Update app.py

app.py

@@ -223,148 +223,175 @@ def format_metrics_display(metrics: Dict) -> str:
 
 def load_example_sequence():
     """Load an example DNA sequence for testing"""
-    example = """ATGAAACGCATTAGCACCACCATTACCACCACCATCACCATTACCACAGGTAACGGTGCGGGCTGACGCGTACAGGAAACACAGAAAAAAGCCCGCACCTGACAGTGCGGGCTTTTTTTTTCGACCAAAGGTAACGAGGTAACAACCATGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGCAAATGCAGAACGTTTTCTGCGGGTTGCCGATATTCTGGAAAGCAATGCCAGGCAGGGGCAGGTGGCCACCGTCCTCTCTGCCCCCGCCAAAATCACCAACCACCTGGTGGCGATGATTGAAAAAACCATTAGCGGCCAGGATGCTTTACCCAATATCAGCGATGCCGAACGTATTTTTGCCGAACTTTTGACGGGACTCGCCGCCGCCCAGCCGGGGTTCCCGCTGGCGCAATTGAAAACTTTCGTCGATCAGGAATTTGCCCAAATAAAACATGTCCTGCATGGCATTAGTTTGTTGGGGCAGTGCCCGGATAGCATCAACGCTGCGCTGATTTGCCGTGGCGAGAAAATGTCGATCGCCATTATGGCCGGCGTATTAGAAGCGCGCGGTCACAACGTTACTGTTATCGATCCGGTCGAAAAACTGCTGGCAGTGGGGCATTACCTCGAATCTACCGTCGATATTGCTGAGTCCACCCGCCGTATTGCGGCAAGCCGCATTCCGGCTGATCACATGGTGCTGATGGCAGGTTTCACCGCCGGTAATGAAAAAGGCGAACTGGTGGTGCTTGGACGCAACGGTTCCGACTACTCTGCTGCGGTGCTGGCTGCCTGTTTACGCGCCGATTGTTGCGAGATTTGGACGGACGTTGACGGGGTCTATACCTGCGACCCGCGTCAGGTGCCCGATGCGAGGTTGTTGAAGTCGATGTCCTACCAGGAAGCGATGGAGCTTTCCTACTTCGGCGCTAAAGTTCTTCACCCCCGCACCATTACCCCCATCGCCCAGTTCCAGATCCCTTGCCTGATTAAAAATACCGGAAATCCTCAAGCACCAGGTACGCTCATTGGTGCCAGCCGTGATGAAGACGAATTACCGGTCAAGGGCATTTCCAATCTGAATAACATGGCAATGTTCAGCGTTTCCGGCCCGGGGATGAAAGGGATGGTCGGCATGGCGGCGCGCGTCTTTGCAGCGATGTCACGCGCCCGTATTTCCGTGGTGCTGATTACGCAATCATCTTCCGAATACAGCATCAGTTTCTGCGTTCCACAAAGCGACTGTGTGCGAGCTGAACGGGCAATGCAGGAAGAGTTCTACCTGGAACTGAAAGAAGGCTTACTGGAGCCGCTGGCAGTGACGGAACGGCTGGCCATTATCTCGGTGGTAGGTGATGGTATGCGCACCTTGCGTGGGATCTCGGCACCAGCGAAAGACGGTGGGCCGTGGATAAAGCGCGGCGTCTCGGCGTTTTCGGACCCCGCGGTCTCTTAACCCGAGTCCGAAAATTGTGATCGGGGCCGGGTTTAACGATGGAGCGATCGGGTCAATTGGGGCTGCACCGTTTGACCTGAAGACGCCGGCGGGAAACCGCGTTTCGTTTGCCAGGCGTGAGAGTATTCTTTCCGGCTCCGGTATAGCTGAAACATGAAATGCTTTCCCCTGCGCTTGGCCGATACGCTGGTTTAAGACTTCGGATCGCCGGGAAAGTCGCCCCCCACATTCTGCCAACGATTTGGTTAAAATAGTGACATTGGTGGAAACGGGGAAATGGGTTGACGGTTTTGAAGGGCGTGTCACACCATCGGTTGTTGGCGTTGACAAACGCGATCCGTATAATGAAACTGAATTTGTACACTTTCGCGTCGGGGATGTGGTCAGCAGTTAGGCTCCAATTGATGCCACGTTGACATGATCAATACCTGCGTGCCGGTCACAATCACCTTACCACCCAGTCCGATCAACGCCTGCGCGGGTGCGCAGATACGCGTGGTGTGTCTCGCGAACCGGGATCGTCGCACGGGCATGGAACACTATGGTGAGCAAGGGCGAGGAGTGATTACGCCTGATCTGCTGTTGAGAAGAAGCGCGTCTACCCCTCGGGACAAGGCAAAGAATTTGCTGCAGAAATACGCTGGAGATTGAAGGTTCTGGGAAACGTTTTGTTGACAGTTTACCTCCTGGACGATCCCGCGCCCGCAGGCTGGCGTCGCGATGAAACGAATTTCGGTTCACGGCCGGTGTAAGACGATCGATGGGCAGGGAATTGATGCCGATGCGGATGCCGCACCCGGGAAAGAACACGCTGCTGTGTACTGTCGGGTCGAAGAAAAGCTTGAAAGCGGGCGAAATTTTTCGCGCACCGTCGATGATCCGCACCCGCGAATTCGACCAGTGAAAGCGACTCGCGATGCGGCCGCGCTACAGGTTGTTAACCTGAATGAGGGCTAG"""
+    example = """ATGAAACGCATTAGCACCACCATTACCACCACCATCACCATTACCACAGGTAACGGTGCGGGCTGACGCGTACAGGAAACACAGAAAAAAGCCCGCACCTGACAGTGCGGGCTTTTTTTTTCGACCAAAGGTAACGAGGTAACAACCATGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGCAAATGCAGAACGTTTTCTGCGGGTTGCCGATATTCTGGAAAGCAATGCCAGGCAGGGGCAGGTGGCCACCGTCCTCTCTGCCCCCGCCAAAATCACCAACCACCTGGTGGCGATGATTGAAAAAACCATTAGCGGCCAGGATGCTTTACCCAATATCAGCGATGCCGAACGTATTTTTGCCGAACTTTTGACGGGACTCGCCGCCGCCCAGCCGGGGTTCCCGCTGGCGCAATTGAAAACTTTCGTCGATCAGGAATTTGCCCAAATAAAACATGTCCTGCATGGCATTAGTTTGTTGGGGCAGTGCCCGGATAGCATCAACGCTGCGCTGATTTGCCGTGGCGAGAAAATGTCGATCGCCATTATGGCCGGCGTATTAGAAGCGCGCGGTCACAACGTTACTGTTATCGATCCGGTCGAAAAACTGCTGGCAGTGGGGCATTACCTCGAATCTACCGTCGATATTGCTGAGTCCACCCGCCGTATTGCGGCAAGCCGCATTCCGGCTGATCACATGGTGCTGATGGCAGGTTTCACCGCCGGTAATGAAAAAGGCGAACTGGTGGTGCTTGGACGCAACGGTTCCGACTACTCTGCTGCGGTGCTGGCTGCCTGTTTACGCGCCGATTGTTGCGAGATTTGGACGGACGTTGACGGGGTCTATACCTGCGACCCGCGTCAGGTGCCCGATGCGAGGTTGTTGAAGTCGATGTCCTACCAGGAAGCGATGGAGCTTTCCTACTTCGGCGCTAAAGTTCTTCACCCCCGCACCATTACCCCCATCGCCCAGTTCCAGATCCCTTGCCTGATTAAAAATACCGGAAATCCTCAAGCACCAGGTACGCTCATTGGTGCCAGCCGTGATGAAGACGAATTACCGGTCAAGGGCATTTCCAATCTGAATAACATGGCAATGTTCAGCGTTTCCGGCCCGGGGATGAAAGGGATGGTCGGCATGGCGGCGCGCGTCTTTGCAGCGATGTCACGCGCCCGTATTTCCGTGGTGCTGATTACGCAATCATCTTCCGAATACAGCATCAGTTTCTGCGTTCCACAAAGCGACTGTGTGCGAGCTGAACGGGCAATGCAGGAAGAGTTCTACCTGGAACTGAAAGAAGGCTTACTGGAGCCGCTGGCAGTGACGGAACGGCTGGCCATTATCTCGGTGGTAGG"""
     return example
 
