Update app.py

app.py

@@ -1,134 +1,213 @@
 import gradio as gr
 import torch
 import numpy as np
-import json
-from typing import Optional, List, Dict, Tuple
-import logging
 import os
 import traceback
+import logging
 
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Debug info
-print("=== Debug Info ===")
+print("=== Gene Prediction App Starting ===")
 print(f"Working directory: {os.getcwd()}")
-print(f"Files: {os.listdir('.')}")
+print(f"Available files: {os.listdir('.')}")
 print(f"PyTorch version: {torch.__version__}")
-print("==================")
+print(f"Gradio version: {gr.__version__}")
 
-# Initialize predictor
+# Global variables
 predictor = None
-initialization_error = None
+model_loaded = False
+error_message = ""
 
-try:
-    from predictor import GenePredictor
-    model_path = 'best_boundary_aware_model.pth'
+def initialize_model():
+    """Initialize the model with proper error handling"""
+    global predictor, model_loaded, error_message
     
-    if os.path.exists(model_path):
+    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}")
+        
+        if not os.path.exists(model_path):
+            error_message = f"❌ Model file '{model_path}' not found in directory"
+            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")
+        
         predictor = GenePredictor(model_path=model_path)
-        print("✅ Model loaded successfully")
-    else:
-        initialization_error = f"Model file {model_path} not found"
-        print(f"❌ {initialization_error}")
-        
-except Exception as e:
-    initialization_error = str(e)
-    print(f"❌ Error: {e}")
+        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)
+        print("Full traceback:")
+        traceback.print_exc()
+        return False
 
 def predict_genes(sequence):
-    """Simple prediction function"""
+    """Gene prediction function with comprehensive error handling"""
     try:
-        if not predictor:
-            return f"❌ Model not loaded: {initialization_error}"
+        # 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."
         
+        # Input validation
         if not sequence or not sequence.strip():
-            return "❌ Please enter a DNA sequence"
+            return "⚠️ **Input Error**\n\nPlease enter a DNA sequence."
         
-        sequence = sequence.strip().upper()
+        sequence = sequence.strip().upper().replace(' ', '').replace('\n', '').replace('\t', '')
         
-        # Validate sequence
-        valid_chars = set('ACTGN')
-        if not set(sequence).issubset(valid_chars):
-            return "❌ Invalid characters. Use only A, C, T, G, N"
+        # 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 "❌ Sequence too short (minimum 3 nucleotides)"
+            return f"⚠️ **Sequence Too Short**\n\nMinimum length: 3 nucleotides\nYour sequence: {len(sequence)} nucleotides"
         
         if len(sequence) > 10000:
-            return "❌ Sequence too long (maximum 10,000 nucleotides)"
+            return f"⚠️ **Sequence Too Long**\n\nMaximum length: 10,000 nucleotides\nYour sequence: {len(sequence)} nucleotides"
+        
+        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 output
+        # Format results
         if not regions:
-            return f"🔍 No gene regions detected (Confidence: {confidence:.3f})"
+            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"🧬 Found {len(regions)} gene region(s) (Confidence: {confidence:.3f})\n\n"
+        result += f"📍 **Detected Regions:**\n\n"
         
         for i, region in enumerate(regions, 1):
-            result += f"**Gene {i}:**\n"
-            result += f"  • Position: {region['start']} - {region['end']}\n"
-            result += f"  • Length: {region['length']} bp\n"
+            result += f"**Region {i}:**\n"
+            result += f"• Position: {region['start']:,} - {region['end']:,}\n"
+            result += f"• Length: {region['length']:,} bp\n"
             
-            # Safe sequence preview
+            # Sequence preview
             seq = region.get('sequence', '')
             if seq:
-                preview = seq[:60] + ('...' if len(seq) > 60 else '')
-                result += f"  • Preview: {preview}\n\n"
-            else:
-                result += f"  • Preview: Not available\n\n"
+                if len(seq) <= 100:
+                    result += f"• Sequence: `{seq}`\n"
+                else:
+                    preview = seq[:50] + '...' + seq[-50:]
+                    result += f"• Preview: `{preview}`\n"
+            
+            result += "\n"
         
         return result
         
     except Exception as e:
+        error_msg = f"🚫 **Prediction Error**\n\nAn error occurred during prediction:\n```\n{str(e)}\n```"
         print(f"Prediction error: {e}")
-        return f"❌ Prediction failed: {str(e)}"
+        traceback.print_exc()
+        return error_msg
+
+# Initialize model on startup
+print("Initializing model...")
+model_status = initialize_model()
 
