app.py

import streamlit as st
from streamlit_chat import message
import os, tempfile, sys
from io import BytesIO
from io import StringIO
import pandas as pd
from langchain.agents import create_pandas_dataframe_agent
from langchain.llms.openai import OpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.chains.summarize import load_summarize_chain
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains.mapreduce import MapReduceChain
from langchain.docstore.document import Document
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.chains import RetrievalQA
from langchain.memory import ConversationBufferMemory
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts.prompt import PromptTemplate
from langchain import LLMChain


st.set_page_config(page_title="CSV Analyzer AI", layout="wide")

def chat(temperature, model_name):
    st.write("# Talk to CSV")
    # Add functionality for Page 1
    reset = st.sidebar.button("Reset Chat")
    uploaded_file = st.sidebar.file_uploader("Upload your CSV here 👇:", type="csv")

    if uploaded_file :
        with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
            tmp_file.write(uploaded_file.getvalue())
            tmp_file_path = tmp_file.name

        loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8")
        data = loader.load()

        embeddings = OpenAIEmbeddings()
        vectors = FAISS.from_documents(data, embeddings)

        chain = ConversationalRetrievalChain.from_llm(llm = ChatOpenAI(temperature=0.0,model_name='gpt-3.5-turbo', openai_api_key=user_api_key),
                                                                        retriever=vectors.as_retriever())

        def conversational_chat(query):
            
            result = chain({"question": query, "chat_history": st.session_state['history']})
            st.session_state['history'].append((query, result["answer"]))
            
            return result["answer"]
        
        if 'history' not in st.session_state:
            st.session_state['history'] = []

        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 ! 👋"]
            
        #container for the chat history
        response_container = st.container()
        #container for the user's text input
        container = st.container()

        with container:
            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 submit_button and user_input:
                output = conversational_chat(user_input)
                
                st.session_state['past'].append(user_input)
                st.session_state['generated'].append(output)

        if st.session_state['generated']:
            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")
                    
# Main App
st.markdown(
    """
    <div style='text-align: center;'>
        <h1>CSV Analyzer AI</h1>
    </div>
    """,
    unsafe_allow_html=True,
)
st.markdown(
    """
    <div style='text-align: center;'>
        <h4>⚡️ Analyzing CSV Files</h4>
    </div>
    """,
    unsafe_allow_html=True,
)


if os.path.exists(".env") and os.environ.get("OPENAI_API_KEY") is not None:
    user_api_key = os.environ["OPENAI_API_KEY"]
    st.success("API key loaded from .env", icon="🚀")
else:
    user_api_key = st.sidebar.text_input(
        label="#### Enter OpenAI API key 👇", placeholder="Paste your openAI API key, sk-", type="password", key="openai_api_key"
    )
    if user_api_key:
        st.sidebar.success("API key loaded", icon="🚀")

os.environ["OPENAI_API_KEY"] = user_api_key



# Execute the home page function
MODEL_OPTIONS = ["gpt-3.5-turbo", "gpt-4", "gpt-4-32k"]
max_tokens = {"gpt-4":7000, "gpt-4-32k":31000, "gpt-3.5-turbo":3000}
TEMPERATURE_MIN_VALUE = 0.0
TEMPERATURE_MAX_VALUE = 1.0
TEMPERATURE_DEFAULT_VALUE = 0.9
TEMPERATURE_STEP = 0.01
model_name = st.sidebar.selectbox(label="Model", options=MODEL_OPTIONS)
top_p = st.sidebar.slider("Top_P", 0.0, 1.0, 1.0, 0.1)
freq_penalty = st.sidebar.slider("Frequency Penalty", 0.0, 2.0, 0.0, 0.1)
temperature = st.sidebar.slider(
            label="Temperature",
            min_value=TEMPERATURE_MIN_VALUE,
            max_value=TEMPERATURE_MAX_VALUE,
            value=TEMPERATURE_DEFAULT_VALUE,
            step=TEMPERATURE_STEP,)



chat(temperature=temperature, model_name=model_name)