app.py

import os
import time
from fastapi import FastAPI, Request
from fastapi.responses import HTMLResponse, JSONResponse
from fastapi.staticfiles import StaticFiles
from llama_index.core import (
    StorageContext,
    load_index_from_storage,
    VectorStoreIndex,
    SimpleDirectoryReader,
    ChatPromptTemplate,
    Settings,
)
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from pydantic import BaseModel
from fastapi.middleware.cors import CORSMiddleware
from fastapi.templating import Jinja2Templates
import uuid
import datetime
import json
import re
from deep_translator import GoogleTranslator
import ollama  # Import Ollama for inference

# Define Pydantic model for incoming request body
class MessageRequest(BaseModel):
    message: str
    language: str

# Initialize FastAPI app
app = FastAPI()

@app.middleware("http")
async def add_security_headers(request: Request, call_next):
    response = await call_next(request)
    response.headers["Content-Security-Policy"] = "frame-ancestors *; frame-src *; object-src *;"
    response.headers["X-Frame-Options"] = "ALLOWALL"
    return response

# Allow CORS requests from any domain
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Static files setup
app.mount("/static", StaticFiles(directory="static"), name="static")
templates = Jinja2Templates(directory="static")

# Configure LlamaIndex settings
Settings.llm = None  # No need for Hugging Face anymore
Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")

PERSIST_DIR = "db"
PDF_DIRECTORY = "data"

# Ensure directories exist
os.makedirs(PDF_DIRECTORY, exist_ok=True)
os.makedirs(PERSIST_DIR, exist_ok=True)
chat_history = []
current_chat_history = []

def data_ingestion_from_directory():
    documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data()
    storage_context = StorageContext.from_defaults()
    index = VectorStoreIndex.from_documents(documents)
    index.storage_context.persist(persist_dir=PERSIST_DIR)

def initialize():
    start_time = time.time()
    data_ingestion_from_directory()  # Process PDF ingestion at startup
    print(f"Data ingestion time: {time.time() - start_time} seconds")

initialize()  # Run initialization tasks

def handle_query(query):
    """Handles queries using Ollama's local inference"""
    chat_text_qa_msgs = [
        (
            "user",
            """
            You are the Hotel voice chatbot, and your name is Hotel Helper. Your goal is to provide accurate, professional, and helpful answers based on hotel data. Always keep responses concise (10-15 words max).
            {context_str}
            Question:
            {query_str}
            """
        )
    ]
    text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)

    storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
    index = load_index_from_storage(storage_context)
    context_str = ""

    for past_query, response in reversed(current_chat_history):
        if past_query.strip():
            context_str += f"User asked: '{past_query}'\nBot answered: '{response}'\n"

    query_engine = index.as_query_engine(text_qa_template=text_qa_template, context_str=context_str)

    print(query)
    
    try:
        response = ollama.chat(model="llama3", messages=[{"role": "user", "content": query}])  # Using Ollama
        response_text = response.get("message", {}).get("content", "Sorry, I couldn't find an answer.")
    except Exception as e:
        print(f"Ollama Error: {e}")
        response_text = "Sorry, something went wrong with the AI model."

    current_chat_history.append((query, response_text))
    return response_text

@app.get("/ch/{id}", response_class=HTMLResponse)
async def load_chat(request: Request, id: str):
    return templates.TemplateResponse("index.html", {"request": request, "user_id": id})

@app.get("/voice/{id}", response_class=HTMLResponse)
async def load_voice_chat(request: Request, id: str):
    return templates.TemplateResponse("voice.html", {"request": request, "user_id": id})

@app.post("/chat/")
async def chat(request: MessageRequest):
    """Handles chat requests and translates responses if needed."""
    message = request.message
    language = request.language
    language_code = request.language.split('-')[0]
    
    response = handle_query(message)  # Process the message
    
    try:
        translator = GoogleTranslator(source="en", target=language_code)
        response_translated = translator.translate(response)
    except Exception as e:
        print(f"Translation error: {e}")
        response_translated = "Sorry, I couldn't translate the response."

    message_data = {
        "sender": "User",
        "message": message,
        "response": response,
        "timestamp": datetime.datetime.now().isoformat(),
    }
    chat_history.append(message_data)

    return {"response": response_translated}

@app.get("/")
def read_root(request: Request):
    return templates.TemplateResponse("home.html", {"request": request})