Update app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 import yfinance as yf
 import pandas as pd
+import random
 
 # Mapping company names to their ticker symbols
 company_dict = {
@@ -11,45 +12,49 @@ company_dict = {
     "Tesla": "TSLA"
 }
 
-# Function to fetch ESG data and save it to a CSV file
-def fetch_and_save_esg_data(company_name):
+# 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 the data is available, save it to a CSV file
+    
+    # If ESG data is available, process it into a DataFrame
     if esg_data is not None:
-        # Convert the ESG data to a pandas DataFrame
         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)
         
-        # Save it to a CSV file
-        filename = f"{ticker}_esg_data.csv"
-        esg_df.to_csv(filename)
+        # Prepare a DataFrame for plotting
+        plot_df = pd.DataFrame({
+            "ESG Category": ["Environment", "Social", "Governance"],
+            "Score": esg_scores.squeeze().values
+        })
         
-        return f"ESG data for {company_name} saved to {filename}", filename
+        return plot_df
     else:
-        return f"No ESG data available for {company_name}", None
+        # Return an empty DataFrame if no data is available
+        return pd.DataFrame()
 
-# Gradio interface with a dropdown for company selection
+# Gradio interface with a dropdown for company selection and line plot visualization
 def app_interface():
     with gr.Blocks() as app:
         # Dropdown to select company name
         company = gr.Dropdown(label="Select Company", choices=list(company_dict.keys()), value="Apple")
         
-        # Button to trigger ESG data extraction
-        fetch_button = gr.Button("Fetch ESG Data")
+        # Button to fetch and plot ESG data
+        plot_button = gr.Button("Generate ESG Plot")
         
-        # Output text and download link
-        output_text = gr.Textbox(label="Output")
-        download_link = gr.File(label="Download CSV")
+        # LinePlot component for displaying the ESG data
+        plot_output = gr.LinePlot(label="ESG Scores Plot", x="ESG Category", y="Score", overlay_point=True)
 
-        # Define the action when the button is clicked
-        fetch_button.click(fn=fetch_and_save_esg_data, 
-                           inputs=company, 
-                           outputs=[output_text, download_link])
+        # Define the action when the "Generate ESG Plot" button is clicked
+        plot_button.click(fn=fetch_esg_data, 
+                          inputs=company, 
+                          outputs=plot_output)
 
     return app
 
@@ -58,3 +63,4 @@ app = app_interface()
 app.launch()
 
 
+