Update app.py

app.py

@@ -10,8 +10,8 @@ from pathlib import Path
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# Configuration - Use current directory for model files
-MODEL_DIR = "."  # Changed from "./adaptive_aphasia_model"
+# Configuration
+MODEL_DIR = "."
 SUPPORTED_AUDIO_FORMATS = [".mp3", ".mp4", ".wav", ".m4a", ".flac", ".ogg"]
 
 def safe_import_modules():
@@ -55,31 +55,9 @@ def safe_import_modules():
 # Import modules
 MODULES = safe_import_modules()
 
-def check_model_files():
-    """Check if required model files exist"""
-    required_files = [
-        "pytorch_model.bin",
-        "config.json",
-        "tokenizer.json",
-        "tokenizer_config.json"
-    ]
-    
-    missing_files = []
-    for file in required_files:
-        if not os.path.exists(os.path.join(MODEL_DIR, file)):
-            missing_files.append(file)
-    
-    if missing_files:
-        logger.error(f"Missing model files: {missing_files}")
-        return False, missing_files
-    
-    logger.info("✓ All required model files found")
-    return True, []
-
 def run_complete_pipeline(audio_file_path: str) -> dict:
     """Complete pipeline: Audio → WAV → CHA → JSON → Model Prediction"""
     
-    # Check if all modules are available
     if not all(MODULES.values()):
         missing = [k for k, v in MODULES.items() if v is None]
         return {
@@ -140,15 +118,13 @@ def process_audio_input(audio_file):
         if audio_file is None:
             return "❌ Error: No audio file uploaded"
         
-        # Check if pipeline is available
         if not all(MODULES.values()):
             return "❌ Error: Audio processing pipeline not available. Missing required modules."
         
-        # Check file format
-        file_path = audio_file
-        if hasattr(audio_file, 'name'):
-            file_path = audio_file.name
+        # Get file path
+        file_path = audio_file.name if hasattr(audio_file, 'name') else str(audio_file)
         
+        # Check file format
         file_ext = Path(file_path).suffix.lower()
         if file_ext not in SUPPORTED_AUDIO_FORMATS:
             return f"❌ Error: Unsupported file format {file_ext}. Supported: {', '.join(SUPPORTED_AUDIO_FORMATS)}"
@@ -184,8 +160,7 @@ def process_audio_input(audio_file):
             prob_dist = first_pred["probability_distribution"]
             top_3 = list(prob_dist.items())[:3]
             
-            result_text = f"""
-🧠 **APHASIA CLASSIFICATION RESULTS**
+            result_text = f"""🧠 **APHASIA CLASSIFICATION RESULTS**
 
 🎯 **Primary Classification:** {predicted_class}
 📊 **Confidence:** {confidence}
@@ -207,11 +182,9 @@ def process_audio_input(audio_file):
 📊 **Processing Summary:**
 • Total sentences analyzed: {results.get('total_sentences', 'N/A')}
 • Average confidence: {results.get('summary', {}).get('average_confidence', 'N/A')}
-• Average fluency: {results.get('summary', {}).get('average_fluency_score', 'N/A')}
 """
             
             return result_text
-        
         else:
             return "❌ No predictions generated. The audio file may not contain analyzable speech."
             
@@ -220,176 +193,39 @@ def process_audio_input(audio_file):
         logger.error(traceback.format_exc())
         return f"❌ Processing Error: {str(e)}\n\nPlease check the logs for more details."
 
-def process_text_input(text_input):
-    """Process text input directly (fallback option)"""
-    try:
-        if not text_input or not text_input.strip():
-            return "❌ Error: Please enter some text for analysis"
-        
-        # Check if prediction module is available
-        if MODULES['predict_from_chajson'] is None:
-            return "❌ Error: Text analysis not available. Missing prediction module."
-        
-        # Create a simple JSON structure for text-only input
-        temp_json = {
-            "sentences": [{
-                "sentence_id": "S1",
-                "aphasia_type": "UNKNOWN",
-                "dialogues": [{
-                    "INV": [],
-                    "PAR": [{
-                        "tokens": text_input.split(),
-                        "word_pos_ids": [0] * len(text_input.split()),
-                        "word_grammar_ids": [[0, 0, 0]] * len(text_input.split()),
-                        "word_durations": [0.0] * len(text_input.split()),
-                        "utterance_text": text_input
-                    }]
-                }]
-            }],
-            "text_all": text_input
-        }
-        
-        # Save to temporary file
-        with tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False) as f:
-            json.dump(temp_json, f, ensure_ascii=False, indent=2)
-            temp_json_path = f.name
-        
-        # Run prediction
-        results = MODULES['predict_from_chajson'](MODEL_DIR, temp_json_path, output_file=None)
-        
-        # Cleanup
-        try:
-            os.unlink(temp_json_path)
-        except:
-            pass
-        
-        # Format results
-        if "predictions" in results and len(results["predictions"]) > 0:
-            first_pred = results["predictions"][0]
-            
-            predicted_class = first_pred["prediction"]["predicted_class"]
-            confidence = first_pred["prediction"]["confidence_percentage"]
-            description = first_pred["class_description"]["description"]
-            severity = first_pred["additional_predictions"]["predicted_severity_level"]
-            fluency = first_pred["additional_predictions"]["fluency_rating"]
-            
-            return f"""
-🧠 **TEXT ANALYSIS RESULTS**
-
-🎯 **Predicted:** {predicted_class}
-📊 **Confidence:** {confidence}
-📈 **Severity:** {severity}/3
-🗣️ **Fluency:** {fluency}
-
-📝 **Description:**
-{description}
-
-ℹ️ **Note:** Text-based analysis provides limited accuracy compared to audio analysis.
-"""
-        else:
-            return "❌ No predictions generated from text input"
-    
-    except Exception as e:
-        logger.error(f"Text processing error: {str(e)}")
-        return f"❌ Error: {str(e)}"
-
-def create_interface():
+# Create simple interface
+def create_simple_interface():
     """Create simplified Gradio interface"""
     
