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| import gradio as gr | |
| import yfinance as yf | |
| import pandas as pd | |
| # Mapping company names to their ticker symbols | |
| company_dict = { | |
| "Apple": "AAPL", | |
| "Google": "GOOGL", | |
| "Microsoft": "MSFT", | |
| "Amazon": "AMZN", | |
| "Tesla": "TSLA" | |
| } | |
| # Function to fetch ESG data for the selected company | |
| def fetch_esg_data(company_name): | |
| # Get the ticker symbol from the company name | |
| ticker = company_dict[company_name] | |
| # Fetch ESG data from Yahoo Finance | |
| stock = yf.Ticker(ticker) | |
| esg_data = stock.sustainability | |
| # If ESG data is available, process it into a DataFrame | |
| if esg_data is not None: | |
| esg_df = pd.DataFrame(esg_data) | |
| # Extract only the relevant ESG scores and convert to a DataFrame | |
| esg_scores = esg_df.loc[["environmentScore", "socialScore", "governanceScore"], :].dropna().astype(float) | |
| # Prepare a DataFrame for plotting | |
| plot_df = pd.DataFrame({ | |
| "ESG Category": ["Environment", "Social", "Governance"], | |
| "Score": esg_scores.squeeze().values | |
| }) | |
| # Save the ESG data to a CSV file | |
| csv_filename = f"{ticker}_esg_data.csv" | |
| esg_df.to_csv(csv_filename) | |
| return plot_df, csv_filename, plot_df, plot_df | |
| else: | |
| # Return empty DataFrames and None if no data is available | |
| return pd.DataFrame(), None, pd.DataFrame(), pd.DataFrame() | |
| # Gradio interface with multiple tabs | |
| def app_interface(): | |
| with gr.Blocks() as app: | |
| with gr.Tab("ESG Data Analysis"): | |
| # Dropdown to select company name | |
| company = gr.Dropdown( | |
| label="Select Company", | |
| choices=list(company_dict.keys()), | |
| value="Apple" | |
| ) | |
| # Button to fetch and plot ESG data | |
| plot_button = gr.Button("Generate ESG Plot") | |
| # LinePlot component for displaying the ESG data | |
| line_plot = gr.LinePlot(label="ESG Scores Line Plot", x="ESG Category", y="Score", overlay_point=True) | |
| # ScatterPlot component for displaying the ESG data | |
| scatter_plot = gr.ScatterPlot(plot_df, x="ESG Category", y="Score", overlay_point=True) | |
| # BarPlot component for displaying the ESG data | |
| bar_plot = gr.BarPlot(plot_df x="ESG Category", y="Score") | |
| # File output for CSV download | |
| csv_output = gr.File(label="Download CSV") | |
| # Define the action when the "Generate ESG Plot" button is clicked | |
| plot_button.click(fn=fetch_esg_data, inputs=company, outputs=[line_plot, csv_output, scatter_plot, bar_plot]) | |
| with gr.Tab("Tab 2"): | |
| gr.Markdown("This is Tab 2. You can add more content here.") | |
| with gr.Tab("Tab 3"): | |
| gr.Markdown("This is Tab 3. Add your custom functionality here.") | |
| with gr.Tab("Tab 4"): | |
| gr.Markdown("This is Tab 4. Add more features or visualization here.") | |
| return app | |
| # Launch the Gradio app | |
| app = app_interface() | |
| if __name__ == "__main__": | |
| app.launch() | |