 def clear_inputs():
     """Clear all input fields"""
     return "", None, None, ""
 
-# Create the Gradio interface
+# Create the Gradio interface with simpler structure
 def create_interface():
-    # Show setup status
-    if predictor:
-        status_message = "✅ Model loaded successfully!"
-        status_color = "green"
-    else:
-        status_message = f"❌ Model failed to load: {initialization_error}"
-        status_color = "red"
-    
-    with gr.Blocks(
-        title="Gene Prediction Tool", 
-        theme=gr.themes.Soft(),
-        css="footer {visibility: hidden}"
-    ) as interface:
-        
-        gr.Markdown(f"""
-        # 🧬 Gene Prediction Tool
-        
-        This tool predicts gene regions in DNA sequences using a boundary-aware deep learning model.
-        The model identifies start and end positions of genes, along with confidence scores and detailed analysis.
+    try:
+        # Show setup status
+        if predictor:
+            status_message = "✅ Model loaded successfully!"
+            status_color = "green"
+        else:
+            status_message = f"��� Model failed to load: {initialization_error}"
+            status_color = "red"
         
-        <div style="padding: 10px; border-left: 4px solid {status_color}; background-color: #f9f9f9;">
-        <strong>Status:</strong> {status_message}
-        </div>
-        """)
+        # Create interface with minimal configuration
+        interface = gr.Blocks(
+            title="Gene Prediction Tool",
+            theme=gr.themes.Default()  # Use default theme instead of Soft
+        )
         
-        with gr.Row():
-            with gr.Column(scale=2):
-                # Input section
-                gr.Markdown("## 📝 Input")
-                
-                sequence_input = gr.Textbox(
-                    label="DNA Sequence",
-                    placeholder="Enter your DNA sequence (A, C, T, G, N only)...",
-                    lines=5,
-                    max_lines=10,
-                    info="Maximum length: 10,000 nucleotides"
-                )
+        with interface:
+            # Header
+            gr.HTML(f"""
+            <div style="text-align: center; padding: 20px;">
+                <h1>🧬 Gene Prediction Tool</h1>
+                <p>This tool predicts gene regions in DNA sequences using a boundary-aware deep learning model.</p>
+                <div style="padding: 10px; border-left: 4px solid {status_color}; background-color: #f9f9f9; margin: 10px 0;">
+                    <strong>Status:</strong> {status_message}
+                </div>
+            </div>
+            """)
+            
+            # Input section
+            gr.Markdown("## 📝 Input")
+            
+            sequence_input = gr.Textbox(
+                label="DNA Sequence",
+                placeholder="Enter your DNA sequence (A, C, T, G, N only)...",
+                lines=5,
+                info="Maximum length: 10,000 nucleotides"
+            )
+            
+            with gr.Row():
+                example_btn = gr.Button("📋 Load Example", size="sm")
+                clear_btn = gr.Button("🗑️ Clear", size="sm")
+                predict_btn = gr.Button("🔬 Predict Genes", variant="primary")
+            
+            # Ground truth section (optional)
+            with gr.Accordion("🎯 Ground Truth (Optional)", open=False):
+                gr.Markdown("*Provide ground truth data to calculate accuracy metrics*")
                 
                 with gr.Row():
-                    example_btn = gr.Button("📋 Load Example", variant="secondary", size="sm")
-                    clear_btn = gr.Button("🗑️ Clear", variant="secondary", size="sm")
-                    predict_btn = gr.Button("🔬 Predict Genes", variant="primary")
-                
-                # Ground truth section (optional)
-                with gr.Accordion("🎯 Ground Truth (Optional)", open=False):
-                    gr.Markdown("*Provide ground truth data to calculate accuracy metrics*")
-                    
-                    with gr.Row():
-                        gt_start = gr.Number(
-                            label="Start Position",
-                            precision=0,
-                            value=None,
-                            minimum=0
-                        )
-                        gt_end = gr.Number(
-                            label="End Position", 
-                            precision=0,
-                            value=None,
-                            minimum=0
-                        )
-                    
-                    gt_labels = gr.Textbox(
-                        label="OR: Labels (comma-separated 0s and 1s)",
-                        placeholder="0,0,1,1,1,0,0...",
-                        lines=2,
-                        info="Alternative to start/end positions"
+                    gt_start = gr.Number(
+                        label="Start Position",
+                        precision=0,
+                        minimum=0
+                    )
+                    gt_end = gr.Number(
+                        label="End Position", 
+                        precision=0,
+                        minimum=0
                     )
-            
-            with gr.Column(scale=3):
-                # Output section
-                gr.Markdown("## 🔬 Prediction Results")
                 
