app.py

# import os
# import time
# from fastapi import FastAPI,Request
# from fastapi.responses import HTMLResponse
# 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.responses import JSONResponse
# import uuid  # for generating unique IDs
# import datetime
# from fastapi.middleware.cors import CORSMiddleware
# from fastapi.templating import Jinja2Templates
# from huggingface_hub import InferenceClient
# import json
# import re
# from gradio_client import Client
# from simple_salesforce import Salesforce, SalesforceLogin
# from llama_index.llms.huggingface import HuggingFaceLLM
# # from llama_index.llms.huggingface import HuggingFaceInferenceAPI


# # Define Pydantic model for incoming request body
# class MessageRequest(BaseModel):
#     message: str
# repo_id = "meta-llama/Meta-Llama-3-8B-Instruct"
# llm_client = InferenceClient(
#     model=repo_id,
#     token=os.getenv("HF_TOKEN"),
# )


# os.environ["HF_TOKEN"] = os.getenv("HF_TOKEN")
# username = os.getenv("username")
# password = os.getenv("password")
# security_token = os.getenv("security_token")
# domain =  os.getenv("domain")# Using sandbox environment
# session_id, sf_instance = SalesforceLogin(username=username, password=password, security_token=security_token, domain=domain)

#     # Create Salesforce object
# sf = Salesforce(instance=sf_instance, session_id=session_id)

# 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=["*"],
# )




# @app.get("/favicon.ico")
# async def favicon():
#     return HTMLResponse("")  # or serve a real favicon if you have one


# app.mount("/static", StaticFiles(directory="static"), name="static")

# templates = Jinja2Templates(directory="static")
# # Configure Llama index settings
# Settings.llm = HuggingFaceLLM(
#     model_name="meta-llama/Meta-Llama-3-8B-Instruct",
#     tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct",
#     context_window=3000,
#     token=os.getenv("HF_TOKEN"),
#     max_new_tokens=512,
#     generate_kwargs={"temperature": 0.1},
# )

# 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")
# def split_name(full_name):
#     # Split the name by spaces
#     words = full_name.strip().split()
    
#     # Logic for determining first name and last name
#     if len(words) == 1:
#         first_name = ''
#         last_name = words[0]
#     elif len(words) == 2:
#         first_name = words[0]
#         last_name = words[1]
#     else:
#         first_name = words[0]
#         last_name = ' '.join(words[1:])
    
#     return first_name, last_name

# initialize()  # Run initialization tasks


# def handle_query(query):
#     chat_text_qa_msgs = [
#         (
#             "user",
#             """
#             You are the Clara Redfernstech chatbot. Your goal is to provide accurate, professional, and helpful answers to user queries based on the company's data. Always ensure your responses are clear and concise. Give response within 10-15 words only       
#             {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)
#     answer = query_engine.query(query)

#     if hasattr(answer, 'response'):
#         response=answer.response
#     elif isinstance(answer, dict) and 'response' in answer:
#         response =answer['response']
#     else:
#         response ="Sorry, I couldn't find an answer."
#     current_chat_history.append((query, response))
#     return response
# @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})
# # Route to save chat history
# @app.post("/hist/")
# async def save_chat_history(history: dict):
#     # Check if 'userId' is present in the incoming dictionary
#     user_id = history.get('userId')
#     print(user_id)

#     # Ensure user_id is defined before proceeding
#     if user_id is None:
#         return {"error": "userId is required"}, 400

#     # Construct the chat history string
#     hist = ''.join([f"'{entry['sender']}: {entry['message']}'\n" for entry in history['history']])
#     hist = "You are a Redfernstech summarize model. Your aim is to use this conversation to identify user interests solely based on that conversation: " + hist
#     print(hist)

#     # Get the summarized result from the client model
#     result = hist

#     try:
#         sf.Lead.update(user_id, {'Description': result})
#     except Exception as e:
#         return {"error": f"Failed to update lead: {str(e)}"}, 500
    
#     return {"summary": result, "message": "Chat history saved"}
# @app.post("/webhook")
# async def receive_form_data(request: Request):
#     form_data = await request.json()
#     # Log in to Salesforce
#     first_name, last_name = split_name(form_data['name'])
#     data = {
#     'FirstName': first_name,
#     'LastName': last_name,
#     'Description': 'hii',  # Static description
#     'Company': form_data['company'],  # Assuming company is available in form_data
#     'Phone': form_data['phone'].strip(),  # Phone from form data
#     'Email': form_data['email'],  # Email from form data
#     }
#     a=sf.Lead.create(data)
#     # Generate a unique ID (for tracking user)
#     unique_id = a['id']
    
#     # Here you can do something with form_data like saving it to a database
#     print("Received form data:", form_data)
    
