Update app.py

app.py

@@ -4,9 +4,10 @@ import numpy as np
 import os
 import traceback
 import logging
+import sys
 
 # Configure logging
-logging.basicConfig(level=logging.INFO)
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 logger = logging.getLogger(__name__)
 
 print("=== Gene Prediction App Starting ===")
@@ -14,6 +15,7 @@ print(f"Working directory: {os.getcwd()}")
 print(f"Available files: {os.listdir('.')}")
 print(f"PyTorch version: {torch.__version__}")
 print(f"Gradio version: {gr.__version__}")
+print(f"Python path: {sys.path}")
 
 # Global variables
 predictor = None
@@ -26,29 +28,55 @@ def initialize_model():
     
     try:
         print("Attempting to import predictor...")
-        from predictor import GenePredictor
-        print("✅ Predictor imported successfully")
         
-        model_path = 'best_boundary_aware_model.pth'
-        print(f"Looking for model file: {model_path}")
+        # Try different import approaches
+        try:
+            from predictor import GenePredictor
+            print("✅ Imported from predictor module")
+        except ImportError:
+            try:
+                # If predictor.py is in the same directory
+                import importlib.util
+                spec = importlib.util.spec_from_file_location("predictor", "predictor.py")
+                predictor_module = importlib.util.module_from_spec(spec)
+                spec.loader.exec_module(predictor_module)
+                GenePredictor = predictor_module.GenePredictor
+                print("✅ Imported predictor.py directly")
+            except Exception as e:
+                print(f"Failed to import predictor: {e}")
+                raise ImportError(f"Could not import GenePredictor: {e}")
         
-        if not os.path.exists(model_path):
-            error_message = f"❌ Model file '{model_path}' not found in directory"
+        # Look for model file
+        possible_model_paths = [
+            'best_boundary_aware_model.pth',
+            'model/best_boundary_aware_model.pth',
+            './best_boundary_aware_model.pth'
+        ]
+        
+        model_path = None
+        for path in possible_model_paths:
+            if os.path.exists(path):
+                model_path = path
+                break
+        
+        if not model_path:
+            available_models = [f for f in os.listdir('.') if f.endswith('.pth')]
+            if os.path.exists('model'):
+                available_models.extend([f"model/{f}" for f in os.listdir('model') if f.endswith('.pth')])
+            
+            error_message = f"❌ Model file not found. Searched: {possible_model_paths}. Available: {available_models}"
             print(error_message)
-            print(f"Available files: {[f for f in os.listdir('.') if f.endswith('.pth')]}")
             return False
             
-        print(f"Model file found. File size: {os.path.getsize(model_path)} bytes")
+        print(f"Found model file: {model_path}")
+        print(f"Model file size: {os.path.getsize(model_path)} bytes")
         
+        # Initialize predictor
         predictor = GenePredictor(model_path=model_path)
         model_loaded = True
         print("✅ Model initialized successfully")
         return True
         
-    except ImportError as e:
-        error_message = f"❌ Failed to import predictor: {str(e)}"
-        print(error_message)
-        return False
     except Exception as e:
         error_message = f"❌ Model initialization failed: {str(e)}"
         print(error_message)
@@ -61,13 +89,15 @@ def predict_genes(sequence):
     try:
         # Check if model is loaded
         if not model_loaded or predictor is None:
-            return f"🚫 **Model Error**\n\n{error_message}\n\nPlease check the logs for more details."
+            return f"🚫 **Model Error**\n\n{error_message}\n\nPlease check that:\n1. predictor.py is in the same directory\n2. Model file (.pth) exists\n3. All dependencies are installed"
         
         # Input validation
         if not sequence or not sequence.strip():
             return "⚠️ **Input Error**\n\nPlease enter a DNA sequence."
         
