Update app.py

app.py

@@ -1,443 +1,18 @@
 import gradio as gr
-import torch
-import numpy as np
-import os
-import traceback
-import logging
-import sys
 
-# Configure logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-logger = logging.getLogger(__name__)
 
-print("=== Gene Prediction App Starting ===")
-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
-model_loaded = False
-error_message = ""
 
-def initialize_model():
-    """Initialize the model with proper error handling"""
-    global predictor, model_loaded, error_message
-    
-    try:
-        print("Attempting to import predictor...")
-        
-        # 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}")
-        
-        # 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)
-            return False
-            
-        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 Exception as e:
-        error_message = f"❌ Model initialization failed: {str(e)}"
-        print(error_message)
-        print("Full traceback:")
-        traceback.print_exc()
-        return False
 
-def predict_genes(sequence):
-    """Gene prediction function with comprehensive error handling"""
-    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 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."
-        
-        # Clean sequence
-        sequence = sequence.strip().upper()
-        sequence = sequence.replace(' ', '').replace('\n', '').replace('\t', '').replace('\r', '')
-        
-        # Character validation
-        valid_chars = set('ATCGN')
-        invalid_chars = set(sequence) - valid_chars
-        if invalid_chars:
-            return f"⚠️ **Invalid Characters**\n\nFound invalid characters: {', '.join(sorted(invalid_chars))}\n\nPlease use only: A, T, C, G, N"
-        
-        # Length validation
-        if len(sequence) < 3:
-            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\n\nFor longer sequences, consider splitting them into smaller chunks."
-        
-        print(f"Processing sequence of length: {len(sequence)}")
-        
-        # Make prediction
-        predictions, probs_dict, confidence = predictor.predict(sequence)
-        regions = predictor.extract_gene_regions(predictions, sequence)
-        
-        # Format results
-        result = f"🧬 **Gene Prediction Results**\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 Gene Regions:**\n\n"
-        
-        total_gene_length = 0
-        for i, region in enumerate(regions, 1):
-            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) <= 120:
-                    result += f"└─ Sequence: `{seq}`\n"
-                else:
-                    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\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
 
-def create_interface():
-    """Create the Gradio interface"""
-    # Initialize model on startup
-    print("Initializing model...")
-    model_status = initialize_model()
+def test_function(text):
+    return f"You entered: {text}"
 
-    # 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;
-    }
-    """
-
-    print("Creating Gradio interface...")
-
-    # Determine status message and styling
-    if model_loaded:
-        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 class="status-error">
-            <strong>❌ Model Status:</strong> Model initialization failed<br>
-            <small>📋 Details: {error_message}</small>
-        </div>
-        '''
-
-    # Example sequences
-    examples = [
-        # Short example with clear gene
-        ["ATGAAACGCATTAGCACCACCATTACCACCACCATCACCATTACCACAGGTAACGGTGCGGGCTGACGCGTACAGGAAACACAGAAAAAAGCCCGCACCTGACAGTGCGGGCTTTTTTTTTCGACCAAAGGTAACGAGGTAACAACCATGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGCAAATGCAGAACGTTTTCTGCGTAA"],
-        # Longer example
-        ["ATGAAACGCATTAGCACCACCATTACCACCACCATCACCATTACCACAGGTAACGGTGCGGGCTGACGCGTACAGGAAACACAGAAAAAAGCCCGCACCTGACAGTGCGGGCTTTTTTTTTCGACCAAAGGTAACGAGGTAACAACCATGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGCAAATGCAGAACGTTTTCTGCGTGTTGCCGATATTCTGGAAAGCAATGCCAGGCAGGGGCAGGTGGCCACCGTCCTCTCTGCCCCCGCCAAAATCACCAACCACCTGGTGGCGATGATTGAAAAAACCATTAGCGGCCAGGATGCTTTACCCAATATCAGCGATGCCGAACGTATTTTTGCCGAACTTTTGACGGGACTCGCCGCCGCCCAGCCGGGGTTCCCGCTGGCGCAATTGAAAACTTTCGTCGATCAGGAATTTGCCCAATAG"],
-    ]
-
-    # 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
-    ) as interface:
-        
-        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.HTML(status_html)
-        
-        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=1):
-                sequence_input = gr.Textbox(
-                    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():
-                    example1_btn = gr.Button("Short Gene", variant="secondary", size="sm")
-                    example2_btn = gr.Button("Longer Sequence", variant="secondary", size="sm")
-            
-            with gr.Column(scale=2):
-                output = gr.Textbox(
-                    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, sequence_stats]
-        )
-        
-        # Example buttons
-        example1_btn.click(
-            fn=lambda: examples[0][0],
-            outputs=sequence_input
-        )
-        
-        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
-        )
-    
-    return interface
-
-
-# Create and launch the interface
-if __name__ == "__main__":
-    print("🚀 Launching Gene Prediction App...")
-    
-    # Create the interface
-    demo = create_interface()
-    
-    print(f"Model loaded: {model_loaded}")
-    print(f"Open your browser to see the interface")
-    
-    # Launch with Hugging Face Spaces compatible settings
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False,
-        debug=False,
-        show_error=True,
-        quiet=False
-    )
+# Simple test interface
+with gr.Blocks(title="Test App") as demo:
+    gr.Markdown("# Test Interface")
+    with gr.Row():
+        input_box = gr.Textbox(label="Input")
+        output_box = gr.Textbox(label="Output")