-def load_example():
-    """Load example DNA sequence"""
-    return "ATGAAACGCATTAGCACCACCATTACCACCACCATCACCATTACCACAGGTAACGGTGCGGGCTGACGCGTACAGGAAACACAGAAAAAAGCCCGCACCTGACAGTGCGGGCTTTTTTTTTCGACCAAAGGTAACGAGGTAACAACCATGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGCAAATGCAGAACGTTTTCTGCGGGTTGCCGATATTCTGGAAAGCAATGCCAGGCAGGGGCAGGTGGCCACCGTCCTCTCTGCCCCCGCCAAAATCACCAACCACCTGGTGGCGATGATTGAAAAAACCATTAGCGGCCAGGATGCTTTACCCAATATCAGCGATGCCGAACGTATTTTTGCCGAACTTTTGACGGGACTCGCCGCCGCCCAGCCGGGGTTCCCGCTGGCGCAATTGAAAACTTTCGTCGATCAGGAATTTGCCCAAATAAAACATGTCCTGCATGGCATTAGTTTGTTGGGGCAGTGCCCGGATAGCATCAACGCTGCGCTGATTTGCCGTGGCGAGAAAATGTCGATCGCCATTATGGCCGGCGTATTAGAAGCGCGCGGTCACAACGTTACTGTTATCGATCCGGTCGAAAAACTGCTGGCAGTGGGGCATTACCTCGAATCTACCGTCGATATTGCTGAGTCCACCCGCCGTATTGCGGCAAGCCGCATTCCGGCTGATCACATGGTGCTGATGGCAGGTTTCACCGCCGGTAATGAAAAAGGCGAACTGGTGGTGCTTGGACGCAACGGTTCCGACTACTCTGCTGCGGTGCTGGCTGCCTGTTTACGCGCCGATTGTTGCGAGATTTGGACGGACGTTGACGGGGTCTATACCTGCGACCCGCGTCAGGTGCCCGATGCGAGGTTGTTGAAGTCGATGTCCTACCAGGAAGCGATGGAGCTTTCCTACTTCGGCGCTAAAGTTCTTCACCCCCGCACCATTACCCCCATCGCCCAGTTCCAGATCCCTTGCCTGATTAAAAATACCGGAAATCCTCAAGCACCAGGTACGCTCATTGGTGCCAGCCGTGATGAAGACGAATTACCGGTCAAGGGCATTTCCAATCTGAATAACATGGCAATGTTCAGCGTTTCCGGCCCGGGGATGAAAGGGATGGTCGGCATGGCGGCGCGCGTCTTTGCAGCGATGTCACGCGCCCGTATTTCCGTGGTGCTGATTACGCAATCATCTTCCGAATACAGCATCAGTTTCTGCGTTCCACAAAGCGACTGTGTGCGAGCTGAACGGGCAATGCAGGAAGAGTTCTACCTGGAACTGAAAGAAGGCTTACTGGAGCCGCTGGCAGTGACGGAACGGCTGGCCATTATCTCGGTGGTAGG"
+# Create interface
+print("Creating Gradio interface...")
 
-# Status message
-if predictor:
-    status_msg = "✅ Model loaded successfully!"
-    status_color = "green"
+# Determine status message and color
+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>'
 else:
-    status_msg = f"❌ Model loading failed: {initialization_error}"
-    status_color = "red"
+    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>'
+
+# Example sequence
+example_sequence = "ATGAAACGCATTAGCACCACCATTACCACCACCATCACCATTACCACAGGTAACGGTGCGGGCTGACGCGTACAGGAAACACAGAAAAAAGCCCGCACCTGACAGTGCGGGCTTTTTTTTTCGACCAAAGGTAACGAGGTAACAACCATGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGCAAATGCAGAACGTTTTCTGCG"
 
