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 datetime
import json
import logging
from fastapi import FastAPI, Request, HTTPException
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
from langchain_groq import ChatGroq
from langchain_core.prompts import ChatPromptTemplate

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

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

# Initialize FastAPI app
app = FastAPI()

# Allow CORS requests from any domain (not recommended for production)
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")

# Validate environment variables
required_env_vars = ["GROQ_API_KEY", "username", "password", "security_token", "domain"]
for var in required_env_vars:
    if not os.getenv(var):
        logger.error(f"Environment variable {var} is not set")
        raise ValueError(f"Environment variable {var} is not set")

# Initialize Groq model
GROQ_API_KEY = os.getenv("CHATGROQ_API_KEY")
GROQ_MODEL = "llama3-8b-8192"  # Default to a valid Groq model
try:
    llm = ChatGroq(
        model_name=GROQ_MODEL,
        api_key=GROQ_API_KEY,
        temperature=0.1,
        max_tokens=50
    )
except Exception as e:
    logger.error(f"Failed to initialize Groq model: {e}")
    raise HTTPException(status_code=500, detail="Failed to initialize Groq model")

# Salesforce credentials
username = os.getenv("username")
password = os.getenv("password")
security_token = os.getenv("security_token")
domain = os.getenv("domain")  # e.g., 'test' for sandbox

# Initialize Salesforce connection
try:
    session_id, sf_instance = SalesforceLogin(
        username=username, password=password, security_token=security_token, domain=domain
    )
    sf = Salesforce(instance=sf_instance, session_id=session_id)
except Exception as e:
    logger.error(f"Failed to connect to Salesforce: {e}")
    raise HTTPException(status_code=500, detail="Salesforce connection failed")

# Chat history
chat_history = []
current_chat_history = []
MAX_HISTORY_SIZE = 100  # Limit chat history size

def handle_query(query):
    # Prepare context from chat history
    context_str = ""
    for past_query, response in reversed(current_chat_history[-10:]):  # Limit context to last 10 exchanges
        if past_query.strip():
            context_str += f"User asked: '{past_query}'\nBot answered: '{response}'\n"

    # Construct the prompt
    prompt_template = ChatPromptTemplate.from_messages([
        ("system", """
        You are the Clara Redfernstech chatbot. Provide accurate, professional answers in 10-15 words.
        
        Context:
        {context}
        
        Question:
        {query}
        """),
    ])
    prompt = prompt_template.format(context=context_str, query=query)

    try:
        response = llm.invoke(prompt)
        response_text = response.content.strip()
    except Exception as e:
        logger.error(f"Error querying Groq API: {e}")
        response_text = "Sorry, I couldn't find an answer."

    # Update chat history
    if len(current_chat_history) >= MAX_HISTORY_SIZE:
        current_chat_history.pop(0)  # Remove oldest entry
    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 not user_id:
        logger.error("userId is missing in history request")
        raise HTTPException(status_code=400, detail="userId is required")

    hist = ''.join([f"'{entry['sender']}: {entry['message']}'\n" for entry in history['history']])
    hist = "You are a Redfernstech summarize model. Identify user interests from this conversation: " + hist

    try:
        sf.Lead.update(user_id, {'Description': hist})
        logger.info(f"Chat history updated for user {user_id}")
    except Exception as e:
        logger.error(f"Failed to update lead: {e}")
        raise HTTPException(status_code=500, detail=f"Failed to update lead: {str(e)}")
    
    return {"summary": hist, "message": "Chat history saved"}

@app.post("/webhook")
async def receive_form_data(request: Request):
    try:
        form_data = await request.json()
    except json.JSONDecodeError:
        logger.error("Invalid JSON in webhook request")
        raise HTTPException(status_code=400, detail="Invalid JSON")

    first_name, last_name = split_name(form_data.get('name', ''))
    data = {
        'FirstName': first_name,
        'LastName': last_name,
        'Description': 'hii',  # Static description
        'Company': form_data.get('company', ''),
        'Phone': form_data.get('phone', '').strip(),
        'Email': form_data.get('email', ''),
    }

    try:
        result = sf.Lead.create(data)
        unique_id = result['id']
        logger.info(f"Lead created with ID {unique_id}")
        return JSONResponse({"id": unique_id})
    except Exception as e:
        logger.error(f"Failed to create lead: {e}")
        raise HTTPException(status_code=500, detail=f"Failed to create lead: {str(e)}")

@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()
    }
    if len(chat_history) >= MAX_HISTORY_SIZE:
        chat_history.pop(0)  # Remove oldest entry
    chat_history.append(message_data)
    logger.info(f"Chat message processed: {message}")
    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:
        return '', words[0]
    elif len(words) == 2:
        return words[0], words[1]
    else:
        return words[0], ' '.join(words[1:])