app.py

import os
import gradio as gr
import spaces
import zipfile
import json
import re
import uuid
from huggingface_hub import hf_hub_download
from llama_cpp import Llama
from llama_cpp_agent import LlamaCppAgent, MessagesFormatterType
from llama_cpp_agent.providers import LlamaCppPythonProvider

MODEL_NAME = "neuraldaredevil-8b-abliterated-q4_k_m-imat.gguf"
MODEL_PATH = f"models/{MODEL_NAME}"

# Pre-download model
hf_hub_download(
    repo_id="tHottie/NeuralDaredevil-8B-abliterated-Q4_K_M-GGUF",
    filename=MODEL_NAME,
    local_dir="./models",
)

LOG_FILE = "/tmp/agent_code_packager.log"

def log(msg: str):
    try:
        with open(LOG_FILE, "a", encoding="utf-8") as f:
            f.write(msg + "\n")
    except Exception:
        pass
    print(msg)

@spaces.GPU(duration=120)
def process_and_package(ai_response: str):
    log("=== Starting process_and_package ===")
    log(f"AI response input: {ai_response[:200]}...")

    # Initialize model & agent
    llm = Llama(
        model_path=MODEL_PATH,
        n_gpu_layers=0,
        n_batch=512,
        n_ctx=4096,
        verbose=False
    )
    provider = LlamaCppPythonProvider(llm)
    agent = LlamaCppAgent(
        provider,
        system_prompt=(
            "You are a code packager. The user will paste text that may contain multiple code blocks. "
            "Extract all code blocks, associate each with a filename if available, and return a JSON object "
            "where keys are filenames and values are the full content. "
            "ONLY return a raw JSON object — no explanations, no markdown, no text outside the JSON. "
            "Example:\n\n"
            "{\n  \"app.py\": \"import gradio as gr\\n...\",\n  \"utils.py\": \"def helper():\\n...\"\n}\n"
        ),
        predefined_messages_formatter_type=MessagesFormatterType.GEMMA_2,
        debug_output=True
    )


    # Call the agent
    # Use get_chat_response (not run)
    try:
        settings = provider.get_provider_default_settings()
        response = agent.get_chat_response(ai_response, llm_sampling_settings=settings)
    except Exception as e:
        log(f"Error in get_chat_response: {e}")
        response = ""  # fallback to empty

    log("Agent response:")
    log(response[:1000] if len(response) > 1000 else response)

    # Parse output from agent
    file_map = {}
    try:
        file_map = json.loads(response)
        log("Parsed JSON file map keys: " + ", ".join(file_map.keys()))
    except Exception as e:
        log(f"JSON parse failed: {e}")
        # fallback regex
        pattern = r"Filename:\s*(?P<fname>[\w\.\-/]+)\s*```(?:[a-zA-Z0-9]*\n)?(?P<code>[\s\S]*?)```"
        for m in re.finditer(pattern, response, re.DOTALL):
            fname = m.group("fname").strip()
            code = m.group("code").rstrip()
            file_map[fname] = code
        log("After regex fallback, keys: " + ", ".join(file_map.keys()))
        if not file_map:
            file_map["output.txt"] = response
            log("Fallback to single file: output.txt")

    # Write files & zip
    temp_dir = f"/tmp/codeproj_{uuid.uuid4().hex}"
    os.makedirs(temp_dir, exist_ok=True)

    for fname, code in file_map.items():
        dest = os.path.join(temp_dir, fname)
        os.makedirs(os.path.dirname(dest), exist_ok=True)
        with open(dest, "w", encoding="utf-8") as f:
            f.write(code)
        log(f"Wrote file: {dest}")

    zip_name = f"code_bundle_{uuid.uuid4().hex}.zip"
    zip_path = os.path.join("/tmp", zip_name)
    with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as zf:
        for root, _, files in os.walk(temp_dir):
            for fn in files:
                fullp = os.path.join(root, fn)
                rel = os.path.relpath(fullp, temp_dir)
                zf.write(fullp, arcname=rel)
                log(f"Added to zip: {rel}")

    log(f"Zip created: {zip_path}")
    log("=== Finished process_and_package ===\n")
    return zip_path

with gr.Blocks() as demo:
    gr.Markdown("### Paste AI response below; get downloadable zip of parsed files")
    inp = gr.Textbox(lines=15, label="AI response text")
    btn = gr.Button("Generate Zip")
    out = gr.File(label="Download .zip")
    btn.click(fn=process_and_package, inputs=inp, outputs=out)

demo.launch()