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.llms.huggingface import HuggingFaceInferenceAPI 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 # 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"), ) client = Client("Be-Bo/llama-3-chatbot_70b") 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 # Log in to Salesforce 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 = HuggingFaceInferenceAPI( 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 = client.predict( message=hist, api_name="/chat" ) # Update the lead's description with the summary result sf.Lead.update(user_id, {'Description': result}) return {"summary": result, "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) # 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"}