Update app.py

app.py

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+import gradio as gr
+import numpy as np
+import torch
+import librosa
+from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
+import matplotlib.pyplot as plt
+from matplotlib.colors import Normalize
+
+# Constants
+SAMPLING_RATE = 16000
+MODEL_NAME = "MIT/ast-finetuned-audioset-10-10-0.4593"
+DEFAULT_THRESHOLD = 0.7
+
+# Load model and feature extractor
+feature_extractor = AutoFeatureExtractor.from_pretrained(MODEL_NAME)
+model = AutoModelForAudioClassification.from_pretrained(MODEL_NAME)
+
+def analyze_audio(audio_array, threshold=DEFAULT_THRESHOLD):
+    """
+    Process audio and detect anomalies
+    Returns:
+    - classification result
+    - confidence score
+    - spectrogram visualization
+    """
+    try:
+        # Resample if needed and convert to mono
+        if isinstance(audio_array, tuple):
+            sr, audio = audio_array
+            audio = librosa.resample(audio, orig_sr=sr, target_sr=SAMPLING_RATE)
+        else:
+            audio = audio_array
+            
+        if len(audio.shape) > 1:
+            audio = librosa.to_mono(audio)
+            
+        # Extract features
+        inputs = feature_extractor(
+            audio, 
+            sampling_rate=SAMPLING_RATE, 
+            return_tensors="pt",
+            padding=True,
+            return_attention_mask=True
+        )
+        
+        # Run inference
+        with torch.no_grad():
+            outputs = model(**inputs)
+            logits = outputs.logits
+            probs = torch.softmax(logits, dim=-1)
+            
+        # Get predicted class and confidence
+        predicted_class = "Normal" if probs[0][0] > threshold else "Anomaly"
+        confidence = probs[0][0].item() if predicted_class == "Normal" else 1 - probs[0][0].item()
+        
+        # Create spectrogram visualization
+        spectrogram = librosa.feature.melspectrogram(
+            y=audio, 
+            sr=SAMPLING_RATE,
+            n_mels=128,
+            fmax=8000
+        )
+        db_spec = librosa.power_to_db(spectrogram, ref=np.max)
+        
+        plt.figure(figsize=(10, 4))
+        plt.imshow(db_spec, aspect='auto', origin='lower', 
+                  norm=Normalize(vmin=-80, vmax=0),
+                  cmap='viridis')
+        plt.colorbar(format='%+2.0f dB')
+        plt.title('Mel Spectrogram')
+        plt.tight_layout()
+        plt.savefig('spec.png', bbox_inches='tight')
+        plt.close()
+        
+        return (
+            predicted_class,
+            f"{confidence:.1%}",
+            'spec.png',
+            str(probs.tolist()[0])
+        )
+        
+    except Exception as e:
+        return f"Error: {str(e)}", "", "", ""
+
+# Gradio interface
+with gr.Blocks(title="Industrial Audio Analyzer", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🏭 Industrial Equipment Sound Analyzer
+    ### Powered by Audio Spectrogram Transformer (AST)
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            audio_input = gr.Audio(
+                label="Upload Equipment Audio Recording",
+                type="numpy",
+                source="upload",
+                show_download_button=True
+            )
+            threshold = gr.Slider(
+                minimum=0.5,
+                maximum=0.95,
+                step=0.05,
+                value=DEFAULT_THRESHOLD,
+                label="Anomaly Detection Threshold",
+                info="Higher values reduce false positives but may miss subtle anomalies"
+            )
+            analyze_btn = gr.Button("🔍 Analyze Sound", variant="primary")
+            
+            gr.Examples(
+                examples=["examples/normal_machine.wav", "examples/anomalous_machine.wav"],
+                inputs=audio_input,
+                label="Sample Recordings"
+            )
+            
+        with gr.Column():
+            result_label = gr.Label(label="Detection Result")
+            confidence = gr.Textbox(label="Confidence Score")
+            spectrogram = gr.Image(label="Spectrogram Visualization")
+            raw_probs = gr.Textbox(
+                label="Model Output Probabilities",
+                visible=False
+            )
+    
+    analyze_btn.click(
+        fn=analyze_audio,
+        inputs=[audio_input, threshold],
+        outputs=[result_label, confidence, spectrogram, raw_probs]
+    )
+    
+    gr.Markdown("""
+    ## How It Works
+    - Upload audio recordings from industrial equipment
+    - The AI analyzes sound patterns using spectrogram analysis
+    - Detects anomalies indicating potential equipment issues
+    
+    **Tip**: For best results, use 5-10 second recordings of steady operation
+    """)
+
+if __name__ == "__main__":
+    demo.launch()