no lists) -- Do NOT add any explanatory text before or after the package list -- Do NOT wrap the output in code blocks -- Just output raw package names as they would appear in requirements.txt - -Generate a comprehensive requirements.txt that ensures the application will work smoothly:""" - - messages = [ - {"role": "system", "content": "You are a Python packaging expert specializing in creating comprehensive, production-ready requirements.txt files. Output ONLY plain text package names without any markdown formatting, code blocks, or explanatory text. Your goal is to ensure applications work smoothly by including not just direct dependencies but also commonly needed companion packages, popular extensions, and supporting libraries that developers typically need together."}, - {"role": "user", "content": prompt} - ] - - response = client.chat.completions.create( - model="zai-org/GLM-4.6", - messages=messages, - max_tokens=1024, - temperature=0.1 - ) - - requirements_content = response.choices[0].message.content.strip() - - # Clean up the response in case it includes extra formatting - if '```' in requirements_content: - # Use the existing remove_code_block function for consistent cleaning - requirements_content = remove_code_block(requirements_content) - - # Enhanced cleanup for markdown and formatting - lines = requirements_content.split('\n') - clean_lines = [] - for line in lines: - stripped_line = line.strip() - - # Skip lines that are markdown formatting - if (stripped_line == '```' or - stripped_line.startswith('```') or - stripped_line.startswith('#') and not stripped_line.startswith('# ') or # Skip markdown headers but keep comments - stripped_line.startswith('**') or # Skip bold text - stripped_line.startswith('*') and not stripped_line[1:2].isalnum() or # Skip markdown lists but keep package names starting with * - stripped_line.startswith('-') and not stripped_line[1:2].isalnum() or # Skip markdown lists but keep package names starting with - - stripped_line.startswith('===') or # Skip section dividers - stripped_line.startswith('---') or # Skip horizontal rules - stripped_line.lower().startswith('here') or # Skip explanatory text - stripped_line.lower().startswith('this') or # Skip explanatory text - stripped_line.lower().startswith('the') or # Skip explanatory text - stripped_line.lower().startswith('based on') or # Skip explanatory text - stripped_line == ''): # Skip empty lines unless they're at natural boundaries - continue - - # Keep lines that look like valid package specifications - # Valid lines: package names, git+https://, comments starting with "# " - if (stripped_line.startswith('# ') or # Valid comments - stripped_line.startswith('git+') or # Git dependencies - stripped_line[0].isalnum() or # Package names start with alphanumeric - '==' in stripped_line or # Version specifications - '>=' in stripped_line or # Version specifications - '<=' in stripped_line): # Version specifications - clean_lines.append(line) - - requirements_content = '\n'.join(clean_lines).strip() - - # Ensure it ends with a newline - if requirements_content and not requirements_content.endswith('\n'): - requirements_content += '\n' - - return requirements_content if requirements_content else "# No additional dependencies required\n" - - except Exception as e: - # Fallback: simple extraction with basic mapping - dependencies = set() - special_cases = { - 'PIL': 'Pillow', - 'sklearn': 'scikit-learn', - 'skimage': 'scikit-image', - 'bs4': 'beautifulsoup4' - } - - for stmt in import_statements: - if stmt.startswith('import '): - module_name = stmt.split()[1].split('.')[0] - package_name = special_cases.get(module_name, module_name) - dependencies.add(package_name) - elif stmt.startswith('from '): - module_name = stmt.split()[1].split('.')[0] - package_name = special_cases.get(module_name, module_name) - dependencies.add(package_name) - - if dependencies: - return '\n'.join(sorted(dependencies)) + '\n' - else: - return "# No additional dependencies required\n" - -def wrap_html_in_gradio_app(html_code): - # Escape triple quotes for safe embedding - safe_html = html_code.replace('"""', r'\"\"\"') - - # Extract import statements and generate requirements.txt with LLM - import_statements = extract_import_statements(html_code) - requirements_comment = "" - if import_statements: - requirements_content = generate_requirements_txt_with_llm(import_statements) - requirements_comment = ( - "# Generated requirements.txt content (create this file manually if needed):\n" - + '\n'.join(f"# {line}" for line in requirements_content.strip().split('\n')) + '\n\n' - ) - - return ( - f'{requirements_comment}' - 'import gradio as gr\n\n' - 'def show_html():\n' - f' return """{safe_html}"""\n\n' - 'demo = gr.Interface(fn=show_html, inputs=None, outputs=gr.HTML())\n\n' - 'if __name__ == "__main__":\n' - ' demo.launch()\n' - ) -def deploy_to_spaces(code): - if not code or not code.strip(): - return # Do nothing if code is empty - # Wrap the HTML code in a Gradio app - app_py = wrap_html_in_gradio_app(code.strip()) - base_url = "https://huggingface.co/new-space" - params = urllib.parse.urlencode({ - "name": "new-space", - "sdk": "gradio" - }) - # Use urlencode for file params - files_params = urllib.parse.urlencode({ - "files[0][path]": "app.py", - "files[0][content]": app_py - }) - full_url = f"{base_url}?{params}&{files_params}" - webbrowser.open_new_tab(full_url) - -def wrap_html_in_static_app(html_code): - # For static Spaces, just use the HTML code as-is - return html_code - -def prettify_comfyui_json_for_html(json_content: str) -> str: - """Convert ComfyUI JSON to prettified HTML display""" - try: - import json - # Parse and prettify the JSON - parsed_json = json.loads(json_content) - prettified_json = json.dumps(parsed_json, indent=2, ensure_ascii=False) - - # Create HTML wrapper with syntax highlighting - html_content = f""" - - - - - ComfyUI Workflow - - - -
