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| 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 | |
| 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() | |