-        sequence = sequence.strip().upper().replace(' ', '').replace('\n', '').replace('\t', '')
+        # Clean sequence
+        sequence = sequence.strip().upper()
+        sequence = sequence.replace(' ', '').replace('\n', '').replace('\t', '').replace('\r', '')
         
         # Character validation
         valid_chars = set('ATCGN')
@@ -80,7 +110,7 @@ def predict_genes(sequence):
             return f"⚠️ **Sequence Too Short**\n\nMinimum length: 3 nucleotides\nYour sequence: {len(sequence)} nucleotides"
         
         if len(sequence) > 10000:
-            return f"⚠️ **Sequence Too Long**\n\nMaximum length: 10,000 nucleotides\nYour sequence: {len(sequence)} nucleotides"
+            return f"⚠️ **Sequence Too Long**\n\nMaximum length: 10,000 nucleotides\nYour sequence: {len(sequence)} nucleotides\n\nFor longer sequences, consider splitting them into smaller chunks."
         
         print(f"Processing sequence of length: {len(sequence)}")
         
@@ -89,125 +119,318 @@ def predict_genes(sequence):
         regions = predictor.extract_gene_regions(predictions, sequence)
         
         # Format results
-        if not regions:
-            return f"🔍 **No Gene Regions Detected**\n\nSequence length: {len(sequence)} bp\nConfidence: {confidence:.3f}\n\nThe model did not detect any gene regions in this sequence."
-        
         result = f"🧬 **Gene Prediction Results**\n\n"
-        result += f"📊 **Summary:**\n"
-        result += f"• Found: {len(regions)} gene region(s)\n"
-        result += f"• Sequence length: {len(sequence)} bp\n"
-        result += f"• Overall confidence: {confidence:.3f}\n\n"
+        result += f"📊 **Analysis Summary:**\n"
+        result += f"• Sequence length: {len(sequence):,} bp\n"
+        result += f"• Gene regions found: {len(regions)}\n"
+        result += f"• Overall confidence: {confidence:.3f}\n"
+        result += f"• Analysis completed successfully ✅\n\n"
+        
+        if not regions:
+            result += f"🔍 **No Gene Regions Detected**\n\n"
+            result += f"The model did not detect any gene regions meeting the minimum criteria in this sequence.\n"
+            result += f"This could mean:\n"
+            result += f"• The sequence may not contain protein-coding genes\n"
+            result += f"• Genes may be partial or fragmented\n"
+            result += f"• The sequence may be non-coding DNA\n"
+            return result
         
-        result += f"📍 **Detected Regions:**\n\n"
+        result += f"📍 **Detected Gene Regions:**\n\n"
         
+        total_gene_length = 0
         for i, region in enumerate(regions, 1):
-            result += f"**Region {i}:**\n"
-            result += f"• Position: {region['start']:,} - {region['end']:,}\n"
-            result += f"• Length: {region['length']:,} bp\n"
+            result += f"**🧬 Gene Region {i}:**\n"
+            result += f"├─ Position: {region['start']:,} - {region['end']:,} bp\n"
+            result += f"├─ Length: {region['length']:,} bp\n"
+            result += f"├─ In-frame: {'Yes' if region.get('in_frame', False) else 'No'}\n"
+            
+            # Start codon info
+            start_codon = region.get('start_codon')
+            if start_codon:
+                result += f"├─ Start codon: {start_codon}\n"
+            else:
+                result += f"├─ Start codon: Not detected\n"
+            
+            # Stop codon info
+            stop_codon = region.get('stop_codon')
+            if stop_codon:
+                result += f"├─ Stop codon: {stop_codon}\n"
+            else:
+                result += f"├─ Stop codon: Not detected\n"
             
             # Sequence preview
             seq = region.get('sequence', '')
             if seq:
-                if len(seq) <= 100:
-                    result += f"• Sequence: `{seq}`\n"
+                if len(seq) <= 120:
+                    result += f"└─ Sequence: `{seq}`\n"
                 else:
-                    preview = seq[:50] + '...' + seq[-50:]
-                    result += f"• Preview: `{preview}`\n"
+                    preview = seq[:60] + '...' + seq[-60:]
+                    result += f"└─ Sequence: `{preview}`\n"
             
+            total_gene_length += region['length']
             result += "\n"
         
