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
from groq import Groq

# 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()

client = Groq()

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

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

async def generate_infographic(infoRequest):
    prompt = PROMPT_TEMPLATE.format(description=description)
    generated_completion = client.chat.completions.create(
            model="llama-3.1-70b-versatile",
            messages=[
                {"role": "system", "content": SYSTEM_INSTRUCT},
                {"role": "user", "content": prompt}
            ],
            temperature=0.5,
            max_tokens=5000,
            top_p=1,
            stream=False,
            stop=None
        )
    generated_text = generated_completion.choices[0].message.content
    code_blocks= await extract_code_blocks(generated_text)
    return code_blocks

# Route to serve the HTML template
@app.get("/", response_class=HTMLResponse)
async def serve_frontend():
    return HTMLResponse(open("static/infographic_gen.html").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=0.4,
        #     max_tokens=6000,
        #     top_p=0.7,
        #     stream=True,
        # )

        
        # generated_text = ""
        # for chunk in stream:
        #     generated_text += chunk.choices[0].delta.content

        # print(generated_text)
        #code_blocks= await extract_code_blocks(generated_text)
        code_blocks=generate_infographic(description)
        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)