old/main_old.py

# main.py
from fastapi import FastAPI, UploadFile, File, HTTPException,Depends,Header
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
import google.generativeai as genai
from typing import List, Dict
import os
from dotenv import load_dotenv
import io
from datetime import datetime
import uuid

import json
import re

# File Format Libraries
import PyPDF2
import docx
import openpyxl
import csv
import io
import pptx
from db import get_db,Chat,ChatMessage,User,SessionLocal


from fastapi.security import OAuth2PasswordBearer
import requests
from jose import jwt

oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")

DOMAIN = "http://localhost:8000"
# Replace these with your own values from the Google Developer Console
GOOGLE_CLIENT_ID = "862058885628-e6mjev28p8e112qrp9gnn4q8mlif3bbf.apps.googleusercontent.com"
GOOGLE_CLIENT_SECRET = "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"
GOOGLE_REDIRECT_URI = "http://localhost:5173/callback"


def parse_json_from_gemini(json_str: str):
    try:
        # Remove potential leading/trailing whitespace
        json_str = json_str.strip()
        # Extract JSON content from triple backticks and "json" language specifier
        json_match = re.search(r"```json\s*(.*?)\s*```", json_str, re.DOTALL)
        if json_match:
            json_str = json_match.group(1)
        return json.loads(json_str)
    except (json.JSONDecodeError, AttributeError):
        return None

load_dotenv()

app = FastAPI(title="EduScope AI")

# Configure CORS
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


@app.get("/login/google")
async def login_google():
    return {
        "url": f"https://accounts.google.com/o/oauth2/auth?response_type=code&client_id={GOOGLE_CLIENT_ID}&redirect_uri={GOOGLE_REDIRECT_URI}&scope=openid%20profile%20email&access_type=offline"
    }

@app.get("/auth/google")
async def auth_google(code: str, db: SessionLocal = Depends(get_db)):
    token_url = "https://accounts.google.com/o/oauth2/token"
    data = {
        "code": code,
        "client_id": GOOGLE_CLIENT_ID,
        "client_secret": GOOGLE_CLIENT_SECRET,
        "redirect_uri": GOOGLE_REDIRECT_URI,
        "grant_type": "authorization_code",
    }
    response = requests.post(token_url, data=data)
    access_token = response.json().get("access_token")
    user_info = requests.get("https://www.googleapis.com/oauth2/v1/userinfo", headers={"Authorization": f"Bearer {access_token}"}).json()
    user = db.query(User).filter(User.id == user_info["id"]).first()
    if not user:
        user = User(id=user_info["id"], email=user_info["email"], name=user_info["name"])
        db.add(user)
        db.commit()

    return {"token": jwt.encode(user_info, GOOGLE_CLIENT_SECRET, algorithm="HS256")}
    # return user_info.json()


async def decode_token(authorization: str = Header(...)):
    if not authorization.startswith("Bearer "):
        raise HTTPException(
            status_code=400,
            detail="Authorization header must start with 'Bearer '"
        )
    
    token = authorization[len("Bearer "):]  # Extract token part

    try:
        # Decode and verify the JWT token
        token_data = jwt.decode(token, GOOGLE_CLIENT_SECRET, algorithms=["HS256"])
        return token_data  # Return decoded token data
    except jwt.ExpiredSignatureError:
        raise HTTPException(status_code=401, detail="Token has expired")
    except jwt.InvalidTokenError:
        raise HTTPException(status_code=401, detail="Invalid token")
    

@app.get("/token")
async def get_token(user_data: dict = Depends(decode_token)):
    return user_data


@app.post("/chats")
async def create_chat(title: str,user_data: dict = Depends(decode_token), db: SessionLocal = Depends(get_db)):
    user_id = user_data["id"]

    chat = Chat(chat_id=str(uuid.uuid4()), user_id=user_id, title=title)
    db.add(chat)
    db.commit()
    return {"chat_id": chat.chat_id, "title": title, "timestamp": chat.timestamp}


@app.get("/chats")
async def get_chats(user_data: dict = Depends(decode_token), db: SessionLocal = Depends(get_db)):
    user_id = user_data["id"]

    chats = db.query(Chat).filter(Chat.user_id == user_id).all()
    return [{"chat_id": chat.chat_id, "title": chat.title, "timestamp": chat.timestamp} for chat in chats]


genai.configure(api_key="AIzaSyDZsN3hnnNQOBLSAznFh7xWbWKNohvqff0")
model = genai.GenerativeModel('gemini-1.5-flash')

documents = {}
chat_history = []

class Document(BaseModel):
    id: str
    name: str
    content: str
    timestamp: str

class Query(BaseModel):
    text: str
    selected_docs: List[str]

class ChatMessage(BaseModel):
    id: str
    type: str  # 'user' or 'assistant'
    content: str
    timestamp: str
    referenced_docs: List[str] = []

    

class Analysis(BaseModel):
    insight: str
    pareto_analysis: dict

def extract_text_from_file(file: UploadFile):
    """