+        # Summary statistics
+        result += f"📈 **Statistics:**\n"
+        result += f"• Total gene content: {total_gene_length:,} bp ({total_gene_length/len(sequence)*100:.1f}% of sequence)\n"
+        result += f"• Average gene length: {total_gene_length//len(regions):,} bp\n"
+        result += f"• Gene density: {len(regions)/(len(sequence)/1000):.2f} genes per kb\n"
+        
         return result
         
     except Exception as e:
-        error_msg = f"🚫 **Prediction Error**\n\nAn error occurred during prediction:\n```\n{str(e)}\n```"
+        error_msg = f"🚫 **Prediction Error**\n\n"
+        error_msg += f"An error occurred during prediction:\n\n"
+        error_msg += f"```\n{str(e)}\n```\n\n"
+        error_msg += f"**Troubleshooting:**\n"
+        error_msg += f"• Check that predictor.py is in the same directory\n"
+        error_msg += f"• Verify model file exists and is not corrupted\n"
+        error_msg += f"• Ensure sequence contains only valid DNA characters\n"
+        
         print(f"Prediction error: {e}")
         traceback.print_exc()
         return error_msg
 
+def get_sequence_stats(sequence):
+    """Get basic statistics about the input sequence"""
+    if not sequence or not sequence.strip():
+        return ""
+    
+    sequence = sequence.strip().upper().replace(' ', '').replace('\n', '').replace('\t', '')
+    
+    if not sequence:
+        return ""
+    
+    stats = f"**Sequence Info:** {len(sequence)} bp"
+    
+    # Base composition
+    a_count = sequence.count('A')
+    t_count = sequence.count('T')
+    c_count = sequence.count('C')
+    g_count = sequence.count('G')
+    n_count = sequence.count('N')
+    
+    total_valid = a_count + t_count + c_count + g_count
+    if total_valid > 0:
+        gc_content = (c_count + g_count) / total_valid * 100
+        stats += f" | GC: {gc_content:.1f}%"
+    
+    if n_count > 0:
+        stats += f" | N's: {n_count}"
+    
+    return stats
+
 # Initialize model on startup
 print("Initializing model...")
 model_status = initialize_model()
 
-# Create interface
+# Create custom CSS for better styling
+custom_css = """
+.gene-app {
+    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+}
+
+.status-ready {
+    background: linear-gradient(135deg, #d4edda 0%, #c3e6cb 100%);
+    border: 2px solid #28a745;
+    border-radius: 10px;
+    padding: 15px;
+    color: #155724;
+    font-weight: bold;
+    box-shadow: 0 2px 10px rgba(40, 167, 69, 0.2);
+}
+
+.status-error {
+    background: linear-gradient(135deg, #f8d7da 0%, #f5c6cb 100%);
+    border: 2px solid #dc3545;
+    border-radius: 10px;
+    padding: 15px;
+    color: #721c24;
+    font-weight: bold;
+    box-shadow: 0 2px 10px rgba(220, 53, 69, 0.2);
+}
+
+.main-title {
+    text-align: center;
+    background: linear-gradient(135deg, #2E8B57 0%, #20B2AA 100%);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    background-clip: text;
+    font-size: 2.5rem;
+    font-weight: bold;
+    margin-bottom: 1rem;
+}
+
+.instructions {
+    background: #f8f9fa;
+    border-radius: 10px;
+    padding: 20px;
+    border-left: 4px solid #2E8B57;
+    margin: 1rem 0;
+}
+
+.sequence-stats {
+    font-size: 0.9rem;
+    color: #6c757d;
+    font-style: italic;
+    margin-top: 5px;
+}
+"""
+
+# Create the interface
 print("Creating Gradio interface...")
 
-# Determine status message and color
+# Determine status message and styling
 if model_loaded:
-    status_html = '<div style="padding: 10px; background-color: #d4edda; border: 1px solid #c3e6cb; border-radius: 5px; color: #155724;"><strong>✅ Model Status:</strong> Ready for predictions!</div>'
+    status_html = '''
+    <div class="status-ready">
+        <strong>✅ Model Status:</strong> Ready for gene prediction!<br>
+        <small>🔬 Boundary-aware deep learning model loaded successfully</small>
+    </div>
+    '''
 else:
-    status_html = f'<div style="padding: 10px; background-color: #f8d7da; border: 1px solid #f5c6cb; border-radius: 5px; color: #721c24;"><strong>❌ Model Status:</strong> {error_message}</div>'
+    status_html = f'''
+    <div class="status-error">
+        <strong>❌ Model Status:</strong> Model initialization failed<br>
+        <small>📋 Details: {error_message}</small>
+    </div>
+    '''
 