    -

    ComfyUI Workflow

    -

    Built with anycoder

    -
    - - - - -
    - 
    {prettified_json}
    -
    - - - -""" - return html_content - except json.JSONDecodeError: - # If it's not valid JSON, return as-is - return json_content - except Exception as e: - print(f"Error prettifying ComfyUI JSON: {e}") - return json_content - -def check_hf_space_url(url: str) -> Tuple[bool, str | None, str | None]: - """Check if URL is a valid Hugging Face Spaces URL and extract username/project""" - import re - - # Pattern to match HF Spaces URLs (allows dots in space names) - url_pattern = re.compile( - r'^(https?://)?(huggingface\.co|hf\.co)/spaces/([\w.-]+)/([\w.-]+)$', - re.IGNORECASE - ) - - match = url_pattern.match(url.strip()) - if match: - username = match.group(3) - project_name = match.group(4) - return True, username, project_name - return False, None, None - -def detect_transformers_js_space(api, username: str, project_name: str) -> bool: - """Check if a space is a transformers.js app by looking for the three key files""" - try: - from huggingface_hub import list_repo_files - files = list_repo_files(repo_id=f"{username}/{project_name}", repo_type="space") - - # Check for the three transformers.js files - has_index_html = any('index.html' in f for f in files) - has_index_js = any('index.js' in f for f in files) - has_style_css = any('style.css' in f for f in files) - - return has_index_html and has_index_js and has_style_css - except: - return False - -def fetch_transformers_js_files(api, username: str, project_name: str) -> dict: - """Fetch all three transformers.js files from a space""" - files = {} - file_names = ['index.html', 'index.js', 'style.css'] - - for file_name in file_names: - try: - content_path = api.hf_hub_download( - repo_id=f"{username}/{project_name}", - filename=file_name, - repo_type="space" - ) - - with open(content_path, 'r', encoding='utf-8') as f: - files[file_name] = f.read() - except: - files[file_name] = "" - - return files - -def combine_transformers_js_files(files: dict, username: str, project_name: str) -> str: - """Combine transformers.js files into the expected format for the LLM""" - combined = f"""IMPORTED PROJECT FROM HUGGING FACE SPACE -============================================== - -Space: {username}/{project_name} -SDK: static (transformers.js) -Type: Transformers.js Application - -""" - - if files.get('index.html'): - combined += f"=== index.html ===\n{files['index.html']}\n\n" - - if files.get('index.js'): - combined += f"=== index.js ===\n{files['index.js']}\n\n" - - if files.get('style.css'): - combined += f"=== style.css ===\n{files['style.css']}\n\n" - - return combined - -def fetch_all_space_files(api, username: str, project_name: str, sdk: str) -> dict: - """Fetch all relevant files from a Hugging Face Space""" - files = {} - - try: - from huggingface_hub import list_repo_files - all_files = list_repo_files(repo_id=f"{username}/{project_name}", repo_type="space") - - # Filter out unwanted files - relevant_files = [] - for file in all_files: - # Skip hidden files, git files, and certain extensions - if (file.startswith('.') or - file.endswith('.md') or - (file.endswith('.txt') and file not in ['requirements.txt', 'packages.txt']) or - file.endswith('.log') or - file.endswith('.pyc') or - '__pycache__' in file): - continue - relevant_files.append(file) - - # Define priority files based on SDK - priority_files = [] - if sdk == "gradio": - priority_files = ["app.py", "main.py", "gradio_app.py", "requirements.txt", "packages.txt"] - elif sdk == "streamlit": - priority_files = ["streamlit_app.py", "app.py", "main.py", "requirements.txt", "packages.txt"] - elif sdk == "static": - priority_files = ["index.html", "index.js", "style.css", "script.js"] - - # Add priority files first, then other Python files, then other files - files_to_fetch = [] - - # Add priority files that exist - for pfile in priority_files: - if pfile in relevant_files: - files_to_fetch.append(pfile) - relevant_files.remove(pfile) - - # Add other Python files - python_files = [f for f in relevant_files if f.endswith('.py')] - files_to_fetch.extend(python_files) - for pf in python_files: - if pf in relevant_files: - relevant_files.remove(pf) - - # Add other important files (JS, CSS, JSON, etc.) - other_important = [f for f in relevant_files if any(f.endswith(ext) for ext in ['.js', '.css', '.json', '.html', '.yml', '.yaml'])] - files_to_fetch.extend(other_important) - - # Limit to reasonable number of files to avoid overwhelming - files_to_fetch = files_to_fetch[:20] # Max 20 files - - # Download each file - for file_name in files_to_fetch: - try: - content_path = api.hf_hub_download( - repo_id=f"{username}/{project_name}", - filename=file_name, - repo_type="space" - ) - - # Read file content with appropriate encoding - try: - with open(content_path, 'r', encoding='utf-8') as f: - files[file_name] = f.read() - except UnicodeDecodeError: - # For binary files or files with different encoding - with open(content_path, 'rb') as f: - content = f.read() - # Skip binary files that are too large or not text - if len(content) > 100000: # Skip files > 100KB - files[file_name] = f"[Binary file: {file_name} - {len(content)} bytes]" - else: - try: - files[file_name] = content.decode('utf-8') - except: - files[file_name] = f"[Binary file: {file_name} - {len(content)} bytes]" - except Exception as e: - files[file_name] = f"[Error loading {file_name}: {str(e)}]" - - except Exception as e: - # Fallback to single file loading - return {} - - return files - -def format_multi_file_space(files: dict, username: str, project_name: str, sdk: str) -> str: - """Format multiple files from a space into a readable format""" - if not files: - return "" - - header = f"""IMPORTED PROJECT FROM HUGGING