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app.py

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+import streamlit as st
+import pandas as pd
+from langchain.agents import create_pandas_dataframe_agent
+from langchain.llms import OpenAI
+# Function to get dataset from user
+def get_user_dataset():
+    st.sidebar.header("Upload Dataset")
+    uploaded_file = st.sidebar.file_uploader("Choose a CSV or Excel file", type=["csv", "xlsx"])
+    if uploaded_file is not None:
+        try:
+            if uploaded_file.name.endswith('csv'):
+                df = pd.read_csv(uploaded_file)
+            else:
+                df = pd.read_excel(uploaded_file)
+            return df
+        except Exception as e:
+            st.sidebar.error(f"Error: {e}")
+    return None
+
+# Function to initialize or retrieve agent
+def get_agent(df, openai_api_key):
+    if "agent" not in st.session_state:
+        agent = create_pandas_dataframe_agent(OpenAI(temperature=0, openai_api_key=openai_api_key), df, verbose=True)
+        st.session_state.agent = agent
+    return st.session_state.agent
+
+# Main Streamlit app
+def main():
+    st.set_page_config(
+        page_title="DataTalker: Have a Conversation with Your Dataset",
+        page_icon=":speech_balloon:",
+    )
+    st.title("Welcome to DataTalker")
+    # Provide user instructions
+    st.sidebar.header("Instructions")
+    #st.sidebar.markdown(
+        #"1. Use the sidebar to upload a CSV or Excel file containing your dataset.\n"
+        #"2. Once the dataset is uploaded, you can start chatting with it!\n"
+        #"3. Type your message in the chat input box and press Enter to send it.\n"
+        #"4. The assistant will respond based on the message you provide.\n"
+    #S)
+    st.sidebar.write("\n\n")
+    st.sidebar.markdown("**Get a free API key from OpenAI:**")
+    st.sidebar.markdown("* Create a [free account](https://platform.openai.com/signup?launch) or [login](https://platform.openai.com/login)")
+    st.sidebar.markdown("* Go to **Personal** and then **API keys**")
+    st.sidebar.markdown("* Create a new API")
+    st.sidebar.markdown("* Paste your API key in the text box")
+    st.sidebar.divider()
+    # Get OpenAI API Key from user
+    openai_api_key = st.sidebar.text_input("Enter your OpenAI API Key")
+
+    # Get or upload dataset
+    df = get_user_dataset()
+    if df is None:
+        return
+
+    # Initialize or retrieve agent
+    agent = get_agent(df, openai_api_key)
+
+    # Initialize chat history
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    # Display chat messages from history on app rerun
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    # Accept user input
+    if prompt := st.chat_input("Ask a question about your dataset"):
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        # Display user message in chat message container
+        with st.chat_message("user"):
+            st.markdown(prompt)
+
+        # Display assistant response in chat message container
+        with st.chat_message("assistant"):
+            message_placeholder = st.empty()
+            full_response = ""
+            assistant_response = agent.run(prompt)
+            # Simulate stream of response with milliseconds delay
+            for chunk in assistant_response.split('n/'):
+                full_response += chunk + " "
+                # Add a blinking cursor to simulate typing
+                message_placeholder.markdown(full_response + "▌")
+            message_placeholder.markdown(full_response)
+        # Add assistant response to chat history
+        st.session_state.messages.append({"role": "assistant", "content": full_response})
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit
+pandas
+langchain