import os import json import sqlite3 from datetime import datetime import streamlit as st from langchain_huggingface import HuggingFaceEmbeddings from langchain_chroma import Chroma from langchain_groq import ChatGroq from langchain.memory import ConversationBufferMemory from langchain.chains import ConversationalRetrievalChain from vectorize_documents import embeddings working_dir = os.path.dirname(os.path.abspath(__file__)) config_data = json.load(open(f"{working_dir}/config.json")) GROQ_API_KEY = config_data["GROQ_API_KEY"] os.environ["GROQ_API_KEY"]= GROQ_API_KEY # Set up the database with check_same_thread=False def setup_db(): conn = sqlite3.connect("chat_history.db", check_same_thread=False) # Ensure thread-safe connection cursor = conn.cursor() cursor.execute(""" CREATE TABLE IF NOT EXISTS chat_histories ( id INTEGER PRIMARY KEY AUTOINCREMENT, username TEXT, timestamp TEXT, day TEXT, user_message TEXT, assistant_response TEXT ) """) conn.commit() return conn # Return the connection # Function to save chat history to SQLite def save_chat_history(conn, username, timestamp, day, user_message, assistant_response): cursor = conn.cursor() cursor.execute(""" INSERT INTO chat_histories (username, timestamp, day, user_message, assistant_response) VALUES (?, ?, ?, ?, ?) """, (username, timestamp, day, user_message, assistant_response)) conn.commit() # Function to set up vectorstore for embeddings def setup_vectorstore(): embeddings = HuggingFaceEmbeddings() vectorstore = Chroma(persist_directory="vector_db_2R", embedding_function=embeddings) return vectorstore # Function to set up the chatbot chain def chat_chain(vectorstore): llm = ChatGroq(model="llama-3.1-70b-versatile", temperature=0) retriever = vectorstore.as_retriever() memory = ConversationBufferMemory( llm=llm, output_key="answer", memory_key="chat_history", return_messages=True ) chain = ConversationalRetrievalChain.from_llm( llm=llm, retriever=retriever, chain_type="stuff", memory=memory, verbose=True, return_source_documents=True ) return chain # Streamlit UI setup st.set_page_config(page_title="Notes.AI", page_icon="🤖AI", layout="centered") st.title("🤖 Notes.AI") st.subheader("Hey! Here you can search for notes of CSE 3rd Sem! Read Notes, Read PYQ answers also!!") # Step 1: Initialize the connection and check if the user is already logged in if "conn" not in st.session_state: st.session_state.conn = setup_db() if "username" not in st.session_state: username = st.text_input("Enter your name to proceed:") if username: with st.spinner("Loading chatbot interface... Please wait."): st.session_state.username = username st.session_state.chat_history = [] # Initialize empty chat history in memory st.session_state.vectorstore = setup_vectorstore() st.session_state.conversational_chain = chat_chain(st.session_state.vectorstore) st.success(f"Welcome, {username}! The chatbot interface is ready.") else: username = st.session_state.username # Step 2: Initialize components if not already set if "conversational_chain" not in st.session_state: st.session_state.vectorstore = setup_vectorstore() st.session_state.conversational_chain = chat_chain(st.session_state.vectorstore) # Step 3: Display the chat history in the UI if "username" in st.session_state: st.subheader(f"Hello {username}, start your query below!") # Display chat history (messages exchanged between user and assistant) if st.session_state.chat_history: for message in st.session_state.chat_history: if message['role'] == 'user': with st.chat_message("user"): st.markdown(message["content"]) elif message['role'] == 'assistant': with st.chat_message("assistant"): st.markdown(message["content"]) # Input field for the user to type their message user_input = st.chat_input("Ask AI....") if user_input: with st.spinner("Processing your query... Please wait."): # Save user input to chat history in memory st.session_state.chat_history.append({"role": "user", "content": user_input}) # Display user's message in chatbot (for UI display) with st.chat_message("user"): st.markdown(user_input) # Get