Upload 3 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+chatbot.jpg filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,184 @@
+import pandas as pd
+import streamlit.components.v1 as components
+import streamlit as st
+from ydata_profiling import ProfileReport
+from streamlit_pandas_profiling import st_profile_report
+from langchain.llms.openai import OpenAI
+from langchain_experimental.agents import create_csv_agent
+from langchain.agents.agent_types import AgentType
+import time
+import os
+from mitosheet.streamlit.v1 import spreadsheet
+from pygwalker.api.streamlit import init_streamlit_comm, get_streamlit_html
+
+# Global variable to store uploaded file
+uploaded_file = None
+
+def main():
+    global uploaded_file
+    st.sidebar.title("App Options")
+    option = st.sidebar.selectbox("Choose an option", ["View Instructions", "View Data","Data Profiling","Tableau AI", "CSV Chatbot"])
+
+    if option == "View Instructions":
+        show_instructions()
+    elif option == "Data Profiling":
+        data_profiling()
+    elif option == "CSV Chatbot":
+        csv_chatbot()
+    elif option == "View Data":
+        view_data()
+    elif option == "Tableau AI":
+        tableau_ai()
+
+def show_instructions():
+    st.title("Welcome to the AI TOOL - Made for MDH")
+    st.write("This tool offers several functionalities to help you analyze and work with your data.")
+    st.write("Please select an option from the sidebar to proceed:")
+    st.write("- **View Data:** Upload a CSV file and view its contents.")
+    st.write("- **Data Profiling:** Upload a CSV file to generate a data profiling report.")
+    st.write("- **CSV Chatbot:** Interact with a chatbot to get insights from your CSV data.")
+    st.write("- **Tableau AI:** Upload a CSV file to visualize it using Tableau AI.")
+    st.write("- **View Instructions:** View these instructions again.")
+
+def data_profiling():
+    global uploaded_file
+    st.title("Data Profiling App")
+    if uploaded_file is None:
+        uploaded_file = st.file_uploader("Upload CSV file", type=["csv", "xlsx"])
+        if uploaded_file is None:
+            st.warning("Please upload a CSV or Excel file.")
+            st.stop()  # Stop execution if no file uploaded
+
+    if uploaded_file.name.endswith('.xlsx'):
+        # Load Excel file into pandas DataFrame
+        df_excel = pd.read_excel(uploaded_file)
+        # Save DataFrame as CSV
+        csv_filename = uploaded_file.name.replace('.xlsx', '.csv')
+        df_excel.to_csv(csv_filename, index=False)
+        st.success(f"Excel file converted to CSV: {csv_filename}")
+        # Set uploaded file to the converted CSV file
+        uploaded_file = open(csv_filename, 'rb')
+
+    df = pd.read_csv(uploaded_file)
+    st.dataframe(df)
+
+    # Generate and display the data profile report
+    pr = ProfileReport(df, title="Report")
+    st_profile_report(pr)
+
+def csv_chatbot():
+    global uploaded_file
+    st.sidebar.title("OpenAI Settings")
+    st.title("Personal Assistant")
+    st.text("A BR CREATION")
+    st.warning("Also try Google PALM @ [PALMCSV](https://palmcsvbot.streamlit.app/)")
+    st.image("chatbot.jpg", caption="Chatbot", width=178)
+   
+    if uploaded_file is None:
+        uploaded_file = st.file_uploader("Upload CSV file", type=["csv", "xlsx"])
+        if uploaded_file is None:
+            st.warning("Please upload a CSV or Excel file.")
+            st.stop()  # Stop execution if no file uploaded
+
+    openai_api_key = st.text_input("Enter your OpenAI API Key", type="password")
+    if not openai_api_key:
+        st.warning("You should have an OpenAI API key to continue. Get one at [OpenAI API Keys](https://platform.openai.com/api-keys)")
+        st.stop()
+
+    os.environ['OPENAI_API_KEY'] = openai_api_key
+    llm = OpenAI(temperature=0)
+    agent = create_csv_agent(
+        llm,
+        uploaded_file,
+        verbose=False,
+        agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+    )
+
+    predefined_questions = ["How many rows are there in the dataset?", "Explain the dataset."]
+    selected_question = st.selectbox("Select a question", ["Select a question"] + predefined_questions) 
+    custom_question = st.text_input("Or ask a custom question")
+
+    if st.button("Ask"):
+        if selected_question != "Select a question":
+            query = selected_question
+        elif custom_question.strip() != "":
+            query = custom_question.strip()
+        else:
+            st.warning("Please select a predefined question or ask a custom question.")
+            return
+
+        start = time.time()
+        answer = agent.run(query)
+        end = time.time()
+        st.write(answer)
+        st.write(f"Answer (took {round(end - start, 2)} s.)")
+
+def view_data():
+    global uploaded_file
+    st.title("Data Viewer Portal")
+    if uploaded_file is None:
+        uploaded_file = st.file_uploader("Upload CSV file", type=["csv", "xlsx"])
+        if uploaded_file is None:
+            st.warning("Please upload a CSV or Excel file.")
+            st.stop()  # Stop execution if no file uploaded
+
+    if uploaded_file.name.endswith('.xlsx'):
+        # Load Excel file into pandas DataFrame
+        df_excel = pd.read_excel(uploaded_file)
+        # Save DataFrame as CSV
+        csv_filename = uploaded_file.name.replace('.xlsx', '.csv')
+        df_excel.to_csv(csv_filename, index=False)
+        st.success(f"Excel file converted to CSV: {csv_filename}")
+        # Set uploaded file to the converted CSV file
+        uploaded_file = open(csv_filename, 'rb')
+
+    df = pd.read_csv(uploaded_file)
+
+    # Convert the dataframe to a list of dictionaries
+    dataframe = df.to_dict(orient="records")
+
+    # Display the dataframe in a Mito spreadsheet
+    final_dfs, code = spreadsheet(dataframe)
+
+def tableau_ai():
+    global uploaded_file
+    st.title("Virtual Tableau AI Tool")
+    init_streamlit_comm()
+
+    # Function to get PygWalker HTML
+    @st.cache_data
+    def get_pyg_html(df: pd.DataFrame) -> str:
+        html = get_streamlit_html(df, use_kernel_calc=True, debug=False)
+        return html
+
+    # Function to get user uploaded DataFrame
+    def get_user_uploaded_data():
+        if uploaded_file is not None:
+            return pd.read_csv(uploaded_file)
+        return None
+
+    if uploaded_file is None:
+        uploaded_file = st.file_uploader("Upload a CSV file", type=["csv", "xlsx"])
+        if uploaded_file is None:
+            st.warning("Please upload a CSV or Excel file.")
+            st.stop()  # Stop execution if no file uploaded
+
+    if uploaded_file.name.endswith('.xlsx'):
+        # Load Excel file into pandas DataFrame
+        df_excel = pd.read_excel(uploaded_file)
+        # Save DataFrame as CSV
+        csv_filename = uploaded_file.name.replace('.xlsx', '.csv')
+        df_excel.to_csv(csv_filename, index=False)
+        st.success(f"Excel file converted to CSV: {csv_filename}")
+        # Set uploaded file to the converted CSV file
+        uploaded_file = open(csv_filename, 'rb')
+
+    df = get_user_uploaded_data()
+
+    if df is not None:
+        components.html(get_pyg_html(df), width=1300, height=1000, scrolling=True)
+    else:
+        st.write("Please upload a CSV file to proceed.")
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,9 @@
+langchain
+langchain_experimental
+openai
+streamlit
+tabulate
+ydata_profiling
+mitosheet 
+streamlit-pandas-profiling
+pygwalker