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
from fastapi import FastAPI, Request, HTTPException
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.core.base.llms.types import ChatMessage, MessageRole
from llama_index.core.llms import LLM
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from pydantic import BaseModel
from fastapi.middleware.cors import CORSMiddleware
from fastapi.templating import Jinja2Templates
from huggingface_hub import InferenceClient
import datetime
from simple_salesforce import Salesforce, SalesforceLogin

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

# Custom LLM class for Hugging Face Inference API
class HuggingFaceInferenceLLM(LLM):
    def __init__(self, model_name: str, token: str):
        super().__init__()
        self.client = InferenceClient(model=model_name, token=token)
        self.model_name = model_name

    def chat(self, messages: list[ChatMessage], **kwargs) -> str:
        prompt = ""
        for msg in messages:
            role = "user" if msg.role == MessageRole.USER else "assistant"
            prompt += f"{role}: {msg.content}\n"
        try:
            response = self.client.text_generation(
                prompt,
                max_new_tokens=512,
                temperature=0.1,
                do_sample=True,
                stop_sequences=["\n"]
            )
            return response
        except Exception as e:
            return f"Error in API call: {str(e)}"

    async def achat(self, messages: list[ChatMessage], **kwargs) -> str:
        return self.chat(messages, **kwargs)

    @property
    def metadata(self):
        return {
            "model_name": self.model_name,
            "context_window": 3000,
            "max_new_tokens": 512
        }

# Validate environment variables
required_env_vars = ["HF_TOKEN", "username", "password", "security_token", "domain"]
for var in required_env_vars:
    if not os.getenv(var):
        raise EnvironmentError(f"Missing required environment variable: {var}")

# Salesforce configuration
try:
    session_id, sf_instance = SalesforceLogin(
        username=os.getenv("username"),
        password=os.getenv("password"),
        security_token=os.getenv("security_token"),
        domain=os.getenv("domain")
    )
    sf = Salesforce(instance=sf_instance, session_id=session_id)
except Exception as e:
    raise Exception(f"Failed to initialize Salesforce: {str(e)}")

# FastAPI setup
app = FastAPI()

# Security headers middleware
@app.middleware("http")
async def add_security_headers(request: Request, call_next):
    response = await call_next(request)
    response.headers.update({
        "Content-Security-Policy": "default-src 'self'; frame-ancestors 'self';",
        "X-Frame-Options": "DENY",
        "X-Content-Type-Options": "nosniff",
        "Referrer-Policy": "strict-origin-when-cross-origin"
    })
    return response

# CORS configuration
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Specify allowed origins in production
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

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

# LlamaIndex configuration
Settings.llm = HuggingFaceInferenceLLM(
    model_name="meta-llama/Meta-Llama-3-8B-Instruct",
    token=os.getenv("HF_TOKEN")
)

Settings.embed_model = HuggingFaceEmbedding(
    model_name="BAAI/bge-small-en-v1.5"
)

# Directory constants
PERSIST_DIR = "db"
PDF_DIRECTORY = "data"

# Initialize directories
os.makedirs(PDF_DIRECTORY, exist_ok=True)
os.makedirs(PERSIST_DIR, exist_ok=True)

# Chat history storage
chat_history = []
current_chat_history = []

def data_ingestion_from_directory():
    try:
        documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data()
        storage_context = StorageContext.from_defaults()
        index = VectorStoreIndex.from_documents(documents)
        index.storage_context.persist(persist_dir=PERSIST_DIR)
    except Exception as e:
        raise Exception(f"Data ingestion failed: {str(e)}")

def initialize():
    start_time = time.time()
    data_ingestion_from_directory()
    print(f"Data ingestion completed in {time.time() - start_time:.2f} seconds")

def split_name(full_name: str) -> tuple:
    words = full_name.strip().split()
    if len(words) == 1:
        return "", words[0]
    elif len(words) == 2:
        return words[0], words[1]
    return words[0], " ".join(words[1:])

# Run initialization
initialize()

def handle_query(query: str) -> str:
    chat_text_qa_msgs = [
        (
            "user",
            """
            You are the Clara Redfernstech chatbot. Provide accurate, professional answers in 10-15 words.
            {context_str}
            Question: {query_str}
            """
        )
    ]
    text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
    
    try:
        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[-5:]):  # Limit context to last 5 interactions
            if past_query.strip():
                context_str += f"User: '{past_query}'\nBot: '{response}'\n"
        
        query_engine = index.as_query_engine(text_qa_template=text_qa_template)
        answer = query_engine.query(query)
        
        response = getattr(answer, 'response', answer.get('response', "Sorry, I couldn't find an answer."))
        current_chat_history.append((query, response))
        return response
    except Exception as e:
        return f"Error processing query: {str(e)}"

@app.get("/favicon.ico")
async def favicon():
    return HTMLResponse(status_code=204)

@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:
        raise HTTPException(status_code=400, detail="userId is required")

    try:
        hist = ''.join([f"'{entry['sender']}: {entry['message']}'\n" for entry in history['history']])
        summary = f"Conversation summary for user interest analysis:\n{hist}"
        
        sf.Lead.update(user_id, {'Description': summary})
        return {"summary": summary, "message": "Chat history saved"}
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to update lead: {str(e)}")

@app.post("/webhook")
async def receive_form_data(request: Request):
    try:
        form_data = await request.json()
        first_name, last_name = split_name(form_data.get('name', ''))
        
        data = {
            'FirstName': first_name,
            'LastName': last_name,
            'Description': 'New lead from webhook',
            'Company': form_data.get('company', 'Unknown'),
            'Phone': form_data.get('phone', '').strip(),
            'Email': form_data.get('email', ''),
        }
        result = sf.Lead.create(data)
        return JSONResponse({"id": result['id']})
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to process webhook: {str(e)}")

@app.post("/chat/")
async def chat(request: MessageRequest):
    try:
        response = handle_query(request.message)
        message_data = {
            "sender": "User",
            "message": request.message,
            "response": response,
            "timestamp": datetime.datetime.now().isoformat()
        }
        chat_history.append(message_data)
        return {"response": response}
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Chat processing failed: {str(e)}")

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