assistant's response from the chain with st.chat_message("assistant"): response = st.session_state.conversational_chain({"question": user_input}) assistant_response = response["answer"] st.markdown(assistant_response) # Save assistant's response to chat history in memory st.session_state.chat_history.append({"role": "assistant", "content": assistant_response}) # Save the chat history to the database (SQLite) timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") day = datetime.now().strftime("%A") # Get the day of the week (e.g., Monday) save_chat_history(st.session_state.conn, username, timestamp, day, user_input, assistant_response) # # Set up the database with check_same_thread=False # def setup_db(): # conn = sqlite3.connect("chat_history.db", check_same_thread=False) # Ensure thread-safe connection # cursor = conn.cursor() # cursor.execute(""" # CREATE TABLE IF NOT EXISTS chat_histories ( # id INTEGER PRIMARY KEY AUTOINCREMENT, # username TEXT, # timestamp TEXT, # day TEXT, # user_message TEXT, # assistant_response TEXT # ) # """) # conn.commit() # return conn # Return the connection # # Function to save chat history to SQLite # def save_chat_history(conn, username, timestamp, day, user_message, assistant_response): # cursor = conn.cursor() # cursor.execute(""" # INSERT INTO chat_histories (username, timestamp, day, user_message, assistant_response) # VALUES (?, ?, ?, ?, ?) # """, (username, timestamp, day, user_message, assistant_response)) # conn.commit() # # Function to load chat history from SQLite # def load_chat_history(conn, username): # cursor = conn.cursor() # cursor.execute(""" # SELECT timestamp, day, user_message, assistant_response # FROM chat_histories # WHERE username = ? # ORDER BY timestamp # """, (username,)) # chat_history = cursor.fetchall() # return chat_history # # Function to set up vectorstore for embeddings # def setup_vectorstore(): # embeddings = HuggingFaceEmbeddings() # vectorstore = Chroma(persist_directory="vector_db_dir_notes_ai", embedding_function=embeddings) # return vectorstore # # Function to set up the chatbot chain # def chat_chain(vectorstore): # llm = ChatGroq( # model="llama-3.1-70b-versatile", # temperature=0 # ) # retriever = vectorstore.as_retriever() # memory = ConversationBufferMemory( # llm=llm, # output_key="answer", # memory_key="chat_history", # return_messages=True # ) # chain = ConversationalRetrievalChain.from_llm( # llm=llm, # retriever=retriever, # chain_type="stuff", # memory=memory, # verbose=True, # return_source_documents=True # ) # return chain # # Streamlit UI setup # st.set_page_config( # page_title="Notes.AI", # page_icon="🤖AI", # layout="centered" # ) # st.title("🤖 Notes.AI") # st.subheader("Hey! Here you can search for notes of CSE 7th Sem! Read Notes, Read PYQ answers also!!") # # Step 1: Initialize the connection and check if the user is already logged in # if "conn" not in st.session_state: # st.session_state.conn = setup_db() # if "username" not in st.session_state: # username = st.text_input("Enter your name to proceed:") # if username: # with st.spinner("Loading chatbot interface... Please wait."): # st.session_state.username = username # st.session_state.chat_history = [] # st.session_state.vectorstore = setup_vectorstore() # st.session_state.conversational_chain = chat_chain(st.session_state.vectorstore) # st.success(f"Welcome, {username}! The chatbot interface is ready.") # else: # username = st.session_state.username # # Step 2: Initialize components if not already set # if "conversational_chain" not in st.session_state: # st.session_state.vectorstore = setup_vectorstore() # st.session_state.conversational_chain = chat_chain(st.session_state.vectorstore) # # Step 3: Show chatbot interface # if "username" in st.session_state: # st.subheader(f"Hello {username}, start your query below!") # user_input = st.chat_input("Ask AI....") # if user_input: # with st.spinner("Processing your query... Please wait."): # # Save user input to chat history # st.session_state.chat_history.append({"role": "user", "content": user_input}) # # Display user's message # with st.chat_message("user"): # st.markdown(user_input) # # Get assistant's response # with st.chat_message("assistant"): # response = st.session_state.conversational_chain({"question": user_input}) # assistant_response = response["answer"] # st.markdown(assistant_response) # # Save response to chat history # st.session_state.chat_history.append({"role": "assistant", "content": assistant_response}) # # Save chat history to SQLite database with timestamp # timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") # day = datetime.now().strftime("%A") # Get the day of the week (e.g., Monday) # save_chat_history(st.session_state.conn, username, timestamp, day, user_input, assistant_response) # # Display chat history for the current user # if "username" in st.session_state: # st.subheader(f"Chat History for {username}:") # chat_history = load_chat_history(st.session_state.conn, username) # if chat_history: # for entry in chat_history: # timestamp, day, user_message, assistant_response = entry # st.write(f"**{day} - {timestamp}:**") # st.write(f"**User:** {user_message}") # st.write(f"**Assistant:** {assistant_response}") # else: # st.write("No chat history available.") # import os # import json # from datetime import datetime # import streamlit as st # from langchain_huggingface import HuggingFaceEmbeddings # from langchain_chroma import Chroma # from langchain_groq import ChatGroq # from langchain.memory import ConversationBufferMemory # from langchain.chains import ConversationalRetrievalChain # # Ensure the JSON file exists # chat_history_file = "chat_histories.json" # if not os.path.exists(chat_history_file): # with open(chat_history_file, "w") as f: # json.dump({}, f) # # Functions to handle chat history # def load_chat_history(): # with open(chat_history_file, "r") as f: # return json.load(f) # def save_chat_history(chat_histories): # with open(chat_history_file, "w") as f: # json.dump(chat_histories, f, indent=4) # # Function to set up vectorstore # def setup_vectorstore(): # embeddings = HuggingFaceEmbeddings() # vectorstore = Chroma(persist_directory="vector_db_dir_notes_ai", # embedding_function=embeddings) # return vectorstore # # Function to set up chatbot chain # def chat_chain(vectorstore): # llm = ChatGroq( # model="llama-3.1-70b-versatile", # temperature=0 # ) # retriever = vectorstore.as_retriever() # memory = ConversationBufferMemory( # llm=llm, # output_key="answer", # memory_key="chat_history", # return_messages=True # ) # chain = ConversationalRetrievalChain.from_llm( # llm=llm, # retriever=retriever, # chain_type="stuff", # memory=memory, # verbose=True, # return_source_documents=True # ) # return chain # # Streamlit UI # st.set_page_config( # page_title="Notes.AI", # page_icon="🤖AI", # layout="centered" # ) # st.title("🤖 Notes.AI") # st.subheader("Hey! Here you can search for notes of CSE 7th Sem! Read Notes, Read PYQ answers also!!") # # Step 1: Input user's name # if "username" not in st.session_state: # username = st.text_input("Enter your name to proceed:") # if username: # with st.spinner("Loading chatbot interface... Please wait."): # st.session_state.username = username # st.session_state.chat_history = [] # Initialize empty chat history # st.session_state.vectorstore = setup_vectorstore() # st.session_state.conversational_chain = chat_chain(st.session_state.vectorstore) # st.success(f"Welcome, {username}! The chatbot interface is ready.") # else: # username = st.session_state.username # # Step 2: Initialize components if not already set # if "conversational_chain" not in st.session_state: # st.session_state.vectorstore = setup_vectorstore() # st.session_state.conversational_chain = chat_chain(st.session_state.vectorstore) # # Step 3: Show chatbot interface # if "username" in st.session_state: # st.subheader(f"Hello {username}, start your query below!") # # Display existing chat history dynamically # for message in st.session_state.chat_history: # if