-# Try gr.Interface first (simpler approach)
-demo = gr.Interface(
-    fn=predict_genes,
-    inputs=gr.Textbox(
-        label="DNA Sequence", 
-        placeholder="Enter DNA sequence (A, C, T, G, N)...",
-        lines=6
-    ),
-    outputs=gr.Textbox(
-        label="Prediction Results", 
-        lines=15
-    ),
-    title="🧬 Gene Prediction Tool",
-    description=f"""
-    Predict gene regions in DNA sequences using deep learning.
+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>")
+    
+    gr.HTML(status_html)
+    
+    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
+    
+    **Sequence Requirements:**
+    - Only A, T, C, G, N characters allowed
+    - Minimum length: 3 nucleotides  
+    - Maximum length: 10,000 nucleotides
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            sequence_input = gr.Textbox(
+                label="DNA Sequence",
+                placeholder="Enter DNA sequence (A, T, C, G, N)...",
+                lines=8,
+                max_lines=15
+            )
+            
+            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")
+        
+        with gr.Column(scale=3):
+            output = gr.Textbox(
+                label="Prediction Results",
+                lines=20,
+                max_lines=30,
+                show_copy_button=True
+            )
+    
+    # Event handlers
+    submit_btn.click(
+        fn=predict_genes,
+        inputs=sequence_input,
+        outputs=output
+    )
+    
+    clear_btn.click(
+        fn=lambda: ("", ""),
+        outputs=[sequence_input, output]
+    )
     
-    **Status:** {status_msg}
+    example_btn.click(
+        fn=lambda: example_sequence,
+        outputs=sequence_input
+    )
     
-    **Instructions:**
-    1. Enter a DNA sequence using only A, C, T, G, N characters
-    2. Click Submit
-    3. View predicted gene regions with positions and confidence scores
-    """,
-    examples=[
-        ["ATGAAACGCATTAGCACCACCATTACCACCACCATCACCATTACCACAGGTAACGGTGCGGGCTGACGCGTACAGGAAACACAGAAAAAAGCCCGCACCTGACAGTGCGGGCTTTTTTTTTCGACCAAAGGTAACGAGGTAACAACCATGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGCAAATGCAGAACGTTTTCTGCGGGTTGCCGATATTCTGGAAAGCAATGCCAGGCAGGGGCAGGTGGCCACCGTCCTCTCTGCCCCCGCCAAAATCACCAACCACCTGGTGGCGATGATTGAAAAAACCATTAGCGGCCAGGATGCTTTACCCAATATCAGCGATGCCGAACGTATTTTTGCCGAACTTTTGACGGGACTCGCCGCCGCCCAGCCGGGGTTCCCGCTGGCGCAATTGAAAACTTTCGTCGATCAGGAATTTGCCCAAATAAAACATGTCCTGCATGGCATTAGTTTGTTGGGGCAGTGCCCGGATAGCATCAACGCTGCGCTGATTTGCCGTGGCGAGAAAATGTCGATCGCCATTATGGCCGGCGTATTAGAAGCGCGCGGTCACAACGTTACTGTTATCGATCCGGTCGAAAAACTGCTGGCAGTGGGGCATTACCTCGAATCTACCGTCGATATTGCTGAGTCCACCCGCCGTATTGCGGCAAGCCGCATTCCGGCTGATCACATGGTGCTGATGGCAGGTTTCACCGCCGGTAATGAAAAAGGCGAACTGGTGGTGCTTGGACGCAACGGTTCCGACTACTCTGCTGCGGTGCTGGCTGCCTGTTTACGCGCCGATTGTTGCGAGATTTGGACGGACGTTGACGGGGTCTATACCTGCGACCCGCGTCAGGTGCCCGATGCGAGGTTGTTGAAGTCGATGTCCTACCAGGAAGCGATGGAGCTTTCCTACTTCGGCGCTAAAGTTCTTCACCCCCGCACCATTACCCCCATCGCCCAGTTCCAGATCCCTTGCCTGATTAAAAATACCGGAAATCCTCAAGCACCAGGTACGCTCATTGGTGCCAGCCGTGATGAAGACGAATTACCGGTCAAGGGCATTTCCAATCTGAATAACATGGCAATGTTCAGCGTTTCCGGCCCGGGGATGAAAGGGATGGTCGGCATGGCGGCGCGCGTCTTTGCAGCGATGTCACGCGCCCGTATTTCCGTGGTGCTGATTACGCAATCATCTTCCGAATACAGCATCAGTTTCTGCGTTCCACAAAGCGACTGTGTGCGAGCTGAACGGGCAATGCAGGAAGAGTTCTACCTGGAACTGAAAGAAGGCTTACTGGAGCCGCTGGCAGTGACGGAACGGCTGGCCATTATCTCGGTGGTAGG"]
-    ],
-    allow_flagging="never"
-)
+    # Also allow Enter key to submit
+    sequence_input.submit(
+        fn=predict_genes,
+        inputs=sequence_input,
+        outputs=output
+    )
 
-# Launch the app
 if __name__ == "__main__":
-    demo.launch()
+    print("Launching Gradio app...")
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_error=True,
+        show_api=False
+    )