Update app.py

app.py

@@ -16,11 +16,16 @@ client = genai.Client(api_key=api_key)
 # 2. Define the core data and plotting logic
 def analyze_and_plot(uploaded_file, user_question):
     if uploaded_file is None:
-        return "⚠️ Please upload a CSV file first.", None
+        # Return error message, hide chart, disable download button
+        return "⚠️ Please upload a CSV file first.", None, gr.update(visible=False)
         
     try:
-        # Clear any previous figures to avoid overlapping graphs
+        # Clear any previous figures and delete old reports to keep clean states
         plt.close('all')
+        if os.path.exists("analysis_report.txt"):
+            os.remove("analysis_report.txt")
+        if os.path.exists("output_chart.png"):
+            os.remove("output_chart.png")
         
         # Read the uploaded CSV file using Pandas
         df = pd.read_csv(uploaded_file.name)
@@ -87,35 +92,44 @@ def analyze_and_plot(uploaded_file, user_question):
                     chart_path = "output_chart.png"
             except Exception as e:
                 text_response += f"\n\n*(Note: There was a background error rendering the chart: {str(e)})*"
-                
-        return text_response, chart_path
+        
+        # Save the finalized text analysis into a downloadable file on the server
+        with open("analysis_report.txt", "w", encoding="utf-8") as f:
+            f.write(text_response)
+            
+        # Return text, chart path, and make the download button visible with the report file attached
+        return text_response, chart_path, gr.update(value="analysis_report.txt", visible=True)
         
     except Exception as e:
-        return f"❌ An error occurred processing the file: {str(e)}", None
+        return f"❌ An error occurred processing the file: {str(e)}", None, gr.update(visible=False)
 
 # 3. Design the custom layout using Gradio Blocks
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("# 📊 Visual Cloud AI Data Analyst")
-    gr.Markdown("Upload a CSV file, ask a question, or request an advanced statistical regression modeling.")
+    gr.Markdown("Upload a CSV file, ask a question, and download your final text reports easily.")
     
     with gr.Row():
         with gr.Column(scale=1):
             file_input = gr.File(label="Step 1: Upload CSV Dataset", file_types=[".csv"])
             question_input = gr.Textbox(
                 label="Step 2: Ask a question or request a model", 
-                placeholder="e.g., 'Run a linear regression between ColumnA and ColumnB and show statistics'..."
+                placeholder="e.g., 'Run a linear regression between ColumnA and ColumnB'..."
             )
             submit_btn = gr.Button("Analyze & Generate Chart", variant="primary")
             
         with gr.Column(scale=1):
             output_text = gr.Markdown(label="AI Written Insights")
+            
+            # The download button is hidden until an analysis runs successfully
+            download_btn = gr.DownloadButton("📥 Download Text Report", visible=False)
+            
             output_image = gr.Image(label="Generated Data Visualization", type="filepath")
             
-    # Connect the inputs to the function when clicking the button
+    # Connect inputs to the execution function and distribute the 3 outputs
     submit_btn.click(
         fn=analyze_and_plot, 
         inputs=[file_input, question_input], 
-        outputs=[output_text, output_image]
+        outputs=[output_text, output_image, download_btn]
     )
 
 # 4. Launch the public cloud server