ds-fire-fighter/app.py

import os
from typing import Optional
from fastapi import FastAPI, HTTPException, Security, Depends
from fastapi.security import APIKeyHeader
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from dotenv import load_dotenv
from utils.document_loader import load_document
import json
from pydantic import BaseModel
from src.llm import ai_chat
from langchain_openai import ChatOpenAI
import requests
import tempfile
from scripts.generate_pdf  import create_pdf
from scripts.generate_theme import generate_theme
from scripts.generate_quiz import generate_quiz
from typing import Dict, Any
from fastapi.responses import Response
from datetime import datetime
from fastapi import HTTPException
from pydantic import BaseModel
from typing import Optional, Union, Dict, Any,List,Optional, List, Tuple, Any
import os
import requests
import os
from PyPDF2 import PdfReader
from config import QUIZ_TYPES
# Load environment variables
load_dotenv()
API_KEY = os.getenv("API_KEY_ACCESS")

base_path = os.path.join("data", "config_files")
QUESTIONS_PATH = os.path.join(base_path, "questions.json")
THEME_CONTEXT_PATH = os.path.join(base_path, "theme_context.json")



with open(THEME_CONTEXT_PATH, "r", encoding="utf-8") as f:
    themes = json.load(f)
# Initialize FastAPI app
app = FastAPI(
    title="Fire Fighter Interview API",
    description="API For fire fighter",
    version="1.0.0"
)

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

# Setup API key authentication
api_key_header = APIKeyHeader(name="Authorization", auto_error=False)


async def get_api_key(api_key_header: str = Security(api_key_header)) -> str:
    """Validate API key from header"""
    if not api_key_header or not api_key_header.startswith('Bearer '):
        raise HTTPException(
            status_code=401,
            detail={"error": "Unauthorized", "message": "API key is missing or invalid."}
        )
    
    token = api_key_header.split(' ')[1]
    if token != API_KEY:
        raise HTTPException(
            status_code=401,
            detail={"error": "Unauthorized", "message": "API key does not match."}
        )
    
    return token

class ChatRequest(BaseModel):
    resume_url: Optional[str] = None
    query: str=None
    chat_id: int
    theme_id: Optional[int] = 1
    full_history_url: Optional[str] = None
    form_id:Optional[int] = None
    feedback: Optional[str] = None
    generate_theme:str="NO"

class ChatResponse(BaseModel):
    message: str
    end: bool
    pop_theme_generation:bool
    error: Optional[str] = None



class GeneratePDFRequest(BaseModel):
    resume_url: Optional[str] = None
    chat_id: int
    theme_id: Optional[int] = 1
    full_history_url: Optional[str] = None
    form_id:Optional[int] = None
    generate_theme:str="YES"

class QuizRequest(BaseModel):
    pdf_url: str
    quiz_type: int  # 1, 2, or 3 corresponding to QUIZ_TYPES

class QuizResponse(BaseModel):
    success: bool
    message: str
    quiz_data: Optional[List[Any]] = None
    error: Optional[str] = None

async def extract_pdf_text(pdf_url: str) -> Union[str, None]:
    """Extract text from PDF and handle potential errors."""
    try:
        response = requests.get(pdf_url)
        response.raise_for_status()
        
        # Create a temporary file
        with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as temp_pdf:
            temp_pdf.write(response.content)
            temp_path = temp_pdf.name

        # Extract text from PDF
        reader = PdfReader(temp_path)
        text = "\n\n".join(
            page.extract_text() for page in reader.pages if page.extract_text()
        )

        # Clean up temporary file
        os.unlink(temp_path)

        if not text.strip():
            return None
        return text

    except requests.RequestException as e:
        raise HTTPException(
            status_code=400,
            detail=f"Error downloading PDF: {str(e)}"
        )
    except Exception as e:
        raise HTTPException(
            status_code=500,
            detail=f"Error processing PDF: {str(e)}"
        )
    
@app.post("/rescue-career/chat")
async def chat_endpoint(
    request: ChatRequest,
    api_key: str = Depends(get_api_key)
):
    try:
        # Validate theme
        print(f"Received request with theme_id: {request.theme_id}")  # Debugging print
        matching_themes = [t for t in themes if t["id"] == request.theme_id]
        if not matching_themes:
            print(f"No theme found with ID: {request.theme_id}")  # Debugging print
            raise HTTPException(
                status_code=400,
                detail=f"No theme found with ID {request.theme_id}"
            )
        print(f"Validated theme ID: {request.theme_id}")  # Print statement added
        
