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# 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
# 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
# # 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! Upload your question bank and get answers instantly!")
# # 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: File upload for question bank
# st.subheader("Upload your question bank (PDF or DOC):")
# uploaded_file = st.file_uploader("Choose a file", type=["pdf", "doc", "docx"])
# if uploaded_file:
# # Process the uploaded file
# with st.spinner("Reading and processing your question bank..."):
# import docx2txt
# from PyPDF2 import PdfReader
# # Extract questions from the file
# def extract_questions(file):
# if file.name.endswith(".pdf"):
# reader = PdfReader(file)
# text = "\n".join([page.extract_text() for page in reader.pages])
# elif file.name.endswith((".doc", ".docx")):
# text = docx2txt.process(file)
# else:
# text = ""
# return text.strip().split("\n")
# questions = extract_questions(uploaded_file)
# # Generate answers using the LLM
# answers = []
# for question in questions:
# if question.strip():
# response = st.session_state.conversational_chain({"question": question})
# answers.append({"question": question, "answer": response["answer"]})
# # Save Q&A to a file
# output_file_path = f"question_answers_{username}.txt"
# with open(output_file_path, "w") as f:
# for qa in answers:
# f.write(f"Q: {qa['question']}\nA: {qa['answer']}\n\n")
# st.success("All questions have been answered and saved!")
# # Provide download link
# with open(output_file_path, "rb") as f:
# st.download_button(
# label="Download Q&A File",
# data=f,
# file_name=output_file_path,
# mime="text/plain"
# )
# # Chatbot interface for additional questions
# st.subheader(f"Hello {username}, ask additional questions below!")
# if "username" in st.session_state:
# # Display existing chat history dynamically
# 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"])
# # 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().strftime("%Y-%m-%d %A") # Added day to timestamp
# if username not in chat_histories:
# chat_histories[username] = []
# chat_histories[username].append({
# "timestamp": timestamp,
# "user": user_input,
# "assistant": assistant_response
# })
# save_chat_history(chat_histories)
#all working but file upload is not added in below code
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
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
# 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(message["content"])
elif message["role"] == "assistant":
with st.chat_message("assistant"):
st.markdown(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().strftime("%Y-%m-%d %H:%M:%S")
if username not in chat_histories:
chat_histories[username] = []
chat_histories[username].append({
"timestamp": timestamp,
"user": user_input,
"assistant": assistant_response
})
save_chat_history(chat_histories)
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