main.py

from fastapi import FastAPI, Request, HTTPException
from fastapi.responses import PlainTextResponse
from fastapi.middleware.cors import CORSMiddleware
from twilio.twiml.messaging_response import MessagingResponse
import os,openai
import google.generativeai as genai
from llama_index.llms import OpenAI
from llama_index import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms import OpenAI
from llama_index import StorageContext, load_index_from_storage

secret = os.environ["key"]

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

import user_guide_sync

global index,query_engine
query_engine = index = None
#query_engine = (user_guide_sync.update_user_guide).as_query_engine()

app = FastAPI()

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

@app.post("/update_user_guide_data")
async def update_user_guide_data():
    user_guide_sync.update_user_guide()
    return "guide updated"



index = None

@app.post("/whatsapp")
async def reply_whatsapp(request: Request):
    
    form_data = await request.form()
    num_media = int(form_data.get("NumMedia", 0))
    from_number = form_data.get("From")
    message_body = form_data.get("Body")
    user_query = message_body

    response = MessagingResponse()
    #msg.media(GOOD_BOY_URL)
    try:
        openai.api_key = os.environ["OPENAI_API_KEY"]

        # Define the messages for the conversation
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": f"always translate to english(only): '{user_query}'   give only translated part only without any extra details"  }
        ]
        # Make the API call
        gptresponse = openai.chat.completions.create(
            model="gpt-4o",
            messages=messages
        )

        user_query = gptresponse.choices[0].message.content

        print(f"translated text : {user_query}")


        
        global index  # Use the global index variable
        if index is None:  # Check if the index is already loaded
                from llama_index import StorageContext, load_index_from_storage  # Import necessary modules
                storage_context = StorageContext.from_defaults(persist_dir="llama_index")
                index = load_index_from_storage(storage_context=storage_context)
                print("Index loaded")
        else:
                print("Index already loaded")
        # Set up a retriever to fetch similar documents directly without full query processing
        retriever = index.as_retriever()
        # Retrieve the top similar documents based on the user query
        similar_docs = retriever.retrieve(user_query)  # Adjust `top_k` as needed
        # Prepare the context for LLM by concatenating the content of similar documents
        context = "\n\n".join([doc.node.text for doc in similar_docs])

        prompt = f"""
        context : {context}
        
        user query : {user_query}
        
                Instructions:
                - First, understand the user question carefully.
                - If you find the correct answer from the provided data, respond with detailed steps (1, 2, ...) and always include a more details link.
                - If the correct answer is not found in the provided data or proide the correct solution to user using data then output is only this : "contact our help desk". dont add extra anything
         """
        messages = [
            {"role": "user", "content": prompt  }
        ]
        
        # Make the API call
        gptresponse = openai.chat.completions.create(
            model="gpt-4o-mini",
            messages=messages
        )
        gpt_response = gptresponse.choices[0].message.content
        
        print(str(gpt_response).lower())
        if "contact our help desk" in str(gpt_response).lower() or "our help desk" in str(gpt_response).lower():
                print("help desk option")
    
                
                prompt = f"""
                         system:
                         you are parallax technologies chatbot design for answer the user question like a real human.
                         contact details suppor team link : https://projects.storemate.lk/customer  Email : [email protected] Youtube : https://www.youtube.com/channel/UCFkX9Fa-Qe6Qi4V5f0RcfSA Facebook : https://www.facebook.com/storemateinventory web link : https://storemate.lk
                         
                         only give single answer and don't give answer for general answers(this is CRM system for only pos system clients)
                         note : don't give any steps for solve the issues but give steps for sytem slow and performance related questions
                         user:{user_query}
                         """
                messages = [
                    {"role": "system", "content": "you are parallax technologies chatbot design for answer the user question like a real human"},
                    {"role": "user", "content": prompt}
                ]

                #gen_response = model.generate_content(prompt)
            
                gpt_response = openai.chat.completions.create(
                
                model="gpt-4o-mini",
                
                messages=messages,
                
                temperature=0,
                
                )
                
                response.message(str(gpt_response.choices[0].message.content))
                #response.message(gen_response.text)
                #response.message(gen_response.text +"\n\n"+default)
                return PlainTextResponse(str(response), media_type="application/xml")

        response.message(str(gpt_response)) 
        #response.message("https://storemate.lk")
        return PlainTextResponse(str(response), media_type="application/xml")   
    except Exception as e:
        print(str(e))
        response.message("please ask again...!")
        return PlainTextResponse(str(response), media_type="application/xml")  
    

# Run the application (Make sure you have the necessary setup to run FastAPI)