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app.py
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app.py
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import os
import pickle
import streamlit as st
import tempfile
import pandas as pd
import asyncio
# Import modules needed for building the chatbot application
from streamlit_chat import message
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.vectorstores import FAISS
# Set the Streamlit page configuration, including the layout and page title/icon
st.set_page_config(layout="wide", page_icon="💬", page_title="ChatBot-CSV")
# Display the header for the application using HTML markdown
st.markdown(
"<h1 style='text-align: center;'>ChatBot-CSV, Talk with your csv-data ! 💬</h1>",
unsafe_allow_html=True)
# Allow the user to enter their OpenAI API key
user_api_key = st.sidebar.text_input(
label="#### Your OpenAI API key 👇",
placeholder="Paste your openAI API key, sk-",
type="password")
def main():
# Check if the user has entered an OpenAI API key
if user_api_key == "":
# Display a message asking the user to enter their API key
st.markdown(
"<div style='text-align: center;'><h4>Enter your OpenAI API key to start chatting 😉</h4></div>",
unsafe_allow_html=True)
else:
# Set the OpenAI API key as an environment variable
os.environ["OPENAI_API_KEY"] = user_api_key
# Allow the user to upload a CSV file
uploaded_file = st.sidebar.file_uploader("upload", type="csv", label_visibility="hidden")
# If the user has uploaded a file, display it in an expander
if uploaded_file is not None:
def show_user_file(uploaded_file):
file_container = st.expander("Your CSV file :")
shows = pd.read_csv(uploaded_file)
uploaded_file.seek(0)
file_container.write(shows)
show_user_file(uploaded_file)
# If the user has not uploaded a file, display a message asking them to do so
else :
st.sidebar.info(
"👆 Upload your CSV file to get started, "
"sample for try : [fishfry-locations.csv](https://drive.google.com/file/d/1ByZwje8U7sPEUer_YkEtumUX4WHOaDpl/view?usp=sharing)"
)
if uploaded_file :
try :
# Define a function for storing document embeddings using Langchain and FAISS
def storeDocEmbeds(file, filename):
# Write the uploaded file to a temporary file
with tempfile.NamedTemporaryFile(mode="wb", delete=False) as tmp_file:
tmp_file.write(file)
tmp_file_path = tmp_file.name
# Load the data from the CSV file using Langchain
loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8")
data = loader.load()
# Create an embeddings object using Langchain
embeddings = OpenAIEmbeddings()
# Store the embeddings vectors using FAISS
vectors = FAISS.from_documents(data, embeddings)
os.remove(tmp_file_path)
# Save the vectors to a pickle file
with open(filename + ".pkl", "wb") as f:
pickle.dump(vectors, f)
# Define a function for retrieving document embeddings
def getDocEmbeds(file, filename):
# Check if embeddings vectors have already been stored in a pickle file
if not os.path.isfile(filename + ".pkl"):
# If not, store the vectors using the storeDocEmbeds function
storeDocEmbeds(file, filename)
# Load the vectors from the pickle file
with open(filename + ".pkl", "rb") as f:
global vectores
vectors = pickle.load(f)
return vectors
# Define a function for conducting conversational chat using Langchain
def conversational_chat(query):
# Use the Langchain ConversationalRetrievalChain to generate a response to the user's query
result = chain({"question": query, "chat_history": st.session_state['history']})
# Add the user's query and the chatbot's response to the chat history
st.session_state['history'].append((query, result["answer"]))
# Print the chat history for debugging purposes
print("Log: ")
print(st.session_state['history'])
return result["answer"]
# Set up sidebar with various options
with st.sidebar.expander("🛠️ Settings", expanded=False):
# Add a button to reset the chat history
if st.button("Reset Chat"):
st.session_state['reset_chat'] = True
# Allow the user to select a chatbot model to use
MODEL = st.selectbox(label='Model', options=['gpt-3.5-turbo','gpt-4'])
# If the chat history has not yet been initialized, do so now
if 'history' not in st.session_state:
st.session_state['history'] = []
# If the chatbot is not yet ready to chat, set the "ready" flag to False
if 'ready' not in st.session_state:
st.session_state['ready'] = False
# If the "reset_chat" flag has not been set, set it to False
if 'reset_chat' not in st.session_state:
st.session_state['reset_chat'] = False
# If a CSV file has been uploaded
if uploaded_file is not None:
# Display a spinner while processing the file
with st.spinner("Processing..."):
# Read the uploaded CSV file
uploaded_file.seek(0)
file = uploaded_file.read()
# Generate embeddings vectors for the file
vectors = getDocEmbeds(file, uploaded_file.name)
# Use the Langchain ConversationalRetrievalChain to set up the chatbot
chain = ConversationalRetrievalChain.from_llm(llm = ChatOpenAI(temperature=0.0,model_name=MODEL, streaming=True),
retriever=vectors.as_retriever(), chain_type="stuff")
# Set the "ready" flag to True now that the chatbot is ready to chat
st.session_state['ready'] = True
# If the chatbot is ready to chat
if st.session_state['ready']:
# If the chat history has not yet been initialized, initialize it now
if 'generated' not in st.session_state:
st.session_state['generated'] = ["Hello ! Ask me anything about " + uploaded_file.name + " 🤗"]
if 'past' not in st.session_state:
st.session_state['past'] = ["Hey ! 👋"]
# Create a container for displaying the chat history
response_container = st.container()
# Create a container for the user's text input
container = st.container()
with container:
# Create a form for the user to enter their query
with st.form(key='my_form', clear_on_submit=True):
user_input = st.text_input("Query:", placeholder="Talk about your csv data here (:", key='input')
submit_button = st.form_submit_button(label='Send')
# If the "reset_chat" flag has been set, reset the chat history and generated messages
if st.session_state['reset_chat']:
st.session_state['history'] = []
st.session_state['past'] = ["Hey ! 👋"]
st.session_state['generated'] = ["Hello ! Ask me anything about " + uploaded_file.name + " 🤗"]
response_container.empty()
st.session_state['reset_chat'] = False
# If the user has submitted a query
if submit_button and user_input:
# Generate a response using the Langchain ConversationalRetrievalChain
output = conversational_chat(user_input)
# Add the user's input and the chatbot's output to the chat history
st.session_state['past'].append(user_input)
st.session_state['generated'].append(output)
# If there are generated messages to display
if st.session_state['generated']:
# Display the chat history
with response_container:
for i in range(len(st.session_state['generated'])):
message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="big-smile")
message(st.session_state["generated"][i], key=str(i), avatar_style="thumbs")
#st.write(chain)
except Exception as e:
st.error(f"Error: {str(e)}")
# Create an expander for the "About" section
about = st.sidebar.expander("About 🤖")
# Write information about the chatbot in the "About" section
about.write("#### ChatBot-CSV is an AI chatbot featuring conversational memory, designed to enable users to discuss their CSV data in a more intuitive manner. 📄")
about.write("#### It employs large language models to provide users with seamless, context-aware natural language interactions for a better understanding of their CSV data. 🌐")
about.write("#### Powered by [Langchain](https://github.com/hwchase17/langchain), [OpenAI](https://platform.openai.com/docs/models/gpt-3-5) and [Streamlit](https://github.com/streamlit/streamlit) ⚡")
about.write("#### Source code : [Architectshwet/Chat-on-csv-data](https://github.com/Architectshwet/Chat-on-csv-data)")
#Run the main function
if __name__ == "__main__":
main()