Update app.py

app.py

@@ -1,56 +1,249 @@
+# 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
-from fastapi.responses import HTMLResponse
+import requests
+from fastapi import FastAPI, Request
+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.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
-
+import uuid
+import datetime
+import json
 
 # 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)
 
+# Initialize FastAPI app
 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,
@@ -60,157 +253,109 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-
-
-
-@app.get("/favicon.ico")
-async def favicon():
-    return HTMLResponse("")  # or serve a real favicon if you have one
-
-
+# Mount static files
 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"
-)
+# 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")
 
-PERSIST_DIR = "db"
-PDF_DIRECTORY = 'data'
+# 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)
 
-# Ensure directories exist
-os.makedirs(PDF_DIRECTORY, exist_ok=True)
-os.makedirs(PERSIST_DIR, exist_ok=True)
+# Chat history
 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)
+    # 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.
     
-    query_engine = index.as_query_engine(text_qa_template=text_qa_template, context_str=context_str)
-    answer = query_engine.query(query)
+    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
 
-    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})
+        sf.Lead.update(user_id, {'Description': hist})
     except Exception as e:
         return {"error": f"Failed to update lead: {str(e)}"}, 500
     
-    return {"summary": result, "message": "Chat history saved"}
+    return {"summary": hist, "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
+        '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)
+    a = sf.Lead.create(data)
     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 = request.message
+    response = handle_query(message)
     message_data = {
         "sender": "User",
         "message": message,
@@ -219,7 +364,20 @@ async def chat(request: MessageRequest):
     }
     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