-                regions_output = gr.Markdown(
-                    value="*Results will appear here after prediction...*"
+                gt_labels = gr.Textbox(
+                    label="OR: Labels (comma-separated 0s and 1s)",
+                    placeholder="0,0,1,1,1,0,0...",
+                    info="Alternative to start/end positions"
                 )
-                
-                metrics_output = gr.Markdown(
-                    value="*Metrics will appear here if ground truth is provided...*"
+            
+            # Output section
+            gr.Markdown("## 🔬 Prediction Results")
+            
+            regions_output = gr.Markdown(
+                value="*Results will appear here after prediction...*"
+            )
+            
+            metrics_output = gr.Markdown(
+                value="*Metrics will appear here if ground truth is provided...*"
+            )
+            
+            # Detailed JSON output (collapsible)
+            with gr.Accordion("📄 Detailed JSON Output", open=False):
+                json_output = gr.Code(
+                    language="json",
+                    value="{}",
+                    lines=10
                 )
-                
-                # Detailed JSON output (collapsible)
-                with gr.Accordion("📄 Detailed JSON Output", open=False):
-                    json_output = gr.Code(
-                        language="json",
-                        value="{}",
-                        lines=15
-                    )
+            
+            # Event handlers
+            example_btn.click(
+                fn=load_example_sequence,
+                outputs=sequence_input
+            )
+            
+            clear_btn.click(
+                fn=clear_inputs,
+                outputs=[sequence_input, gt_start, gt_end, gt_labels]
+            )
+            
+            predict_btn.click(
+                fn=predict_gene_regions,
+                inputs=[sequence_input, gt_labels, gt_start, gt_end],
+                outputs=[regions_output, metrics_output, json_output]
+            )
+            
+            # Also trigger prediction on Enter in the sequence box
+            sequence_input.submit(
+                fn=predict_gene_regions,
+                inputs=[sequence_input, gt_labels, gt_start, gt_end],
+                outputs=[regions_output, metrics_output, json_output]
+            )
+            
+            # Footer
+            gr.Markdown("""
+            ---
+            **Model Info:** Boundary-aware gene prediction using multi-task deep learning  
+            **Supported:** DNA sequences with A, C, T, G, N nucleotides  
+            **Output:** Gene regions with start/end positions, codons, and confidence scores
+            """)
         
-        # Event handlers
-        example_btn.click(
-            fn=load_example_sequence,
-            outputs=sequence_input
-        )
+        return interface
         
-        clear_btn.click(
-            fn=clear_inputs,
-            outputs=[sequence_input, gt_start, gt_end, gt_labels]
-        )
+    except Exception as e:
+        print(f"❌ Error creating interface: {e}")
+        print(f"Full traceback: {traceback.format_exc()}")
         
-        predict_btn.click(
-            fn=predict_gene_regions,
-            inputs=[sequence_input, gt_labels, gt_start, gt_end],
-            outputs=[regions_output, metrics_output, json_output]
+        # Create a minimal fallback interface
+        fallback_interface = gr.Interface(
+            fn=lambda x: f"Error: {str(e)}",
+            inputs=gr.Textbox(label="DNA Sequence"),
+            outputs=gr.Textbox(label="Output"),
+            title="Gene Prediction Tool - Error Recovery Mode"
         )
+        return fallback_interface
+
+# Main execution
+if __name__ == "__main__":
+    try:
+        print("Creating Gradio interface...")
+        interface = create_interface()
+        print("✅ Gradio interface created successfully")
         
-        # Also trigger prediction on Enter in the sequence box
-        sequence_input.submit(
-            fn=predict_gene_regions,
-            inputs=[sequence_input, gt_labels, gt_start, gt_end],
-            outputs=[regions_output, metrics_output, json_output]
+        # Launch the interface
+        # Remove custom CSS and use default settings for HF Spaces
+        interface.launch(
+            server_name="0.0.0.0",  # Important for HF Spaces
+            server_port=7860,       # Standard port for HF Spaces
+            share=False,            # Don't create public link on HF Spaces
+            debug=False,            # Turn off debug mode for production
+            show_error=True         # Show errors in interface
         )
         
-        # Footer
-        gr.Markdown("""
-        ---
-        **Model Info:** Boundary-aware gene prediction using multi-task deep learning  
-        **Supported:** DNA sequences with A, C, T, G, N nucleotides  
-        **Output:** Gene regions with start/end positions, codons, and confidence scores
-        """)
-    
-    return interface
-
-# Create and launch the interface
-try:
-    interface = create_interface()
-    print("✅ Gradio interface created successfully")
-    
-    # Launch the interface
-    # For Hugging Face Spaces, this will start the app
-    # For local development, you can add debug=True
-    interface.launch()
-    
-except Exception as e:
-    print(f"❌ Failed to create Gradio interface: {e}")
-    print(f"Full traceback: {traceback.format_exc()}")
-    raise
+    except Exception as e:
+        print(f"❌ Failed to launch interface: {e}")
+        print(f"Full traceback: {traceback.format_exc()}")
+        
+        # Last resort - create the simplest possible interface
+        try:
+            simple_interface = gr.Interface(
+                fn=lambda x: "Simple interface loaded - main interface failed",
+                inputs=gr.Textbox(label="Test Input"),
+                outputs=gr.Textbox(label="Test Output"),
+                title="Gene Prediction Tool - Minimal Mode"
+            )
+            simple_interface.launch()
+        except Exception as e2:
+            print(f"❌ Even simple interface failed: {e2}")
+            raise