#     # Send back the unique id to the frontend
#     return JSONResponse({"id": unique_id})

# @app.post("/chat/")
# async def chat(request: MessageRequest):
#     message = request.message  # Access the message from the request body
#     response = handle_query(message)  # Process the message
#     message_data = {
#         "sender": "User",
#         "message": message,
#         "response": response,
#         "timestamp": datetime.datetime.now().isoformat()
#     }
#     chat_history.append(message_data)
#     return {"response": response}
# @app.get("/")
# def read_root():
#     return {"message": "Welcome to the API"}

import os
import time
import requests
from fastapi import FastAPI, Request
from fastapi.responses import HTMLResponse, JSONResponse
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel
from fastapi.middleware.cors import CORSMiddleware
from fastapi.templating import Jinja2Templates
from simple_salesforce import Salesforce, SalesforceLogin
import uuid
import datetime
import json

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

# Initialize FastAPI app
app = FastAPI()

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

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

# Configure ChatGroq API
CHATGROQ_API_URL = "https://api.chatgroq.com/v1/chat/completions"  # Replace with actual endpoint
CHATGROQ_API_KEY = os.getenv("CHATGROQ_API_KEY")

# Salesforce credentials
username = os.getenv("username")
password = os.getenv("password")
security_token = os.getenv("security_token")
domain = os.getenv("domain")  # Using sandbox environment
session_id, sf_instance = SalesforceLogin(username=username, password=password, security_token=security_token, domain=domain)
sf = Salesforce(instance=sf_instance, session_id=session_id)

# Chat history
chat_history = []
current_chat_history = []

def handle_query(query):
    # Prepare context from chat history
    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"

    # Construct the prompt for ChatGroq
    prompt = f"""
    You are the Clara Redfernstech chatbot. Your goal is to provide accurate, professional, and helpful answers to user queries based on the company's data. Always ensure your responses are clear and concise. Give response within 10-15 words only.
    
    Context:
    {context_str}
    
    Question:
    {query}
    """

    # Send request to ChatGroq API
    headers = {
        "Authorization": f"Bearer {CHATGROQ_API_KEY}",
        "Content-Type": "application/json",
    }
    payload = {
        "model": "meta-llama/llama-4-maverick-17b-128e-instruct",  # Replace with the actual model name
        "messages": [{"role": "user", "content": prompt}],
        "max_tokens": 50,  # Adjust as needed
        "temperature": 0.1,
    }

    try:
        response = requests.post(CHATGROQ_API_URL, headers=headers, json=payload)
        response_data = response.json()
        response_text = response_data["choices"][0]["message"]["content"].strip()
    except Exception as e:
        print(f"Error querying ChatGroq: {e}")
        response_text = "Sorry, I couldn't find an answer."

    # Update chat history
    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.post("/hist/")
async def save_chat_history(history: dict):
    user_id = history.get('userId')
    if user_id is None:
        return {"error": "userId is required"}, 400

    hist = ''.join([f"'{entry['sender']}: {entry['message']}'\n" for entry in history['history']])
    hist = "You are a Redfernstech summarize model. Your aim is to use this conversation to identify user interests solely based on that conversation: " + hist

    try:
        sf.Lead.update(user_id, {'Description': hist})
    except Exception as e:
        return {"error": f"Failed to update lead: {str(e)}"}, 500
    
    return {"summary": hist, "message": "Chat history saved"}

@app.post("/webhook")
async def receive_form_data(request: Request):
    form_data = await request.json()
    first_name, last_name = split_name(form_data['name'])
    data = {
        'FirstName': first_name,
        'LastName': last_name,
        'Description': 'hii',  # Static description
        'Company': form_data['company'],  # Assuming company is available in form_data
        'Phone': form_data['phone'].strip(),  # Phone from form data
        'Email': form_data['email'],  # Email from form data
    }
    a = sf.Lead.create(data)
    unique_id = a['id']
    print("Received form data:", form_data)
    return JSONResponse({"id": unique_id})

@app.post("/chat/")
async def chat(request: MessageRequest):
    message = request.message
    response = handle_query(message)
    message_data = {
        "sender": "User",
        "message": message,
        "response": response,
        "timestamp": datetime.datetime.now().isoformat()
    }
    chat_history.append(message_data)
    return {"response": response}

@app.get("/")
def read_root():
    return {"message": "Welcome to the API"}

def split_name(full_name):
    words = full_name.strip().split()
    if len(words) == 1:
        first_name = ''
        last_name = words[0]
    elif len(words) == 2:
        first_name = words[0]
        last_name = words[1]
    else:
        first_name = words[0]
        last_name = ' '.join(words[1:])
    return first_name, last_name