FACE SPACE -============================================== - -Space: {username}/{project_name} -SDK: {sdk} -Files: {len(files)} files loaded - -""" - - # Sort files to show main files first - main_files = [] - other_files = [] - - priority_order = ["app.py", "main.py", "streamlit_app.py", "gradio_app.py", "index.html", "requirements.txt"] - - for priority_file in priority_order: - if priority_file in files: - main_files.append(priority_file) - - for file_name in sorted(files.keys()): - if file_name not in main_files: - other_files.append(file_name) - - content = header - - # Add main files first - for file_name in main_files: - content += f"=== {file_name} ===\n{files[file_name]}\n\n" - - # Add other files - for file_name in other_files: - content += f"=== {file_name} ===\n{files[file_name]}\n\n" - - return content - -def fetch_hf_space_content(username: str, project_name: str) -> str: - """Fetch content from a Hugging Face Space""" - try: - import requests - from huggingface_hub import HfApi - - # Try to get space info first - api = HfApi() - space_info = api.space_info(f"{username}/{project_name}") - - # Check if this is a transformers.js space first - if space_info.sdk == "static" and detect_transformers_js_space(api, username, project_name): - files = fetch_transformers_js_files(api, username, project_name) - return combine_transformers_js_files(files, username, project_name) - - # Use the new multi-file loading approach for all space types - sdk = space_info.sdk - files = fetch_all_space_files(api, username, project_name, sdk) - - if files: - # Use the multi-file format - return format_multi_file_space(files, username, project_name, sdk) - else: - # Fallback to single file loading for compatibility - main_file = None - - # Define file patterns to try based on SDK - if sdk == "static": - file_patterns = ["index.html"] - elif sdk == "gradio": - file_patterns = ["app.py", "main.py", "gradio_app.py"] - elif sdk == "streamlit": - file_patterns = ["streamlit_app.py", "src/streamlit_app.py", "app.py", "src/app.py", "main.py", "src/main.py", "Home.py", "src/Home.py", "🏠_Home.py", "src/🏠_Home.py", "1_🏠_Home.py", "src/1_🏠_Home.py"] - else: - # Try common files for unknown SDKs - file_patterns = ["app.py", "src/app.py", "index.html", "streamlit_app.py", "src/streamlit_app.py", "main.py", "src/main.py", "Home.py", "src/Home.py"] - - # Try to find and download the main file - for file in file_patterns: - try: - content = api.hf_hub_download( - repo_id=f"{username}/{project_name}", - filename=file, - repo_type="space" - ) - main_file = file - break - except: - continue - - if main_file: - content = api.hf_hub_download( - repo_id=f"{username}/{project_name}", - filename=main_file, - repo_type="space" - ) - - # Read the file content - with open(content, 'r', encoding='utf-8') as f: - file_content = f.read() - - return f"""IMPORTED PROJECT FROM HUGGING FACE SPACE -============================================== - -Space: {username}/{project_name} -SDK: {sdk} -Main File: {main_file} - -{file_content}""" - else: - # Try to get more information about available files for debugging - try: - from huggingface_hub import list_repo_files - files_list = list_repo_files(repo_id=f"{username}/{project_name}", repo_type="space") - available_files = [f for f in files_list if not f.startswith('.') and not f.endswith('.md')] - return f"Error: Could not find main file in space {username}/{project_name}.\n\nSDK: {sdk}\nAvailable files: {', '.join(available_files[:10])}{'...' if len(available_files) > 10 else ''}\n\nTried looking for: {', '.join(file_patterns)}" - except: - return f"Error: Could not find main file in space {username}/{project_name}. Expected files for {sdk} SDK: {', '.join(file_patterns) if 'file_patterns' in locals() else 'standard files'}" - - except Exception as e: - return f"Error fetching space content: {str(e)}" - -def load_project_from_url(url: str) -> Tuple[str, str]: - """Load project from Hugging Face Space URL""" - # Validate URL - is_valid, username, project_name = check_hf_space_url(url) - - if not is_valid: - return "Error: Please enter a valid Hugging Face Spaces URL.\n\nExpected format: https://huggingface.co/spaces/username/project", "" - - # Fetch content - content = fetch_hf_space_content(username, project_name) - - if content.startswith("Error:"): - return content, "" - - # Extract the actual code content by removing metadata - lines = content.split('\n') - code_start = 0 - for i, line in enumerate(lines): - # Skip metadata lines and find the start of actual code - if (line.strip() and - not line.startswith('=') and - not line.startswith('IMPORTED PROJECT') and - not line.startswith('Space:') and - not line.startswith('SDK:') and - not line.startswith('Main File:')): - code_start = i - break - - code_content = '\n'.join(lines[code_start:]) - - return f"✅ Successfully imported project from {username}/{project_name}", code_content - -# -------- Repo/Model Import (GitHub & Hugging Face model) -------- -def _parse_repo_or_model_url(url: str) -> Tuple[str, Optional[dict]]: - """Parse a URL and detect if it's a GitHub repo, HF Space, or HF Model. - - Returns a tuple of (kind, meta) where kind in {"github", "hf_space", "hf_model", "unknown"} - Meta contains parsed identifiers. - """ - try: - parsed = urlparse(url.strip()) - netloc = (parsed.netloc or "").lower() - path = (parsed.path or "").strip("/") - # Hugging Face spaces - if ("huggingface.co" in netloc or netloc.endswith("hf.co")) and path.startswith("spaces/"): - parts = path.split("/") - if len(parts) >= 3: - return "hf_space", {"username": parts[1], "project": parts[2]} - # Hugging Face model repo (default) - if ("huggingface.co" in netloc or netloc.endswith("hf.co")) and not path.startswith(("spaces/", "datasets/", "organizations/")): - parts = path.split("/") - if len(parts) >= 2: - repo_id = f"{parts[0]}/{parts[1]}" - return "hf_model", {"repo_id": repo_id} - # GitHub repo - if "github.com" in netloc: - parts = path.split("/") - if len(parts) >= 2: - return "github", {"owner": parts[0], "repo": parts[1]} - except Exception: - pass - return "unknown", None - -def _fetch_hf_model_readme(repo_id: str) -> str | None: - """Fetch README.md (model card) for a Hugging Face model repo.""" - try: - api = HfApi() - # Try direct README.md first - try: - local_path = api.hf_hub_download(repo_id=repo_id, filename="README.md", repo_type="model") - with open(local_path, "r", encoding="utf-8") as f: - return f.read() - except Exception: - # Some repos use README at root without explicit type - local_path = api.hf_hub_download(repo_id=repo_id, filename="README.md") - with open(local_path, "r", encoding="utf-8") as f: - return f.read() - except Exception: - return None - -def _fetch_github_readme(owner: str, repo: str) -> str | None: - """Fetch README.md from a GitHub repo via raw URLs, trying HEAD/main/master.""" - bases = [ - f"https://raw.githubusercontent.com/{owner}/{repo}/HEAD/README.md", - f"https://raw.githubusercontent.com/{owner}/{repo}/main/README.md", - f"https://raw.githubusercontent.com/{owner}/{repo}/master/README.md", - ] - for url in bases: - try: - resp = requests.get(url, timeout=10) - if resp.status_code == 200 and resp.text: - return resp.text - except Exception: - continue - return None - -def _extract_transformers_or_diffusers_snippet(markdown_text: str) -> Tuple[str | None, str | None]: - """Extract the most relevant Python code block referencing transformers/diffusers from markdown. - - Returns (language, code). If not found, returns (None, None). - """ - if not markdown_text: - return None, None - # Find fenced code blocks - code_blocks = [] - import re as _re - for match in _re.finditer(r"```([\w+-]+)?\s*\n([\s\S]*?)```", markdown_text, _re.IGNORECASE): - lang = (match.group(1) or "").lower() - code = match.group(2) or "" - code_blocks.append((lang, code.strip())) - # Filter for transformers/diffusers relevance - def score_block(code: str) -> int: - score = 0 - kws = [ - "from transformers", "import transformers", "pipeline(", - "AutoModel", "AutoTokenizer", "text-generation", - "from diffusers", "import diffusers", "DiffusionPipeline", - "StableDiffusion", "UNet", "EulerDiscreteScheduler" - ] - for kw in kws: - if kw in code: - score += 1 - # Prefer longer, self-contained snippets - score += min(len(code) // 200, 5) - return score - scored = sorted( - [cb for cb in code_blocks if any(kw in cb[1] for kw in ["transformers", "diffusers", "pipeline(", "StableDiffusion"])], - key=lambda x: score_block(x[1]), - reverse=True, - ) - if scored: - return scored[0][0] or None, scored[0][1] - return None, None - -def _infer_task_from_context(snippet: str | None, pipeline_tag: str | None) -> str: - """Infer a task string for transformers pipeline; fall back to provided pipeline_tag or 'text-generation'.""" - if pipeline_tag: - return pipeline_tag - if not snippet: - return "text-generation" - lowered = snippet.lower() - task_hints = { - "text-generation": ["text-generation", "automodelforcausallm"], - "text2text-generation": ["text2text-generation", "t5forconditionalgeneration"], - "fill-mask": ["fill-mask", "automodelformaskedlm"], - "summarization": ["summarization"], - "translation": ["translation"], - "text-classification": ["text-classification", "sequenceclassification"], - "automatic-speech-recognition": ["speechrecognition", "automatic-speech-recognition", "asr"], - "image-classification": ["image-classification"], - "zero-shot-image-classification": ["zero-shot-image-classification"], - } - for task, hints in task_hints.items(): - if any(h in lowered for h in hints): - return task - # Inspect explicit pipeline("task") - import re as _re - m = _re.search(r"pipeline\(\s*['\"]([\w\-]+)['\"]", snippet) - if m: - return m.group(1) - return "text-generation" - -def _generate_gradio_app_from_transformers(repo_id: str, task: str) -> str: - """Build a minimal Gradio app using transformers.pipeline for a given model and task.""" - # Map simple UI per task; default to text in/out - if task in {"text-generation", "text2text-generation", "summarization", "translation", "fill-mask"}: - return ( - "import gradio as gr\n" - "from transformers import pipeline\n\n" - f"pipe = pipeline(task='{task}', model='{repo_id}')\n\n" - "def infer(prompt, max_new_tokens=256, temperature=0.7, top_p=0.95):\n" - " if '\u2047' in prompt:\n" - " # Fill-mask often uses [MASK]; keep generic handling\n" - " pass\n" - " out = pipe(prompt, max_new_tokens=max_new_tokens, do_sample=True, temperature=temperature, top_p=top_p)\n" - " if isinstance(out, list):\n" - " if isinstance(out[0], dict):\n" - " return next(iter(out[0].values())) if out[0] else str(out)\n" - " return str(out[0])\n" - " return str(out)\n\n" - "demo = gr.Interface(\n" - " fn=infer,\n" - " inputs=[gr.Textbox(label='Input', lines=8), gr.Slider(1, 2048, value=256, label='max_new_tokens'), gr.Slider(0.0, 1.5, value=0.7, step=0.01, label='temperature'), gr.Slider(0.0, 1.0, value=0.95, step=0.01, label='top_p')],\n" - " outputs=gr.Textbox(label='Output', lines=8),\n" - " title='Transformers Demo'\n" - ")\n\n" - "if __name__ == '__main__':\n" - " demo.launch()\n" - ) - elif task in {"text-classification"}: - return ( - "import gradio as gr\n" - "from transformers import pipeline\n\n" - f"pipe = pipeline(task='{task}', model='{repo_id}')\n\n" - "def infer(text):\n" - " out = pipe(text)\n" - " # Expect list of dicts with label/score\n" - " return {o['label']: float(o['score']) for o in out}\n\n" - "demo = gr.Interface(fn=infer, inputs=gr.Textbox(lines=6), outputs=gr.Label(), title='Text Classification')\n\n" - "if __name__ == '__main__':\n" - " demo.launch()\n" - ) - else: - # Fallback generic text pipeline (pipeline infers