diff --git "a/anycoder_app/deploy.py" "b/anycoder_app/deploy.py" deleted file mode 100644--- "a/anycoder_app/deploy.py" +++ /dev/null @@ -1,2583 +0,0 @@ -""" -Deployment utilities for publishing to HuggingFace Spaces. -Handles authentication, space creation, and code deployment. -""" -import os -import re -import json -import uuid -import tempfile -import shutil -from typing import Dict, List, Optional, Tuple -from urllib.parse import urlparse -import requests -from bs4 import BeautifulSoup -import html2text - -import gradio as gr -from huggingface_hub import HfApi, InferenceClient -from openai import OpenAI - -from .config import HF_TOKEN, get_gradio_language -from .parsers import ( - parse_transformers_js_output, parse_multipage_html_output, - parse_multi_file_python_output, parse_react_output, - remove_code_block, is_streamlit_code, is_gradio_code, - clean_requirements_txt_content, History, - format_transformers_js_output, build_transformers_inline_html, - send_transformers_to_sandbox, validate_and_autofix_files, - inline_multipage_into_single_preview, apply_search_replace_changes, - apply_transformers_js_search_replace_changes, send_to_sandbox, - format_multi_file_python_output, send_streamlit_to_stlite, - send_gradio_to_lite, extract_html_document -) -from .models import ( - get_inference_client, get_real_model_id, history_to_messages, - history_to_chatbot_messages, strip_placeholder_thinking, - is_placeholder_thinking_only, extract_last_thinking_line, - strip_thinking_tags -) -from . import prompts -from .prompts import ( - HTML_SYSTEM_PROMPT, - TRANSFORMERS_JS_SYSTEM_PROMPT, STREAMLIT_SYSTEM_PROMPT, - REACT_SYSTEM_PROMPT, REACT_FOLLOW_UP_SYSTEM_PROMPT, - JSON_SYSTEM_PROMPT, - GENERIC_SYSTEM_PROMPT, MULTIPAGE_HTML_SYSTEM_PROMPT, - DYNAMIC_MULTIPAGE_HTML_SYSTEM_PROMPT, - FollowUpSystemPrompt, GradioFollowUpSystemPrompt, - TransformersJSFollowUpSystemPrompt -) -from .docs_manager import get_comfyui_system_prompt, update_gradio_system_prompts, update_json_system_prompts - - -def check_authentication(profile: Optional[gr.OAuthProfile] = None, token: Optional[gr.OAuthToken] = 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: Optional[gr.OAuthProfile] = None, token: Optional[gr.OAuthToken] = 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 ( - gr.update(interactive=True, placeholder="Describe your application..."), # input - gr.update(interactive=True, variant="primary") # btn - ) - else: - # User not authenticated - disable main components - return ( - gr.update( - interactive=False, - placeholder="🔒 Click Sign in with Hugging Face button to use AnyCoder for free" - ), # input - gr.update(interactive=False, variant="secondary") # btn - ) - - -def generation_code(query: Optional[str], _setting: Dict[str, str], _history: Optional[History], _current_model: Dict, language: str = "html", provider: str = "auto", profile: Optional[gr.OAuthProfile] = None, token: Optional[gr.OAuthToken] = None, code_output=None, history_output=None, history=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." - if code_output is not None and history_output is not None: - yield { - code_output: error_message, - history_output: history_to_chatbot_messages(_history or []), - } - else: - yield (error_message, _history or [], history_to_chatbot_messages(_history or [])) - return - - # CRITICAL: Catch any HuggingFace API errors for non-HF models like Gemini 3 - try: - yield from _generation_code_impl(query, _setting, _history, _current_model, language, provider, profile, token, code_output, history_output, history) - except Exception as e: - import traceback - error_str = str(e) - if "Repository Not Found" in error_str and "inferenceProviderMapping" in error_str: - # This is a HuggingFace API error for a non-HF model - model_id = _current_model.get('id', 'unknown') - - # Get full traceback to see where the call originated - tb = traceback.format_exc() - print(f"DEBUG: HuggingFace API error for model {model_id}") - print(f"DEBUG: Full traceback:\n{tb}") - - error_message = f"""❌ Error: Attempted to validate model '{model_id}' against HuggingFace API, but this is not a HuggingFace model. - -This error should not occur. Please check the server logs for the full traceback. - -- Model: {model_id} -- Error: {error_str} - -Try reloading the page and selecting the model again.""" - if code_output is not None and history_output is not None: - yield { - code_output: error_message, - history_output: history_to_chatbot_messages(_history or []), - } - else: - yield (error_message, _history or [], history_to_chatbot_messages(_history