-    # Check system status
-    model_available, missing_files = check_model_files()
-    pipeline_available = all(MODULES.values())
-    
-    status_message = "🟢 **System Status: Ready**" if model_available and pipeline_available else "🔴 **System Status: Issues Detected**"
-    
-    if not model_available:
-        status_message += f"\n❌ Missing model files: {', '.join(missing_files)}"
-    
-    if not pipeline_available:
-        missing_modules = [k for k, v in MODULES.items() if v is None]
-        status_message += f"\n❌ Missing modules: {', '.join(missing_modules)}"
-    
-    # Create interface
-    with gr.Blocks(title="Aphasia Classification System") as demo:
-        
-        gr.HTML("""
-        <div style="text-align: center; margin-bottom: 2rem;">
-            <h1>🧠 Advanced Aphasia Classification System</h1>
-            <p>Upload audio files or enter text to analyze speech patterns and classify aphasia types</p>
-        </div>
-        """)
-        
-        gr.Markdown(status_message)
-        
-        with gr.Tabs():
-            # Audio Tab
-            with gr.Tab("🎵 Audio Analysis"):
-                gr.Markdown("### Upload Audio File")
-                gr.Markdown("**Supported formats:** MP3, MP4, WAV, M4A, FLAC, OGG")
-                
-                audio_input = gr.File(
-                    label="Upload Audio File",
-                    file_types=["audio"]
-                )
-                
-                audio_btn = gr.Button("🔍 Analyze Audio", variant="primary")
-                
-                audio_output = gr.Textbox(
-                    label="Analysis Results",
-                    lines=20,
-                    max_lines=30
-                )
-                
-                audio_btn.click(
-                    fn=process_audio_input,
-                    inputs=audio_input,
-                    outputs=audio_output
-                )
-            
-            # Text Tab
-            with gr.Tab("📝 Text Analysis"):
-                gr.Markdown("### Direct Text Input")
-                gr.Markdown("**Note:** Audio analysis provides more accurate results")
-                
-                text_input = gr.Textbox(
-                    label="Enter Text",
-                    placeholder="Enter speech transcription or text for analysis...",
-                    lines=5
-                )
-                
-                text_btn = gr.Button("🔍 Analyze Text", variant="secondary")
-                
-                text_output = gr.Textbox(
-                    label="Analysis Results",
-                    lines=15,
-                    max_lines=20
-                )
-                
-                text_btn.click(
-                    fn=process_text_input,
-                    inputs=text_input,
-                    outputs=text_output
-                )
-        
-        gr.HTML("""
-        <div style="text-align: center; margin-top: 2rem; padding: 1rem; border-top: 1px solid #eee;">
-            <p><strong>About:</strong> This system uses advanced NLP and acoustic analysis to classify different types of aphasia.</p>
-            <p><em>For research and clinical assessment purposes.</em></p>
-        </div>
-        """)
-    
-    return demo
+    # Create interface with basic components only
+    iface = gr.Interface(
+        fn=process_audio_input,
+        inputs=gr.File(
+            label="Upload Audio File (MP3, MP4, WAV, M4A, FLAC, OGG)",
+            file_types=["audio"]
+        ),
+        outputs=gr.Textbox(
+            label="Analysis Results",
+            lines=25,
+            max_lines=50
+        ),
+        title="🧠 Aphasia Classification System",
+        description="Upload audio files to analyze speech patterns and classify aphasia types",
+        article="<p><strong>About:</strong> This system uses advanced NLP and acoustic analysis to classify different types of aphasia.</p><p><em>For research and clinical assessment purposes.</em></p>"
+    )
+    
+    return iface
 
 if __name__ == "__main__":
     try:
         logger.info("Starting Aphasia Classification System...")
         
         # Create and launch interface
-        demo = create_interface()
+        demo = create_simple_interface()
         demo.launch(
             server_name="0.0.0.0",
             server_port=7860,
-            share=True,  # This fixes the localhost error
+            share=False,  # Set to False to avoid the share warning
             show_error=True
         )