task from model config) - return ( - "import gradio as gr\n" - "from transformers import pipeline\n\n" - f"pipe = pipeline(model='{repo_id}')\n\n" - "def infer(prompt):\n" - " out = pipe(prompt)\n" - " if isinstance(out, list):\n" - " if isinstance(out[0], dict):\n" - " return next(iter(out[0].values())) if out[0] else str(out)\n" - " return str(out[0])\n" - " return str(out)\n\n" - "demo = gr.Interface(fn=infer, inputs=gr.Textbox(lines=8), outputs=gr.Textbox(lines=8), title='Transformers Demo')\n\n" - "if __name__ == '__main__':\n" - " demo.launch()\n" - ) - -def _generate_gradio_app_from_diffusers(repo_id: str) -> str: - """Build a minimal Gradio app for text-to-image using diffusers.""" - return ( - "import gradio as gr\n" - "import torch\n" - "from diffusers import DiffusionPipeline\n\n" - f"pipe = DiffusionPipeline.from_pretrained('{repo_id}')\n" - "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n" - "pipe = pipe.to(device)\n\n" - "def infer(prompt, guidance_scale=7.0, num_inference_steps=30, seed=0):\n" - " generator = None if seed == 0 else torch.Generator(device=device).manual_seed(int(seed))\n" - " image = pipe(prompt, guidance_scale=float(guidance_scale), num_inference_steps=int(num_inference_steps), generator=generator).images[0]\n" - " return image\n\n" - "demo = gr.Interface(\n" - " fn=infer,\n" - " inputs=[gr.Textbox(label='Prompt'), gr.Slider(0.0, 15.0, value=7.0, step=0.1, label='guidance_scale'), gr.Slider(1, 100, value=30, step=1, label='num_inference_steps'), gr.Slider(0, 2**32-1, value=0, step=1, label='seed')],\n" - " outputs=gr.Image(type='pil'),\n" - " title='Diffusers Text-to-Image'\n" - ")\n\n" - "if __name__ == '__main__':\n" - " demo.launch()\n" - ) - -def import_repo_to_app(url: str, framework: str = "Gradio") -> Tuple[str, str, str]: - """Import a GitHub or HF model repo and return the raw code snippet from README/model card. - - Returns (status_markdown, code_snippet, preview_html). Preview left empty; UI will decide. - """ - if not url or not url.strip(): - return "Please enter a repository URL.", "", "" - kind, meta = _parse_repo_or_model_url(url) - if kind == "hf_space" and meta: - # Spaces already contain runnable apps; keep existing behavior to fetch main file raw - status, code = load_project_from_url(url) - return status, code, "" - # Fetch markdown - markdown = None - repo_id = None - pipeline_tag = None - library_name = None - if kind == "hf_model" and meta: - repo_id = meta.get("repo_id") - # Try model info to get pipeline tag/library - try: - api = HfApi() - info = api.model_info(repo_id) - pipeline_tag = getattr(info, "pipeline_tag", None) - library_name = getattr(info, "library_name", None) - except Exception: - pass - markdown = _fetch_hf_model_readme(repo_id) - elif kind == "github" and meta: - markdown = _fetch_github_readme(meta.get("owner"), meta.get("repo")) - else: - return "Error: Unsupported or invalid URL. Provide a GitHub repo or Hugging Face model URL.", "", "" - - if not markdown: - return "Error: Could not fetch README/model card.", "", "" - - lang, snippet = _extract_transformers_or_diffusers_snippet(markdown) - if not snippet: - return "Error: No relevant transformers/diffusers code block found in README/model card.", "", "" - - status = "✅ Imported code snippet from README/model card. Use it as a starting point." - return status, snippet, "" - -# Gradio Theme Configurations with proper theme objects -def get_saved_theme(): - """Get the saved theme preference from file""" - try: - if os.path.exists('.theme_preference'): - with open('.theme_preference', 'r') as f: - return f.read().strip() - except: - pass - return "Developer" -def save_theme_preference(theme_name): - """Save theme preference to file""" - try: - with open('.theme_preference', 'w') as f: - f.write(theme_name) - except: - pass - -THEME_CONFIGS = { - "Default": { - "theme": gr.themes.Default(), - "description": "Gradio's standard theme with clean orange accents" - }, - "Base": { - "theme": gr.themes.Base( - primary_hue="blue", - secondary_hue="slate", - neutral_hue="slate", - text_size="sm", - spacing_size="sm", - radius_size="md" - ), - "description": "Minimal foundation theme with blue accents" - }, - "Soft": { - "theme": gr.themes.Soft( - primary_hue="emerald", - secondary_hue="emerald", - neutral_hue="slate", - text_size="sm", - spacing_size="md", - radius_size="lg" - ), - "description": "Gentle rounded theme with soft emerald colors" - }, - "Monochrome": { - "theme": gr.themes.Monochrome( - primary_hue="slate", - secondary_hue="slate", - neutral_hue="slate", - text_size="sm", - spacing_size="sm", - radius_size="sm" - ), - "description": "Elegant black and white design" - }, - "Glass": { - "theme": gr.themes.Glass( - primary_hue="blue", - secondary_hue="blue", - neutral_hue="slate", - text_size="sm", - spacing_size="md", - radius_size="lg" - ), - "description": "Modern glassmorphism with blur effects" - }, - "Dark Ocean": { - "theme": gr.themes.Base( - primary_hue="blue", - secondary_hue="slate", - neutral_hue="slate", - text_size="sm", - spacing_size="sm", - radius_size="md" - ).set( - body_background_fill="#0f172a", - body_background_fill_dark="#0f172a", - background_fill_primary="#3b82f6", - background_fill_secondary="#1e293b", - border_color_primary="#334155", - block_background_fill="#1e293b", - block_border_color="#334155", - body_text_color="#f1f5f9", - body_text_color_dark="#f1f5f9", - block_label_text_color="#f1f5f9", - block_label_text_color_dark="#f1f5f9", - block_title_text_color="#f1f5f9", - block_title_text_color_dark="#f1f5f9", - input_background_fill="#0f172a", - input_background_fill_dark="#0f172a", - input_border_color="#334155", - input_border_color_dark="#334155", - button_primary_background_fill="#3b82f6", - button_primary_border_color="#3b82f6", - button_secondary_background_fill="#334155", - button_secondary_border_color="#475569" - ), - "description": "Deep