or [])) - return - else: - # Re-raise other errors - raise - -def _generation_code_impl(query: Optional[str], _setting: Dict[str, str], _history: Optional[History], _current_model: Dict, language: str = "html", provider: str = "auto", profile: Optional[gr.OAuthProfile] = None, token: Optional[gr.OAuthToken] = None, code_output=None, history_output=None, history=None): - """Internal implementation of generation_code""" - - 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 if this is imported model code (should NOT be treated as existing content to modify) - is_imported_model_code = ( - "Imported model:" in _history[-1][0] or - "Imported inference provider code" in last_assistant_msg or - "Imported transformers/diffusers code" in last_assistant_msg or - "Switched code type" in _history[-1][0] - ) - - # Only treat as existing content if it's NOT imported model code - if not is_imported_model_code: - # 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=10000, - 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=10000, - 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=10000, - 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 system prompts if needed - if language == "gradio": - update_gradio_system_prompts() - print(f"[Generation] Updated Gradio system prompt (length: {len(prompts.GRADIO_SYSTEM_PROMPT)} chars)") - elif language == "json": - update_json_system_prompts() - print(f"[Generation] Updated JSON system prompt (length: {len(prompts.JSON_SYSTEM_PROMPT)} chars)") - - # 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": - # Access GRADIO_SYSTEM_PROMPT from prompts module to get updated value - system_prompt = prompts.GRADIO_SYSTEM_PROMPT - elif language == "streamlit": - system_prompt = STREAMLIT_SYSTEM_PROMPT - elif language == "json": - # Access JSON_SYSTEM_PROMPT from prompts module to get updated value - system_prompt = prompts.JSON_SYSTEM_PROMPT - elif language == "comfyui": - system_prompt = get_comfyui_system_prompt() - else: - system_prompt = GENERIC_SYSTEM_PROMPT.format(language=language) - - # Debug: Log system prompt info - prompt_preview = system_prompt[:200] if system_prompt else "None" - print(f"[Generation] Using system prompt (first 200 chars): {prompt_preview}...") - print(f"[Generation] System prompt total length: {len(system_prompt) if system_prompt else 0} chars") - - messages = history_to_messages(_history, system_prompt) - - # Use the original query without any enhancements - let the system prompt handle everything - 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": - 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 - - # Use dynamic client based on selected model - client = get_inference_client(_current_model["id"], provider) - - 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"] == "gemini-3.0-pro": - completion = client.chat.completions.create( - model="Gemini-3.0-Pro", - messages=messages, - stream=True, - max_tokens=20000 - ) - elif _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 - - # Strip thinking tags and tool call markers from all streaming chunks - chunk_content = strip_thinking_tags(chunk_content) - 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, Optional[str], Optional[str]]: - """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) -> Optional[str]: - """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) -> Optional[str]: - """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[Optional[str], Optional[str]]: - """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: Optional[str], pipeline_tag: Optional[str]) -> 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 get_trending_models(limit: int = 10) -> List[Tuple[str, str]]: - """ - Fetch top trending models from HuggingFace Hub. - - Returns a list of tuples: (display_name, model_id) - Display name format: "model_name (task)" - """ - try: - # Use the HuggingFace trending API endpoint directly - response = requests.get("https://huggingface.co/api/trending") - - if response.status_code != 200: - print(f"Failed to fetch trending models: HTTP {response.status_code}") - return [("Unable to load trending models", "")] - - trending_data = response.json() - - # The API returns {"recentlyTrending": [...]