message["role"] == "user": # with st.chat_message("user"): # st.markdown(f"{message['day']}: {message['content']}") # elif message["role"] == "assistant": # with st.chat_message("assistant"): # st.markdown(f"{message['day']}: {message['content']}") # # User input section # user_input = st.chat_input("Ask AI....") # if user_input: # with st.spinner("Processing your query... Please wait."): # # Save user input to session state # st.session_state.chat_history.append({"role": "user", "content": user_input}) # # Display user's message # with st.chat_message("user"): # st.markdown(user_input) # # Get assistant's response # with st.chat_message("assistant"): # response = st.session_state.conversational_chain({"question": user_input}) # assistant_response = response["answer"] # st.markdown(assistant_response) # # Save assistant's response to session state # st.session_state.chat_history.append({"role": "assistant", "content": assistant_response}) # # Save chat history to file with timestamp # chat_histories = load_chat_history() # timestamp = datetime.now() # day = timestamp.strftime("%A") # Get the full weekday name (e.g., Monday) # formatted_timestamp = timestamp.strftime("%Y-%m-%d %H:%M:%S") # if username not in chat_histories: # chat_histories[username] = [] # chat_histories[username].append({ # "timestamp": formatted_timestamp, # "day": day, # "user": user_input, # "assistant": assistant_response # }) # save_chat_history(chat_histories) # import os # import json # import streamlit as st # from langchain_huggingface import HuggingFaceEmbeddings # from langchain_chroma import Chroma # from langchain_groq import ChatGroq # from langchain.memory import ConversationBufferMemory # from langchain.chains import ConversationalRetrievalChain # from vectorize_documents import embeddings # working_dir = os.path.dirname(os.path.abspath(__file__)) # config_data = json.load(open(f"{working_dir}/config.json")) # GROQ_API_KEY = config_data["GROQ_API_KEY"] # os.environ["GROQ_API_KEY"]= GROQ_API_KEY # def setup_vectorstore(): # persist_directory = f"{working_dir}/vector_db_dir_notes_ai" # embeddings = HuggingFaceEmbeddings() # vectorstore = Chroma(persist_directory=persist_directory, # embedding_function=embeddings) # return vectorstore # def chat_chain(vectorstore): # llm = ChatGroq( # model = "llama-3.1-70b-versatile", # temperature = 0 # ) # retriever = vectorstore.as_retriever() # memory = ConversationBufferMemory( # llm = llm, # output_key = "answer", # memory_key = "chat_history", # return_messages = True # ) # chain = ConversationalRetrievalChain.from_llm( # llm=llm, # retriever = retriever, # chain_type = "stuff", # memory = memory, # verbose=True, # return_source_documents= True # ) # return chain # st.set_page_config( # page_title="Notes.AI", # page_icon="🤖AI", # layout="centered" # ) # st.title("🤖 Notes.AI") # # st.title("🤖 Hey! Here you can search for notes of CSE 7th Sem! Read Notes, Read PYQ answers also!!") # st.subheader("Hey! Here you can search for notes of CSE 7th Sem! Read Notes, Read PYQ answers also!!") # # Additional subheading # st.subheader("Start your query below to get instant help!") # if "chat_history" not in st.session_state: # st.session_state.chat_history = [] # if "vectorstore" not in st.session_state: # st.session_state.vectorstore = setup_vectorstore() # if "conversational_chain" not in st.session_state: # st.session_state.conversational_chain = chat_chain(st.session_state.vectorstore) # for message in st.session_state.chat_history: # with st.chat_message(message["role"]): # st.markdown(message["content"]) # user_input = st.chat_input("Ask AI....") # if user_input: # st.session_state.chat_history.append({"role":"user", "content":user_input}) # with st.chat_message("user"): # st.markdown(user_input) # with st.chat_message("assistant"): # response = st.session_state.conversational_chain({"question":user_input}) # assistant_response = response["answer"] # st.markdown(assistant_response) # st.session_state.chat_history.append({"role":"assistant","content": assistant_response})