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 uvicorn

# 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")

# 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:
        # Query Hugging Face model
        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,
        )

        # Collect the HTML content from the stream
        generated_html = ""
        for chunk in stream:
            generated_html += chunk.choices[0].delta.content

        # Return the generated HTML content
        return JSONResponse(content={"html": generated_html})

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