blue dark theme perfect for coding" - }, - "Cyberpunk": { - "theme": gr.themes.Base( - primary_hue="fuchsia", - secondary_hue="cyan", - neutral_hue="slate", - text_size="sm", - spacing_size="sm", - radius_size="none", - font="Orbitron" - ).set( - body_background_fill="#0a0a0f", - body_background_fill_dark="#0a0a0f", - background_fill_primary="#ff10f0", - background_fill_secondary="#1a1a2e", - border_color_primary="#00f5ff", - block_background_fill="#1a1a2e", - block_border_color="#00f5ff", - body_text_color="#00f5ff", - body_text_color_dark="#00f5ff", - block_label_text_color="#ff10f0", - block_label_text_color_dark="#ff10f0", - block_title_text_color="#ff10f0", - block_title_text_color_dark="#ff10f0", - input_background_fill="#0a0a0f", - input_background_fill_dark="#0a0a0f", - input_border_color="#00f5ff", - input_border_color_dark="#00f5ff", - button_primary_background_fill="#ff10f0", - button_primary_border_color="#ff10f0", - button_secondary_background_fill="#1a1a2e", - button_secondary_border_color="#00f5ff" - ), - "description": "Futuristic neon cyber aesthetics" - }, - "Forest": { - "theme": gr.themes.Soft( - primary_hue="emerald", - secondary_hue="green", - neutral_hue="emerald", - text_size="sm", - spacing_size="md", - radius_size="lg" - ).set( - body_background_fill="#f0fdf4", - body_background_fill_dark="#064e3b", - background_fill_primary="#059669", - background_fill_secondary="#ecfdf5", - border_color_primary="#bbf7d0", - block_background_fill="#ffffff", - block_border_color="#d1fae5", - body_text_color="#064e3b", - body_text_color_dark="#f0fdf4", - block_label_text_color="#064e3b", - block_label_text_color_dark="#f0fdf4", - block_title_text_color="#059669", - block_title_text_color_dark="#10b981" - ), - "description": "Nature-inspired green earth tones" - }, - "High Contrast": { - "theme": gr.themes.Base( - primary_hue="yellow", - secondary_hue="slate", - neutral_hue="slate", - text_size="lg", - spacing_size="lg", - radius_size="sm" - ).set( - body_background_fill="#ffffff", - body_background_fill_dark="#ffffff", - background_fill_primary="#000000", - background_fill_secondary="#ffffff", - border_color_primary="#000000", - block_background_fill="#ffffff", - block_border_color="#000000", - body_text_color="#000000", - body_text_color_dark="#000000", - block_label_text_color="#000000", - block_label_text_color_dark="#000000", - block_title_text_color="#000000", - block_title_text_color_dark="#000000", - input_background_fill="#ffffff", - input_background_fill_dark="#ffffff", - input_border_color="#000000", - input_border_color_dark="#000000", - button_primary_background_fill="#ffff00", - button_primary_border_color="#000000", - button_secondary_background_fill="#ffffff", - button_secondary_border_color="#000000" - ), - "description": "Accessibility-focused high visibility" - }, - "Developer": { - "theme": gr.themes.Base( - primary_hue="blue", - secondary_hue="slate", - neutral_hue="slate", - text_size="sm", - spacing_size="sm", - radius_size="sm", - font="Consolas" - ).set( - # VS Code exact colors - body_background_fill="#1e1e1e", # VS Code editor background - body_background_fill_dark="#1e1e1e", - background_fill_primary="#007acc", # VS Code blue accent - background_fill_secondary="#252526", # VS Code sidebar background - border_color_primary="#3e3e42", # VS Code border color - block_background_fill="#252526", # VS Code panel background - block_border_color="#3e3e42", # VS Code subtle borders - body_text_color="#cccccc", # VS Code default text - body_text_color_dark="#cccccc", - block_label_text_color="#cccccc", - block_label_text_color_dark="#cccccc", - block_title_text_color="#ffffff", # VS Code active text - block_title_text_color_dark="#ffffff", - input_background_fill="#2d2d30", # VS Code input background - input_background_fill_dark="#2d2d30", - input_border_color="#3e3e42", # VS Code input border - input_border_color_dark="#3e3e42", - input_border_color_focus="#007acc", # VS Code focus border - input_border_color_focus_dark="#007acc", - button_primary_background_fill="#007acc", # VS Code button blue - button_primary_border_color="#007acc", - button_primary_background_fill_hover="#0e639c", # VS Code button hover - button_secondary_background_fill="#2d2d30", - button_secondary_border_color="#3e3e42", - button_secondary_text_color="#cccccc" - ), - "description": "Authentic VS Code dark theme with exact color matching" - } -} - -# Additional theme information for developers -THEME_FEATURES = { - "Default": ["Orange accents", "Clean layout", "Standard Gradio look"], - "Base": ["Blue accents", "Minimal styling", "Clean foundation"], - "Soft": ["Rounded corners", "Emerald colors", "Comfortable viewing"], - "Monochrome": ["Black & white", "High elegance", "Timeless design"], - "Glass": ["Glassmorphism", "Blur effects", "Translucent elements"], - "Dark Ocean": ["Deep blue palette", "Dark theme", "Easy on eyes"], - "Cyberpunk": ["Neon cyan/magenta", "Futuristic fonts", "Cyber vibes"], - "Forest": ["Nature inspired", "Green tones", "Organic rounded"], - "High Contrast": ["Black/white/yellow", "High visibility", "Accessibility"], - "Developer": ["Authentic VS Code colors", "Consolas/Monaco fonts", "Exact theme matching"] -} - -# Load saved theme and apply it -current_theme_name = get_saved_theme() -current_theme = THEME_CONFIGS[current_theme_name]["theme"] - -# Main application with proper Gradio theming -with gr.Blocks( - title="AnyCoder - AI Code Generator", - theme=current_theme, - css=""" - .theme-info { font-size: 0.9em; opacity: 0.8; } - .theme-description { padding: 8px 0; } - .theme-status { - padding: 10px; - border-radius: 8px; - background: rgba(34, 197, 94, 0.1); - border: 1px solid rgba(34, 197, 94, 0.2); - margin: 8px 0; - } - .restart-needed { - padding: 12px; - border-radius: 