app.py

import os
from fastapi import FastAPI, Request
from fastapi.responses import HTMLResponse, JSONResponse
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel
from huggingface_hub import InferenceClient
import re

# Initialize FastAPI app
app = FastAPI()

# Serve static files for assets
app.mount("/static", StaticFiles(directory="static"), name="static")

# Initialize Hugging Face Inference Client
client = InferenceClient()

# Pydantic model for API input
class InfographicRequest(BaseModel):
    description: str

# Load prompt template from environment variable
PROMPT_TEMPLATE = os.getenv("PROMPT_TEMPLATE")


async def extract_code_blocks(markdown_text):
    """
    Extracts code blocks from the given Markdown text.

    Args:
        markdown_text (str): The Markdown content as a string.

    Returns:
        list: A list of code blocks extracted from the Markdown.
    """
    # Regex to match code blocks (fenced with triple backticks)
    code_block_pattern = re.compile(r'```.*?\n(.*?)```', re.DOTALL)

    # Find all code blocks
    code_blocks = code_block_pattern.findall(markdown_text)
    
    return code_blocks

# Route to serve the HTML template
@app.get("/", response_class=HTMLResponse)
async def serve_frontend():
    with open("infographic_gen.html", "r") as file:
        return HTMLResponse(content=file.read())

# Route to handle infographic generation
@app.post("/generate")
async def generate_infographic(request: InfographicRequest):
    description = request.description
    prompt = PROMPT_TEMPLATE.format(description=description)

    try:
        messages = [{"role": "user", "content": prompt}]
        stream = client.chat.completions.create(
            model="Qwen/Qwen2.5-Coder-32B-Instruct",
            messages=messages,
            temperature=0.5,
            max_tokens=1024,
            top_p=0.7,
            stream=True,
        )

        
        generated_text = ""
        for chunk in stream:
            generated_text += chunk.choices[0].delta.content
        
        code_blocks=extract_code_blocks(generated_text)
        if code_blocks:
            return JSONResponse(content={"html": code_blocks[0]})
        else:
            return JSONResponse(content={"error": "No generation"},status_code=500)

    except Exception as e:
        return JSONResponse(content={"error": str(e)}, status_code=500)