-# Example sequence
-example_sequence = "ATGAAACGCATTAGCACCACCATTACCACCACCATCACCATTACCACAGGTAACGGTGCGGGCTGACGCGTACAGGAAACACAGAAAAAAGCCCGCACCTGACAGTGCGGGCTTTTTTTTTCGACCAAAGGTAACGAGGTAACAACCATGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGCAAATGCAGAACGTTTTCTGCG"
+# Example sequences
+examples = [
+    # Short example with clear gene
+    ["ATGAAACGCATTAGCACCACCATTACCACCACCATCACCATTACCACAGGTAACGGTGCGGGCTGACGCGTACAGGAAACACAGAAAAAAGCCCGCACCTGACAGTGCGGGCTTTTTTTTTCGACCAAAGGTAACGAGGTAACAACCATGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGCAAATGCAGAACGTTTTCTGCGTAA"],
+    # Longer example
+    ["ATGAAACGCATTAGCACCACCATTACCACCACCATCACCATTACCACAGGTAACGGTGCGGGCTGACGCGTACAGGAAACACAGAAAAAAGCCCGCACCTGACAGTGCGGGCTTTTTTTTTCGACCAAAGGTAACGAGGTAACAACCATGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGCAAATGCAGAACGTTTTCTGCGTGTTGCCGATATTCTGGAAAGCAATGCCAGGCAGGGGCAGGTGGCCACCGTCCTCTCTGCCCCCGCCAAAATCACCAACCACCTGGTGGCGATGATTGAAAAAACCATTAGCGGCCAGGATGCTTTACCCAATATCAGCGATGCCGAACGTATTTTTGCCGAACTTTTGACGGGACTCGCCGCCGCCCAGCCGGGGTTCCCGCTGGCGCAATTGAAAACTTTCGTCGATCAGGAATTTGCCCAATAG"],
+]
 
-with gr.Blocks(title="🧬 Gene Prediction Tool", theme=gr.themes.Soft()) as demo:
-    gr.HTML("<h1 style='text-align: center; color: #2E8B57;'>🧬 Gene Prediction Tool</h1>")
+# Create the interface with custom theme
+with gr.Blocks(
+    title="🧬 Gene Prediction Tool", 
+    theme=gr.themes.Soft(primary_hue="emerald", secondary_hue="teal"),
+    css=custom_css,
+    head="<meta name='viewport' content='width=device-width, initial-scale=1.0'>"
+) as demo:
     
-    gr.HTML(status_html)
+    gr.HTML('<h1 class="main-title">🧬 Advanced Gene Prediction Tool</h1>')
+    gr.HTML('<p style="text-align: center; font-size: 1.1rem; color: #6c757d; margin-bottom: 2rem;">AI-powered boundary-aware gene detection system</p>')
     
-    gr.Markdown("""
-    ### Instructions:
-    1. **Enter a DNA sequence** using only A, T, C, G, N characters
-    2. **Click Submit** to analyze the sequence
-    3. **View results** showing predicted gene regions with positions and confidence scores
+    gr.HTML(status_html)
     
-    **Sequence Requirements:**
-    - Only A, T, C, G, N characters allowed
-    - Minimum length: 3 nucleotides  
-    - Maximum length: 10,000 nucleotides
-    """)
+    with gr.Row():
+        gr.HTML('''
+        <div class="instructions">
+            <h3>🔬 How to Use:</h3>
+            <ol>
+                <li><strong>Enter DNA sequence:</strong> Paste your sequence using A, T, C, G, N characters</li>
+                <li><strong>Click Analyze:</strong> The AI model will predict gene regions</li>
+                <li><strong>Review results:</strong> View detected genes with positions, codons, and confidence</li>
+            </ol>
+            
+            <h4>📏 Requirements:</h4>
+            <ul>
+                <li>Characters: Only A, T, C, G, N allowed</li>
+                <li>Length: 3 - 10,000 nucleotides</li>
+                <li>Format: Raw sequence (FASTA headers will be ignored)</li>
+            </ul>
+        </div>
+        ''')
     