} - recently_trending = trending_data.get("recentlyTrending", []) - - if not recently_trending: - print("No trending items found in API response") - return [("No trending models available", "")] - - trending_list = [] - count = 0 - - # Process trending items, filter for models only - for item in recently_trending: - if count >= limit: - break - - try: - # Check if this is a model (not a space or dataset) - repo_type = item.get("repoType") - if repo_type != "model": - continue - - # Extract model data - repo_data = item.get("repoData", {}) - model_id = repo_data.get("id") - - if not model_id: - continue - - # Get pipeline tag (task type) - pipeline_tag = repo_data.get("pipeline_tag") - - # Default to "general" if no task found - task = pipeline_tag or "general" - - # Clean up task name for display - task_display = task.replace("-", " ").title() if task != "general" else "General" - - # Create display name: "model_name (Task)" - display_name = f"{model_id} ({task_display})" - trending_list.append((display_name, model_id)) - count += 1 - - except Exception as model_error: - print(f"Error processing trending item: {model_error}") - continue - - if not trending_list: - print("No models found in trending list, using fallback") - # Fallback: use list_models with downloads sort - try: - api = HfApi() - models = api.list_models(sort="downloads", limit=limit) - for model in models: - model_id = model.id - task = getattr(model, "pipeline_tag", None) or "general" - task_display = task.replace("-", " ").title() if task != "general" else "General" - display_name = f"{model_id} ({task_display})" - trending_list.append((display_name, model_id)) - except Exception as fallback_error: - print(f"Fallback also failed: {fallback_error}") - return [("No models available", "")] - - return trending_list - - except Exception as e: - print(f"Error fetching trending models: {e}") - # Fallback to most downloaded models - try: - api = HfApi() - models = api.list_models(sort="downloads", limit=limit) - trending_list = [] - for model in models: - model_id = model.id - task = getattr(model, "pipeline_tag", None) or "general" - task_display = task.replace("-", " ").title() if task != "general" else "General" - display_name = f"{model_id} ({task_display})" - trending_list.append((display_name, model_id)) - return trending_list - except Exception: - return [("Error loading models", "")] - - -def get_trending_spaces(limit: int = 10) -> List[Tuple[str, str]]: - """ - Fetch top trending spaces from HuggingFace Hub. - - Returns a list of tuples: (display_name, space_id) - Display name format: "space_name (category)" - """ - try: - # Use the HuggingFace trending API endpoint for spaces - response = requests.get("https://huggingface.co/api/trending?type=space") - - if response.status_code != 200: - print(f"Failed to fetch trending spaces: HTTP {response.status_code}") - return [("Unable to load trending spaces", "")] - - trending_data = response.json() - - # The API returns {"recentlyTrending": [...]} - recently_trending = trending_data.get("recentlyTrending", []) - - if not recently_trending: - print("No trending spaces found in API response") - return [("No trending spaces available", "")] - - trending_list = [] - count = 0 - - # Process trending items - for item in recently_trending: - if count >= limit: - break - - try: - # Check if this is a space - repo_type = item.get("repoType") - if repo_type != "space": - continue - - # Extract space data - repo_data = item.get("repoData", {}) - space_id = repo_data.get("id") - - if not space_id: - continue - - # Get title and category - title = repo_data.get("title") or space_id - category = repo_data.get("ai_category") or repo_data.get("shortDescription", "Space") - - # Create display name: "title (category)" - # Truncate long titles - if len(title) > 40: - title = title[:37] + "..." - - display_name = f"{title} ({category})" - trending_list.append((display_name, space_id)) - count += 1 - - except Exception as space_error: - print(f"Error processing trending space: {space_error}") - continue - - if not trending_list: - return [("No spaces available", "")] - - return trending_list - - except Exception as e: - print(f"Error fetching trending spaces: {e}") - return [("Error loading spaces", "")] - - -def import_space_from_hf(space_id: str) -> Tuple[str, str, str, str]: - """ - Import a HuggingFace space by ID and extract its code. - - Returns: (status, code, language, space_url) - """ - if not space_id or space_id == "": - return "Please select a space.", "", "html", "" - - # Build space URL - space_url = f"https://huggingface.co/spaces/{space_id}" - - # Use existing load_project_from_url function - status, code = load_project_from_url(space_url) - - # Determine language based on code content - code_lang = "html" # default - language = "html" # for language dropdown - - # Check imports to determine framework for Python code - if is_streamlit_code(code): - code_lang = "python" - language = "streamlit" - elif is_gradio_code(code): - code_lang = "python" - language = "gradio" - elif "=== index.html ===" in code and "=== index.js ===" in code: - code_lang = "html" - language = "transformers.js" - elif ("import " in code or "def " in code) and not ("" in code or " Optional[str]: - """ - Generate inference provider code template based on model's pipeline tag. - - Args: - model_id: The HuggingFace model ID - pipeline_tag: The model's pipeline tag (e.g., "text-generation", "text-to-image") - has_inference_providers: Whether the model has inference providers available - - Returns: - Generated code snippet or None - """ - if not has_inference_providers: - return None - - # Map pipeline tags to code templates based on HuggingFace Inference Providers docs - # https://huggingface.co/docs/inference-providers - - # Chat Completion / Text Generation models - if pipeline_tag in ["text-generation", "conversational"]: - return f'''import os -from huggingface_hub import InferenceClient - -client = InferenceClient( - api_key=os.environ["HF_TOKEN"], -) - -completion = client.chat.completions.create( - model="{model_id}", - messages=[ - {{ - "role": "user", - "content": "What is the capital of France?" - }} - ], -) - -print(completion.choices[0].message)''' - - # Vision-Language Models (Image-Text to Text) - elif pipeline_tag in ["image-text-to-text", "visual-question-answering"]: - return f'''import os -from huggingface_hub import InferenceClient - -client = InferenceClient( - api_key=os.environ["HF_TOKEN"], -) - -completion = client.chat.completions.create( - model="{model_id}", - messages=[ - {{ - "role": "user", - "content": [ - {{ - "type": "text", - "text": "Describe this image in one sentence." - }}, - {{ - "type": "image_url", - "image_url": {{ - "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" - }} - }} - ] - }} - ], -) - -print(completion.choices[0].message)''' - - # Text to Image models - elif pipeline_tag == "text-to-image": - return f'''import os -from huggingface_hub import InferenceClient - -client = InferenceClient( - api_key=os.environ["HF_TOKEN"], -) - -# output is a PIL.Image object -image = client.text_to_image( - "Astronaut riding a horse", - model="{model_id}", -) - -# Save the image -image.save("output.png")''' - - # Text to Video models - elif pipeline_tag == "text-to-video": - return f'''import os -from huggingface_hub import InferenceClient - -client = InferenceClient( - api_key=os.environ["HF_TOKEN"], -) - -video = client.text_to_video( - "A young man walking on the street", - model="{model_id}", -) - -# Save the video -with open("output.mp4", "wb") as f: - f.write(video)''' - - # Image to Image models - elif pipeline_tag == "image-to-image": - return f'''import os -from huggingface_hub import InferenceClient -from PIL import Image - -client = InferenceClient( - api_key=os.environ["HF_TOKEN"], -) - -# Load input image -input_image = Image.open("input.jpg") - -# output is a PIL.Image object -output_image = client.image_to_image( - input_image, - model="{model_id}", - prompt="Make it more vibrant" -) - -# Save the output -output_image.save("output.png")''' - - # Text to Speech models - elif pipeline_tag == "text-to-speech": - return f'''import os -from huggingface_hub import InferenceClient - -client = InferenceClient( - api_key=os.environ["HF_TOKEN"], -) - -audio = client.text_to_speech( - "Hello world", - model="{model_id}", -) - -# Save the audio -with open("output.mp3", "wb") as f: - f.write(audio)''' - - # Automatic Speech Recognition - elif pipeline_tag == "automatic-speech-recognition": - return f'''import os -from huggingface_hub import InferenceClient - -client = InferenceClient( - api_key=os.environ["HF_TOKEN"], -) - -with open("audio.mp3", "rb") as f: - audio_data = f.read() - -result = client.automatic_speech_recognition( - audio_data, - model="{model_id}", -) - -print(result)''' - - # Feature Extraction / Embeddings - elif pipeline_tag == "feature-extraction": - return f'''import os -from huggingface_hub import InferenceClient - -client = InferenceClient( - api_key=os.environ["HF_TOKEN"], -) - -embeddings = client.feature_extraction( - "Hello world", - model="{model_id}", -) - -print(embeddings)''' - - # Default: try chat completion for conversational models - else: - # If it has inference providers but unknown task, try chat completion - return f'''import os -from huggingface_hub import InferenceClient - -client = InferenceClient( - api_key=os.environ["HF_TOKEN"], -) - -completion = client.chat.completions.create( - model="{model_id}", - messages=[ - {{ - "role": "user", - "content": "Hello, how are you?" - }} - ], -) - -print(completion.choices[0].message)''' - - -def _fetch_inference_provider_code(model_id: str) -> Optional[str]: - """ - Fetch inference provider information from HuggingFace API and generate code template. - - Args: - model_id: The HuggingFace model ID (e.g., "moonshotai/Kimi-K2-Thinking") - - Returns: - The code snippet if model has inference providers, None otherwise - """ - # Skip non-HuggingFace models (external APIs) - non_hf_models = [ - "gemini-3.0-pro", "gemini-2.5-flash", "gemini-2.5-pro", - "gemini-flash-latest", "gemini-flash-lite-latest", - "gpt-5", "gpt-5.1", "gpt-5.1-instant", "gpt-5.1-codex", "gpt-5.1-codex-mini", - "grok-4", "Grok-Code-Fast-1", - "claude-opus-4.1", "claude-sonnet-4.5", "claude-haiku-4.5", - "qwen3-30b-a3b-instruct-2507", "qwen3-30b-a3b-thinking-2507", - "qwen3-coder-30b-a3b-instruct", "qwen3-max-preview", - "kimi-k2-turbo-preview", "step-3", - "codestral-2508", "mistral-medium-2508", - "stealth-model-1", - "openrouter/sonoma-dusk-alpha", "openrouter/sonoma-sky-alpha", - "x-ai/grok-4.1-fast", "openrouter/sherlock-think-alpha" - ] - - if model_id in non_hf_models: - return None - - try: - # Fetch trending models data from HuggingFace API - response = requests.get("https://huggingface.co/api/trending", timeout=10) - - if response.status_code != 200: - print(f"Failed to fetch trending models API: HTTP {response.status_code}") - return None - - trending_data = response.json() - recently_trending = trending_data.get("recentlyTrending", []) - - # Find the specific model in trending data - model_info = None - for item in recently_trending: - repo_data = item.get("repoData", {}) - if repo_data.get("id") == model_id: - model_info = repo_data - break - - # If not found in trending, try to get model info directly from API - if not model_info: - try: - api = HfApi() - info = api.model_info(model_id) - pipeline_tag = getattr(info, "pipeline_tag", None) - - # Check if model has inference providers via model info - # Note: The direct API might not have availableInferenceProviders - # In this case, we'll generate a generic template - has_inference = pipeline_tag is not None - - if has_inference: - return _generate_inference_code_template(model_id, pipeline_tag, True) - except Exception as e: - print(f"Could not fetch model info for {model_id}: {e}") - return None - else: - # Extract pipeline tag and inference providers info - pipeline_tag = model_info.get("pipeline_tag") - inference_providers = model_info.get("availableInferenceProviders", []) - has_inference_providers = len(inference_providers) > 0 - - # Generate code template based on pipeline tag - return _generate_inference_code_template(model_id, pipeline_tag, has_inference_providers) - - return None - - except Exception as e: - print(f"Error fetching inference provider code: {e}") - return None - - -# Global storage for code alternatives (used when both inference and local code are available) -_model_code_alternatives = {} - - -def store_model_code_alternatives(model_id: str, inference_code: Optional[str], local_code: Optional[str]): - """Store both code alternatives for a model for later retrieval.""" - global _model_code_alternatives - _model_code_alternatives[model_id] = { - 'inference': inference_code, - 'local': local_code - } - - -def get_model_code_alternatives(model_id: str) -> Dict[str, Optional[str]]: - """Retrieve stored code alternatives for a model.""" - global _model_code_alternatives - return _model_code_alternatives.get(model_id, {'inference': None, 'local': None}) - - -def import_model_from_hf(model_id: str, prefer_local: bool = False) -> Tuple[str, str, str, str]: - """ - Import a HuggingFace model by ID and extract code snippet. - Tries to fetch both inference provider code and transformers/diffusers code from README. - - Args: - model_id: The HuggingFace model ID - prefer_local: If True and both options available, return local code instead of inference code - - Returns: (status, code, language, model_url) - """ - if not model_id or model_id == "": - return "Please select a model.", "", "python", "" - - # Skip non-HuggingFace models (external APIs) - these are not importable - non_hf_models = [ - "gemini-3.0-pro", "gemini-2.5-flash", "gemini-2.5-pro", - "gemini-flash-latest", "gemini-flash-lite-latest", - "gpt-5", "gpt-5.1", "gpt-5.1-instant", "gpt-5.1-codex", "gpt-5.1-codex-mini", - "grok-4", "Grok-Code-Fast-1", - "claude-opus-4.1", "claude-sonnet-4.5", "claude-haiku-4.5", - "qwen3-30b-a3b-instruct-2507", "qwen3-30b-a3b-thinking-2507", - "qwen3-coder-30b-a3b-instruct", "qwen3-max-preview", - "kimi-k2-turbo-preview", "step-3", - "codestral-2508", "mistral-medium-2508", - "stealth-model-1", - "openrouter/sonoma-dusk-alpha", "openrouter/sonoma-sky-alpha", - "x-ai/grok-4.1-fast", "openrouter/sherlock-think-alpha" - ] - - if model_id in non_hf_models: - return f"❌ `{model_id}` is not a HuggingFace model and cannot be imported. This model is accessed via external API.", "", "python", "" - - # Build model URL - model_url = f"https://huggingface.co/{model_id}" - - # Try to fetch both types of code - inference_code = _fetch_inference_provider_code(model_id) - - # Also try to extract transformers/diffusers code from README - readme_status, readme_code, _ = import_repo_to_app(model_url) - has_readme_code = readme_code and ("transformers" in readme_code or "diffusers" in readme_code) - - # Store both alternatives for later switching - store_model_code_alternatives(model_id, inference_code, readme_code if has_readme_code else None) - - # Build status message and code based on what's available - if inference_code and has_readme_code: - # Both available - provide choice - if prefer_local: - status = f"""✅ **Found multiple code options for `{model_id}`** - -**Currently showing:** Local Transformers/Diffusers Code (Option 2) 💻 - -**Option 1: Inference Provider Code (Serverless)** ⚡ -- Uses HuggingFace Inference API (serverless, pay-per-use) -- No GPU required, instant startup -- Requires `HF_TOKEN` environment variable - -**Option 2: Local Transformers/Diffusers Code (Currently Active)** 💻 -- Runs locally on your hardware -- Requires GPU for optimal performance -- Full control over model parameters - ---- - -To switch to inference provider code, click the button below or ask: "Show me the inference provider code instead" -""" - code = readme_code - else: - status = f"""✅ **Found multiple code options for `{model_id}`** - -**Currently showing:** Inference Provider Code (Option 1) ⚡ *Recommended* - -**Option 1: Inference Provider Code (Serverless - Currently Active)** ⚡ -- Uses HuggingFace Inference API (serverless, pay-per-use) -- No GPU required, instant startup -- Requires `HF_TOKEN` environment variable - -**Option 2: Local Transformers/Diffusers Code** 💻 -- Runs locally on your hardware -- Requires GPU for optimal performance -- Full control over model parameters - ---- - -To switch to local transformers/diffusers code, click the button below or ask: "Show me the local transformers code instead" -""" - code = inference_code - - language = "gradio" - return status, code, language, model_url - - elif inference_code: - # Only inference provider code available - status = f"✅ Imported inference provider code for `{model_id}` (serverless inference)" - language = "gradio" - return status, inference_code, language, model_url - - elif has_readme_code: - # Only README code available - status = f"✅ Imported transformers/diffusers code from README for `{model_id}` (local inference)" - language = "gradio" - return status, readme_code, language, model_url - - else: - # No code found - status = f"⚠️ No inference provider or transformers/diffusers code found for `{model_id}`" - return status, "", "python", model_url - - -def switch_model_code_type(model_id: str, current_code: str) -> Tuple[str, str]: - """ - Switch between inference provider code and local transformers/diffusers code. - - Args: - model_id: The model ID - current_code: The currently displayed code - - Returns: (status_message, new_code) - """ - alternatives = get_model_code_alternatives(model_id) - inference_code = alternatives['inference'] - local_code = alternatives['local'] - - if not inference_code and not local_code: - return "⚠️ No alternative code available for this model.", current_code - - # Determine which code is currently shown - is_showing_inference = current_code == inference_code - - if is_showing_inference and local_code: - # Switch to local code - status = f"✅ Switched to **Local Transformers/Diffusers Code** for `{model_id}` 💻\n\nThis code runs locally on your hardware." - return status, local_code - elif not is_showing_inference and inference_code: - # Switch to inference provider code - status = f"✅ Switched to **Inference Provider Code** for `{model_id}` ⚡\n\nThis code uses serverless HuggingFace Inference API." - return status, inference_code - else: - return "⚠️ Alternative code type not available for this model.", current_code - - -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, "" -