main.py

from fastapi import FastAPI, UploadFile, File, Form
from fastapi.responses import JSONResponse
from pydantic import BaseModel
import os
import io
from dotenv import load_dotenv
from PIL import Image
import pytesseract
import whisper
import requests
from bs4 import BeautifulSoup
from docx import Document
import pandas as pd
import PyPDF2
from groq import Groq

# Load environment variables
load_dotenv()
pytesseract.pytesseract.tesseract_cmd = os.getenv("TESSERACT_CMD", "/usr/bin/tesseract")
ffmpeg_path = os.getenv("FFMPEG_PATH", "/usr/bin")
os.environ["PATH"] += os.pathsep + ffmpeg_path

app = FastAPI()
client = Groq(api_key=os.getenv("GROQ_API_KEY"))

UPLOAD_DIR = "uploaded_files"
os.makedirs(UPLOAD_DIR, exist_ok=True)
MAX_FILE_SIZE_MB = 10

# ========== File Text Extraction ==========
def extract_text_from_file(file_path):
    ext = os.path.splitext(file_path)[-1].lower()
    if ext == ".txt":
        with open(file_path, "r", encoding="utf-8") as f:
            return f.read()
    elif ext == ".docx":
        doc = Document(file_path)
        return "\n".join([para.text for para in doc.paragraphs])
    elif ext == ".csv":
        df = pd.read_csv(file_path)
        return df.to_string(index=False)
    elif ext == ".pdf":
        with open(file_path, "rb") as f:
            reader = PyPDF2.PdfReader(f)
            return "\n".join([page.extract_text() for page in reader.pages if page.extract_text()])
    else:
        return "❌ Unsupported file type."

# ========== Chat with File ==========
@app.post("/chat-with-file")
async def chat_with_file(file: UploadFile = File(...), question: str = Form(...)):
    try:
        contents = await file.read()
        if len(contents) > MAX_FILE_SIZE_MB * 1024 * 1024:
            return JSONResponse(status_code=400, content={"error": "❌ File too large. Max size is 10MB."})

        file_path = os.path.join(UPLOAD_DIR, file.filename)
        with open(file_path, "wb") as f:
            f.write(contents)

        file_content = extract_text_from_file(file_path)

        response = client.chat.completions.create(
            model="llama3-8b-8192",
            messages=[
                {"role": "system", "content": "You are a helpful assistant. Use the uploaded file content to answer questions."},
                {"role": "user", "content": f"{file_content}\n\nQuestion: {question}"}
            ]
        )
        return {"answer": response.choices[0].message.content}
    except Exception as e:
        return JSONResponse(status_code=500, content={"error": str(e)})

# ========== Chat with URL ==========
class URLQuery(BaseModel):
    url: str
    question: str

@app.post("/chat-with-url")
async def chat_with_url(data: URLQuery):
    try:
        headers = {"User-Agent": "Mozilla/5.0"}
        res = requests.get(data.url, headers=headers, timeout=10)
        soup = BeautifulSoup(res.text, "html.parser")
        web_content = soup.get_text(separator="\n")
        trimmed_content = web_content[:8000]  # limit for Groq

        response = client.chat.completions.create(
            model="llama3-8b-8192",
            messages=[
                {"role": "system", "content": "You are a helpful assistant. Use the website content to answer the user's question."},
                {"role": "user", "content": f"{trimmed_content}\n\nNow answer this question:\n{data.question}"}
            ]
        )
        return {"answer": response.choices[0].message.content}
    except Exception as e:
        return JSONResponse(status_code=500, content={"error": str(e)})

# ========== Extract Text from Image ==========
@app.post("/extract-text-from-image")
async def extract_text_from_image(file: UploadFile = File(...)):
    try:
        contents = await file.read()
        image = Image.open(io.BytesIO(contents)).convert("RGB")
        text = pytesseract.image_to_string(image)
        return {"answer": text.strip() or "⚠️ No text extracted."}
    except Exception as e:
        return JSONResponse(status_code=500, content={"error": str(e)})

# ========== Transcribe Audio ==========
@app.post("/transcribe-audio")
async def transcribe_audio(file: UploadFile = File(...)):
    try:
        contents = await file.read()
        audio_path = os.path.join(UPLOAD_DIR, file.filename)
        with open(audio_path, "wb") as f:
            f.write(contents)

        model = whisper.load_model("base")
        result = model.transcribe(audio_path)
        return {"answer": result["text"] if result.get("text") else "⚠️ No transcript returned."}
    except Exception as e:
        return JSONResponse(status_code=500, content={"error": str(e)})