        resume_docs = ""
        if request.resume_url:
            print(f"Loading resume from URL: {request.resume_url}")  # Debugging print
            docs = load_document(request.resume_url)
            if not docs:
                print("Invalid resume URL: Unable to fetch document")  # Debugging print
                raise HTTPException(
                    status_code=400, 
                    detail="Invalid resume URL: Unable to fetch document"
                )
            resume_docs = "\n".join(f"- {doc.page_content}" for doc in docs)
            print(f"Loaded resume documents: {resume_docs}")  # Debugging print

        full_history_docs = ""
        if request.full_history_url:
            print(f"Loading full history from URL: {request.full_history_url}")  # Debugging print
            docs = load_document(request.full_history_url)
            if not docs:
                print("Invalid full history URL: Unable to fetch document")  # Debugging print
                raise HTTPException(
                    status_code=400, 
                    detail="Invalid full history URL: Unable to fetch document"
                )
            full_history_docs = "\n".join(f"- {doc.page_content}" for doc in docs)
            print(f"Loaded full history documents: {full_history_docs}")  # Debugging print

        form_response_docs = ""
        if request.form_id:
            print(f"Fetching form response for form_id: {request.form_id}")  # Debugging print
            try:
                x_api_key = os.getenv("BACKEND_XAPI_KEY")
                url = f"{os.getenv('BACKEND_BASE_URL')}/v3/api/custom/theme-document/answer/{request.form_id}?x-project={x_api_key}"
                result = requests.get(url)
                result.raise_for_status()  # Ensure we raise an error for bad responses
                form_response = result.json()["data"]  # Return response in JSON format
                form_response_docs = "\n".join(f"- {form_response}")
                
                print(f"Fetched form response: {form_response}")  # Debugging print
            except requests.RequestException as e:
                print(f"Error fetching onboarding data: {str(e)}")  # Debugging print
                raise HTTPException(
                    status_code=400, 
                    detail="Unable to fetch onboarding data"
                )
        # Parse response
        print("Parsing AI response...")  # Debugging print
        query = request.query
        if not query:
            query = "Let's get started"
        response = ai_chat(
                query=query,
                conversation_id=request.chat_id,
                theme_id=request.theme_id,
                resume=resume_docs,
                full_history=full_history_docs,
                form_response=form_response_docs
            )
        print(response)
        
        return ChatResponse(
            message=response.get("message", ""),
            end=response.get("end", "no") == "yes",
            pop_theme_generation=response.get("pop_theme_generation","no") == "yes",
            error=None
        )
        
    except Exception as e:
        print(f"Error processing chat request: {str(e)}")  # Print statement added
        raise HTTPException(
            status_code=500,
            detail=f"Error processing chat request: {str(e)}"
        )



@app.post("/rescue-career/generate-theme")
async def generate_pdf_endpoint(
    request: GeneratePDFRequest,
    api_key: str = Depends(get_api_key)
):
    
    try:

        print(f"Received request with theme_id: {request.theme_id}")  # Debugging print
        matching_themes = [t for t in themes if t["id"] == request.theme_id]
        if not matching_themes:
            print(f"No theme found with ID: {request.theme_id}")  # Debugging print
            raise HTTPException(
                status_code=400,
                detail=f"No theme found with ID {request.theme_id}"
            )
        print(f"Validated theme ID: {request.theme_id}")  # Print statement added
        
        resume_docs = ""
        if request.resume_url:
            print(f"Loading resume from URL: {request.resume_url}")  # Debugging print
            docs = load_document(request.resume_url)
            if not docs:
                print("Invalid resume URL: Unable to fetch document")  # Debugging print
                raise HTTPException(
                    status_code=400, 
                    detail="Invalid resume URL: Unable to fetch document"
                )
            resume_docs = "\n".join(f"- {doc.page_content}" for doc in docs)
            print(f"Loaded resume documents: {resume_docs}")  # Debugging print

        full_history_docs = ""
        if request.full_history_url:
            print(f"Loading full history from URL: {request.full_history_url}")  # Debugging print
            docs = load_document(request.full_history_url)
            if not docs:
                print("Invalid full history URL: Unable to fetch document")  # Debugging print
                raise HTTPException(
                    status_code=400, 
                    detail="Invalid full history URL: Unable to fetch document"
                )
            full_history_docs = "\n".join(f"- {doc.page_content}" for doc in docs)
            print(f"Loaded full history documents: {full_history_docs}")  # Debugging print

        form_response_docs = ""
        if request.form_id:
            print(f"Fetching form response for form_id: {request.form_id}")  # Debugging print
            try:
                x_api_key = os.getenv("BACKEND_XAPI_KEY")
                url = f"{os.getenv('BACKEND_BASE_URL')}/v3/api/custom/theme-document/answer/{request.form_id}?x-project={x_api_key}"
                result = requests.get(url)
                result.raise_for_status()  # Ensure we raise an error for bad responses
                form_response = result.json()["data"]  # Return response in JSON format
                form_response_docs = "\n".join(f"- {form_response}")
                
