import os
import google.generativeai as genai
from playwright.async_api import async_playwright
from dotenv import load_dotenv
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import uvicorn
import asyncio
import json

# Load environment variables
load_dotenv()

# Configure Google Generative AI API key
genai.configure(api_key=os.environ["API_KEY"])

# FastAPI app initialization
app = FastAPI()

# Function to scrape webpage and extract visible text
async def scrape_visible_text(url):
    async with async_playwright() as p:
        browser = await p.chromium.launch(headless=True)  # Launch browser in headless mode
        context = await browser.new_context(
            user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/129.0.0.0 Safari/537.36",
            viewport={"width": 1280, "height": 800},
            extra_http_headers={
                "accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7",
                "accept-encoding": "gzip, deflate, br, zstd",
                "accept-language": "en-US,en;q=0.9,hi;q=0.8",
                "cache-control": "max-age=0",
                "sec-ch-ua": '"Google Chrome";v="129", "Not=A?Brand";v="8", "Chromium";v="129"',
                "sec-ch-ua-mobile": "?0",
                "sec-ch-ua-platform": '"Windows"',
                "sec-fetch-dest": "document",
                "sec-fetch-mode": "navigate",
                "sec-fetch-site": "none",
                "sec-fetch-user": "?1",
                "upgrade-insecure-requests": "1"
            }
        )
        page = await context.new_page()
        await page.goto(url, wait_until="networkidle")
        visible_text = await page.evaluate("document.body.innerText")
        await browser.close()
        return visible_text

# Function to structure data using Google's Gemini model
def structure_data(text, college_name):
    prompt = f"Convert the following unstructured text into a well-written and comprehensive structured form with titles and content containing all relevant data. The response should be a detailed paragraph mentioning everything about the college named '{college_name}', ensuring no important information is missed. Include details such as connectivity, placement, nearby colleges, infrastructure, courses, branches, students, festivals, clubs, reviews, Q&A, and any other college-related parameters available in the text. Provide the response text with no fromatting!\n{text}"
    model = genai.GenerativeModel("gemini-1.5-pro")
    response = model.generate_content(prompt)
    return response.text.strip()

# Pydantic model for request body
class URLRequest(BaseModel):
    url: str
    college_name: str

# FastAPI endpoint to scrape and structure data
@app.post("/scrape")
async def scrape_and_structure_data(request: URLRequest):
    try:
        # Scrape visible text from the webpage
        visible_text = await scrape_visible_text(request.url)
        
        # Structure the data using Google's Gemini model
        structured_data = structure_data(visible_text, request.college_name)
        
        # Return the structured data
        return {"structured_data": structured_data}
    except Exception as e:
        print(f"Error occurred while processing the request: {e}")
        raise HTTPException(status_code=500, detail=str(e))

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)