    Extract text from various file types

    Supports: PDF, DOCX, XLSX, CSV, TXT

    """
    file_extension = os.path.splitext(file.filename)[1].lower()
    content = file.file.read()
    
    try:
        if file_extension == '.pdf':
            pdf_reader = PyPDF2.PdfReader(io.BytesIO(content))
            text = "\n".join([page.extract_text() for page in pdf_reader.pages])
        
        elif file_extension == '.docx':
            doc = docx.Document(io.BytesIO(content))
            text = "\n".join([para.text for para in doc.paragraphs])
        
        elif file_extension == '.xlsx':
            wb = openpyxl.load_workbook(io.BytesIO(content), read_only=True)
            text = ""
            for sheet in wb:
                for row in sheet.iter_rows(values_only=True):
                    text += " ".join(str(cell) for cell in row if cell is not None) + "\n"
        
        elif file_extension == '.csv':
            csv_reader = csv.reader(io.StringIO(content.decode('utf-8')))
            text = "\n".join([" ".join(row) for row in csv_reader])
        
        elif file_extension == '.txt':
            text = content.decode('utf-8')

        elif file_extension in ['.ppt', '.pptx']:
            ppt = pptx.Presentation(io.BytesIO(content))
            text = ""
            for slide in ppt.slides:
                for shape in slide.shapes:
                    if hasattr(shape, "text"):
                        text += shape.text + "\n"
        
        else:
            raise ValueError(f"Unsupported file type: {file_extension}")
        
        return text
    except Exception as e:
        raise HTTPException(status_code=400, detail=f"Error processing file: {str(e)}")

@app.post("/upload")
async def upload_document(file: UploadFile = File(...)):
    try:
        text = extract_text_from_file(file)
        
        doc_id = str(uuid.uuid4())
        document = Document(
            id=doc_id,
            name=file.filename,
            content=text,
            timestamp=datetime.now().isoformat()
        )
        documents[doc_id] = document
        
        return document.dict()
    except HTTPException as e:
        raise e
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Unexpected error: {str(e)}")

@app.get("/documents")
async def get_documents():
    return list(documents.values())

@app.post("/analyze", response_model=Analysis)
async def analyze_text(query: Query):
    # try:
        # Combine content from selected documents
        combined_context = "\n\n".join([
            f"Document '{documents[doc_id].name}':\n{documents[doc_id].content}"
            for doc_id in query.selected_docs
        ])
        
        prompt = f"""

        Analyze the following text in the context of this query: {query.text}



        Context from multiple documents:

        {combined_context}



        Provide:

        1. Detailed insights and analysis, comparing information across documents when relevant

        2. Apply the Pareto Principle (80/20 rule) to identify the most important aspects



        Format the response as JSON with 'insight' and 'pareto_analysis' keys.



        Example format:

        {{

            "insight": "Key findings and analysis from the documents...",

            "pareto_analysis": {{

                "vital_few": "The 20% of factors that drive 80% of the impact...",

                "trivial_many": "The remaining 80% of factors that contribute 20% of the impact..."

            }}

        }}



        also give a complete html document with the illustrative analysis like pie charts, bar charts,graphs etc.

        """
        response = model.generate_content(prompt)
        response_text = response.text
        # print(response_text)

        
        # Create chat message
        message = ChatMessage(
            id=str(uuid.uuid4()),
            type="user",
            content=query.text,
            timestamp=datetime.now().isoformat(),
            referenced_docs=query.selected_docs
        )
        chat_history.append(message)
        
        # print(response_text)
        # Create assistant response
        # analysis = {
        #     "insight": response_text.split("Pareto Analysis:")[0].strip(),
        #     "pareto_analysis": {
        #         "vital_few": response_text.split("Vital Few (20%):")[1].split("Trivial Many")[0].strip(),
        #         "trivial_many": response_text.split("Trivial Many (80%):")[1].strip()
        #     }
        # }
        analysis = parse_json_from_gemini(response_text)
        
        assistant_message = ChatMessage(
            id=str(uuid.uuid4()),
            type="assistant",
            content=json.dumps(analysis, indent=4),
            timestamp=datetime.now().isoformat(),
            referenced_docs=query.selected_docs
        )
        chat_history.append(assistant_message)
  



        if '```html' in response_text:
            html = response_text.split('```html')[1]
            html = html.split('```')[0]
            html = html.strip()
            assistant_message = ChatMessage(
                id=str(uuid.uuid4()),
                type="assistant",
                content=html,
                timestamp=datetime.now().isoformat(),
                referenced_docs=query.selected_docs
            )
            chat_history.append(assistant_message)
        
        return analysis
    # except Exception as e:
    #     raise HTTPException(status_code=500, detail=str(e))

@app.get("/chat-history")
async def get_chat_history():
    return chat_history

@app.get("/clear-all")
async def clear_all():
    chat_history.clear()
    documents.clear()
    return {"message": "All Data cleared successfully"}




if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)