     with gr.Row():
-        with gr.Column(scale=2):
+        with gr.Column(scale=1):
             sequence_input = gr.Textbox(
-                label="DNA Sequence",
-                placeholder="Enter DNA sequence (A, T, C, G, N)...",
-                lines=8,
-                max_lines=15
+                label="🧬 DNA Sequence Input",
+                placeholder="Enter or paste your DNA sequence here...\nExample: ATGAAACGCATTAGCACC...",
+                lines=10,
+                max_lines=20,
+                show_copy_button=True,
+                container=True
             )
             
+            # Real-time sequence stats
+            sequence_stats = gr.HTML(value="", elem_classes=["sequence-stats"])
+            
+            with gr.Row():
+                submit_btn = gr.Button("🔬 Analyze Sequence", variant="primary", size="lg", scale=2)
+                clear_btn = gr.Button("🗑️ Clear", variant="secondary", size="lg", scale=1)
+            
+            # Example buttons
+            gr.Markdown("### 📝 Quick Examples:")
             with gr.Row():
-                submit_btn = gr.Button("🔬 Analyze Sequence", variant="primary", size="lg")
-                clear_btn = gr.Button("🗑️ Clear", variant="secondary")
-                example_btn = gr.Button("📝 Load Example", variant="secondary")
+                example1_btn = gr.Button("Short Gene", variant="secondary", size="sm")
+                example2_btn = gr.Button("Longer Sequence", variant="secondary", size="sm")
         
-        with gr.Column(scale=3):
+        with gr.Column(scale=2):
             output = gr.Textbox(
-                label="Prediction Results",
-                lines=20,
-                max_lines=30,
-                show_copy_button=True
+                label="🔬 Analysis Results",
+                lines=25,
+                max_lines=35,
+                show_copy_button=True,
+                container=True,
+                placeholder="Results will appear here after analysis..."
             )
     
+    # Footer
+    gr.HTML('''
+    <div style="text-align: center; margin-top: 2rem; padding: 1rem; border-top: 1px solid #dee2e6; color: #6c757d;">
+        <small>🧬 Powered by boundary-aware deep learning | Built with PyTorch & Gradio</small>
+    </div>
+    ''')
+    
     # Event handlers
+    def update_stats(sequence):
+        return get_sequence_stats(sequence)
+    
+    # Real-time sequence stats update
+    sequence_input.change(
+        fn=update_stats,
+        inputs=sequence_input,
+        outputs=sequence_stats
+    )
+    
+    # Main prediction
     submit_btn.click(
         fn=predict_genes,
         inputs=sequence_input,
         outputs=output
     )
     
+    # Clear functionality
     clear_btn.click(
-        fn=lambda: ("", ""),
-        outputs=[sequence_input, output]
+        fn=lambda: ("", "", ""),
+        outputs=[sequence_input, output, sequence_stats]
     )
     
-    example_btn.click(
-        fn=lambda: example_sequence,
+    # Example buttons
+    example1_btn.click(
+        fn=lambda: examples[0][0],
         outputs=sequence_input
     )
     
-    # Also allow Enter key to submit
+    example2_btn.click(
+        fn=lambda: examples[1][0],
+        outputs=sequence_input
+    )
+    
+    # Allow Enter key to submit
     sequence_input.submit(
         fn=predict_genes,
         inputs=sequence_input,
         outputs=output
     )
 
+# Launch configuration
 if __name__ == "__main__":
-    print("Launching Gradio app...")
+    print("🚀 Launching Gene Prediction App...")
+    print(f"Model loaded: {model_loaded}")
+    print(f"Open your browser to see the interface")
+    
     demo.launch(
         server_name="0.0.0.0",
         server_port=7860,
         show_error=True,
-        show_api=False
+        show_api=False,
+        share=False,  # Set to True if you want a public link
+        inbrowser=True,  # Automatically open browser
+        prevent_thread_lock=False
     )