                print(f"Fetched form response: {form_response}")  # Debugging print
            except requests.RequestException as e:
                print(f"Error fetching onboarding data: {str(e)}")  # Debugging print
                raise HTTPException(
                    status_code=400, 
                    detail="Unable to fetch onboarding data"
                )
        # Here you would fetch the conversation data using the conversation_id
        # This is a placeholder - replace with your actual conversation data fetching logic
         # Get AI-generated theme content
        # Get AI-generated theme content
        response = ai_chat(
            query="NOW GENERATE THE STARTPOP FRAMEWORK",
            conversation_id=request.chat_id,
            theme_id=request.theme_id,
            resume=resume_docs,
            full_history=full_history_docs,
            form_response=form_response_docs,
            generate_theme="YES"
        )
        print(f"AI Response for theme: {response}")
        
        # Ensure AI response is valid
        if not isinstance(response, str):
            raise HTTPException(status_code=500, detail="Invalid AI response format")
        
        # Generate PDF
        response_data = json.loads(response)
        pdf_content = create_pdf(response_data)
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        file_path = f"theme_{timestamp}.pdf"
        
        # Save the PDF locally temporarily
        with open(file_path, "wb") as file:
            file.write(pdf_content)
        
        # Upload the PDF to S3 using the API
        upload_url = f"{os.getenv('BACKEND_BASE_URL')}/v3/api/custom/theme/doc-upload?x-project={x_api_key}"
        with open(file_path, 'rb') as file:
            files = {'file': file}
            upload_response = requests.post(upload_url, files=files)
        
        # Check if the upload was successful
        if upload_response.status_code != 200:
            raise HTTPException(status_code=upload_response.status_code, detail="File upload to S3 failed: " + upload_response.text)
        
        upload_data = upload_response.json()  # Get the response in JSON format
        
        # Extract the uploaded file URL
        theme_url = upload_data.get("url")  # Adjust this key based on the actual API response structure
        if not theme_url:
            raise HTTPException(status_code=500, detail="Failed to retrieve theme URL from upload response")
        
        # Clean up the temporary file
        os.remove(file_path)
        
        # Return JSON response with theme URL and text
        return {
            "theme_url": theme_url,
            "theme_text": response_data
        }
    except Exception as e:
        print(f"Error generating theme: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Error: {str(e)}")

        
@app.post("/rescue-career/generate-quiz", response_model=QuizResponse)
async def generate_quiz_endpoint(
    request: QuizRequest,
    api_key: str = Depends(get_api_key)
):
    """Generate quiz based on PDF content and quiz type."""
    # Validate quiz type
    if request.quiz_type not in QUIZ_TYPES:
        raise HTTPException(
            status_code=400,
            detail=f"Invalid quiz type. Must be one of: {list(QUIZ_TYPES)}"
        )

    try:
        # Extract text from PDF
        pdf_text = await extract_pdf_text(request.pdf_url)
        if not pdf_text:
            return QuizResponse(
                success=False,
                message="PDF extraction completed but no text content found",
                error="Empty PDF content"
            )
            
        # Generate quiz using the existing function
        quiz_data = generate_quiz(
            startpop_pdf=pdf_text,
            quiz_type=request.quiz_type
        )

        if not quiz_data:
            return QuizResponse(
                success=False,
                message="Quiz generation failed",
                error="Unable to generate quiz from the provided content"
            )

        return QuizResponse(
            success=True,
            message="Quiz generated successfully",
            quiz_data=quiz_data
        )

    except HTTPException as he:
        raise he
    except Exception as e:
        raise HTTPException(
            status_code=500,
            detail=f"Unexpected error during quiz generation: {str(e)}"
        )
        
        
        
async def get_conversation_data(conversation_id: str) -> dict:
    """
    Fetch conversation data using the conversation ID.
    Replace this with your actual implementation to fetch conversation data.
    """
    try:
        storage_path = "conversations.json"
        with open(storage_path, 'r') as f:
                convs =  json.load(f)
        convs_id = convs[conversation_id]
        return convs_id
    except Exception as e:
        print(f"Error fetching conversation data: {e}")
        return None
    
    
@app.on_event("startup")
async def startup_event():
    """Initialize required components on startup"""
    pass

if __name__ == "__main__":
    import uvicorn
    uvicorn.run("app:app", host="0.0.0.0", port=5048, reload=True)