8px; - background: rgba(255, 193, 7, 0.1); - border: 1px solid rgba(255, 193, 7, 0.3); - margin: 8px 0; - text-align: center; - } - /* Authentication status styling */ - .auth-status { - padding: 8px 12px; - border-radius: 6px; - margin: 8px 0; - font-weight: 500; - text-align: center; - } - .auth-status:has-text("🔒") { - background: rgba(231, 76, 60, 0.1); - border: 1px solid rgba(231, 76, 60, 0.3); - color: #e74c3c; - } - .auth-status:has-text("✅") { - background: rgba(46, 204, 113, 0.1); - border: 1px solid rgba(46, 204, 113, 0.3); - color: #2ecc71; - } - """ -) as demo: - history = gr.State([]) - setting = gr.State({ - "system": HTML_SYSTEM_PROMPT, - }) - current_model = gr.State(DEFAULT_MODEL) - open_panel = gr.State(None) - last_login_state = gr.State(None) - - with gr.Sidebar() as sidebar: - login_button = gr.LoginButton() - - - - - # Unified Import section - import_header_md = gr.Markdown("📥 Import Project (Space, GitHub, or Model)", visible=False) - load_project_url = gr.Textbox( - label="Project URL", - placeholder="https://huggingface.co/spaces/user/space OR https://huggingface.co/user/model OR https://github.com/owner/repo", - lines=1 - , visible=False) - load_project_btn = gr.Button("📥 Import Project", variant="secondary", size="sm", visible=True) - load_project_status = gr.Markdown(visible=False) - - input = gr.Textbox( - label="What would you like to build?", - placeholder="🔒 Please log in with Hugging Face to use AnyCoder...", - lines=3, - visible=True, - interactive=False - ) - # Language dropdown for code generation (add Streamlit and Gradio as first-class options) - language_choices = [ - "html", "gradio", "transformers.js", "streamlit", "comfyui", "react" - ] - language_dropdown = gr.Dropdown( - choices=language_choices, - value="html", - label="Code Language", - visible=True - ) - # Removed image generation components - with gr.Row(): - btn = gr.Button("Generate", variant="secondary", size="lg", scale=2, visible=True, interactive=False) - clear_btn = gr.Button("Clear", variant="secondary", size="sm", scale=1, visible=True) - # --- Deploy components (visible by default) --- - deploy_header_md = gr.Markdown("", visible=False) - deploy_btn = gr.Button("Publish", variant="primary", visible=True) - deploy_status = gr.Markdown(visible=False, label="Deploy status") - # --- End move --- - # Removed media generation and web search UI components - - # Removed media generation toggle event handlers - model_dropdown = gr.Dropdown( - choices=[model['name'] for model in AVAILABLE_MODELS], - value=DEFAULT_MODEL_NAME, - label="Model", - visible=True - ) - provider_state = gr.State("auto") - # Removed web search availability indicator - def on_model_change(model_name): - for m in AVAILABLE_MODELS: - if m['name'] == model_name: - return m - return AVAILABLE_MODELS[0] - def save_prompt(input): - return {setting: {"system": input}} - model_dropdown.change( - lambda model_name: on_model_change(model_name), - inputs=model_dropdown, - outputs=[current_model] - ) - # --- Remove deploy/app name/sdk from bottom column --- - # (delete the gr.Column() block containing space_name_input, sdk_dropdown, deploy_btn, deploy_status) - - with gr.Column() as main_column: - with gr.Tabs(): - with gr.Tab("Code"): - code_output = gr.Code( - language="html", - lines=25, - interactive=True, - label="Generated code" - ) - - - - # Transformers.js multi-file editors (hidden by default) - with gr.Group(visible=False) as tjs_group: - with gr.Tabs(): - with gr.Tab("index.html"): - tjs_html_code = gr.Code(language="html", lines=20, interactive=True, label="index.html") - with gr.Tab("index.js"): - tjs_js_code = gr.Code(language="javascript", lines=20, interactive=True, label="index.js") - with gr.Tab("style.css"): - tjs_css_code = gr.Code(language="css", lines=20, interactive=True, label="style.css") - - # Python multi-file editors (hidden by default) for Gradio/Streamlit - with gr.Group(visible=False) as python_group_2: - with gr.Tabs(): - with gr.Tab("app.py") as python_tab_2_1: - python_code_2_1 = gr.Code(language="python", lines=20, interactive=True, label="app.py") - with gr.Tab("file 2") as python_tab_2_2: - python_code_2_2 = gr.Code(language="python", lines=18, interactive=True, label="file 2") - - with gr.Group(visible=False) as python_group_3: - with gr.Tabs(): - with gr.Tab("app.py") as python_tab_3_1: - python_code_3_1 = gr.Code(language="python", lines=20, interactive=True, label="app.py") - with gr.Tab("file 2") as python_tab_3_2: - python_code_3_2 = gr.Code(language="python", lines=18, interactive=True, label="file 2") - with gr.Tab("file 3") as python_tab_3_3: - python_code_3_3 = gr.Code(language="python", lines=18, interactive=True, label="file 3") - - with gr.Group(visible=False) as python_group_4: - with gr.Tabs(): - with gr.Tab("app.py") as python_tab_4_1: - python_code_4_1 = gr.Code(language="python", lines=20, interactive=True, label="app.py") - with gr.Tab("file 2") as python_tab_4_2: - python_code_4_2 = gr.Code(language="python", lines=18, interactive=True, label="file 2") - with gr.Tab("file 3") as python_tab_4_3: - python_code_4_3 = gr.Code(language="python", lines=18, interactive=True, label="file 3") - with gr.Tab("file 4") as python_tab_4_4: - python_code_4_4 = gr.Code(language="python", lines=18, interactive=True, label="file 4") - - with gr.Group(visible=False) as python_group_5plus: - with gr.Tabs(): - with gr.Tab("app.py") as python_tab_5_1: - python_code_5_1 = gr.Code(language="python", lines=20, interactive=True, label="app.py") - with gr.Tab("file 2") as python_tab_5_2: - python_code_5_2 = gr.Code(language="python", lines=18, interactive=True, label="file 2") - with gr.Tab("file 3") as python_tab_5_3: - python_code_5_3 = gr.Code(language="python", lines=18, interactive=True, label="file 3") - with gr.Tab("file 4") as python_tab_5_4: - python_code_5_4 = gr.Code(language="python", lines=18, interactive=True, label="file 4") - with gr.Tab("file 5") as python_tab_5_5: - python_code_5_5 = gr.Code(language="python", lines=18, interactive=True, label="file 5") - - # Static HTML multi-file editors (hidden by default). Use separate tab groups for different file counts. - with gr.Group(visible=False) as static_group_2: - with gr.Tabs(): - with gr.Tab("index.html") as static_tab_2_1: - static_code_2_1 = gr.Code(language="html", lines=20, interactive=True, label="index.html") - with gr.Tab("file 2") as static_tab_2_2: - static_code_2_2 = gr.Code(language="html", lines=18, interactive=True, label="file 2") - - with gr.Group(visible=False) as static_group_3: - with gr.Tabs(): - with gr.Tab("index.html") as static_tab_3_1: - static_code_3_1 = gr.Code(language="html", lines=20, interactive=True, label="index.html") - with gr.Tab("file 2") as static_tab_3_2: - static_code_3_2 = gr.Code(language="html", lines=18, interactive=True, label="file 2") - with gr.Tab("file 3") as static_tab_3_3: - static_code_3_3 = gr.Code(language="html", lines=18, interactive=True, label="file 3") - - with gr.Group(visible=False) as static_group_4: - with gr.Tabs(): - with gr.Tab("index.html") as static_tab_4_1: - static_code_4_1 = gr.Code(language="html", lines=20, interactive=True, label="index.html") - with gr.Tab("file 2") as static_tab_4_2: - static_code_4_2 = gr.Code(language="html", lines=18, interactive=True, label="file 2") - with gr.Tab("file 3") as static_tab_4_3: - static_code_4_3 = gr.Code(language="html", lines=18, interactive=True, label="file 3") - with gr.Tab("file 4") as static_tab_4_4: - static_code_4_4 = gr.Code(language="html", lines=18, interactive=True, label="file 4") - - with gr.Group(visible=False) as static_group_5plus: - with gr.Tabs(): - with gr.Tab("index.html") as static_tab_5_1: - static_code_5_1 = gr.Code(language="html", lines=20, interactive=True, label="index.html") - with gr.Tab("file 2") as static_tab_5_2: - static_code_5_2 = gr.Code(language="html", lines=18, interactive=True, label="file 2") - with gr.Tab("file 3") as static_tab_5_3: - static_code_5_3 = gr.Code(language="html", lines=18, interactive=True, label="file 3") - with gr.Tab("file 4") as static_tab_5_4: - static_code_5_4 = gr.Code(language="html", lines=18, interactive=True, label="file 4") - with gr.Tab("file 5") as static_tab_5_5: - static_code_5_5 = gr.Code(language="html", lines=18, interactive=True, label="file 5") - # React Next.js multi-file editors (hidden by default) - with gr.Group(visible=False) as react_group: - with gr.Tabs(): - with gr.Tab("Dockerfile"): - react_code_dockerfile = gr.Code(language="dockerfile", lines=15, interactive=True, label="Dockerfile") - with gr.Tab("package.json"): - react_code_package_json = gr.Code(language="json", lines=20, interactive=True, label="package.json") - with gr.Tab("next.config.js"): - react_code_next_config = gr.Code(language="javascript", lines=15, interactive=True, label="next.config.js") - with gr.Tab("postcss.config.js"): - react_code_postcss_config = gr.Code(language="javascript", lines=10, interactive=True, label="postcss.config.js") - with gr.Tab("tailwind.config.js"): - react_code_tailwind_config = gr.Code(language="javascript", lines=15, interactive=True, label="tailwind.config.js") - with gr.Tab("pages/_app.js"): - react_code_pages_app = gr.Code(language="javascript", lines=15, interactive=True, label="pages/_app.js") - with gr.Tab("pages/index.js"): - react_code_pages_index = gr.Code(language="javascript", lines=20, interactive=True, label="pages/index.js") - with gr.Tab("components/ChatApp.jsx"): - react_code_components = gr.Code(language="javascript", lines=25, interactive=True, label="components/ChatApp.jsx") - with gr.Tab("styles/globals.css"): - react_code_styles = gr.Code(language="css", lines=20, interactive=True, label="styles/globals.css") - - # Removed Import Logs tab for cleaner UI - # History tab hidden per user request - # with gr.Tab("History"): - # history_output = gr.Chatbot(show_label=False, height=400, type="messages") - - # Keep history_output as hidden component to maintain functionality - history_output = gr.Chatbot(show_label=False, height=400, type="messages", visible=False) - - # Global generation status view (disabled placeholder) - generating_status = gr.Markdown("", visible=False) - - # Unified import handler - def handle_import_project(url): - if not url.strip(): - return [ - gr.update(value="Please enter a URL.", visible=True), - gr.update(), - gr.update(), - [], - [], - gr.update(value="Publish", visible=False), - gr.update(), # keep import header as-is - gr.update(), # keep import button as-is - gr.update() # language dropdown - no change - ] - - kind, meta = _parse_repo_or_model_url(url) - if kind == "hf_space": - status, code = load_project_from_url(url) - # Extract space info for deployment - is_valid, username, project_name = check_hf_space_url(url) - space_name = f"{username}/{project_name}" if is_valid else "" - loaded_history = [[f"Imported Space from {url}", code]] - - # Determine the correct language/framework based on the imported content - code_lang = "html" # default - framework_type = "html" # for language dropdown - - # Check imports to determine framework for Python code - if is_streamlit_code(code): - code_lang = "python" - framework_type = "streamlit" - elif is_gradio_code(code): - code_lang = "python" - framework_type = "gradio" - elif "=== index.html ===" in code and "=== index.js ===" in code and "=== style.css ===" in code: - # This is a transformers.js app with the combined format - code_lang = "html" # Use html for code display - framework_type = "transformers.js" # But set dropdown to transformers.js - elif ("import " in code or "def " in code) and not ("" in code or "") or code.strip().startswith("") or code.strip().startswith("