Create backup app.py

backup app.py

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+import gradio as gr
+import tensorflow as tf
+import librosa
+import numpy as np
+from PIL import Image
+import requests
+from io import BytesIO
+
+# Load model - added caching
+model = tf.keras.models.load_model("animal_sound_cnn.h5", compile=False)
+
+# Updated class mapping
+class_names = {
+    0: "Lion", 1: "Donkey", 2: "Cow", 3: "Cat", 4: "Dog",
+    5: "Sheep", 6: "Frog", 7: "Bird", 8: "Monkey", 9: "Chicken"
+}
+
+# Fixed image URLs with working links
+image_urls = {
+    "Lion": "https://upload.wikimedia.org/wikipedia/commons/7/73/Lion_waiting_in_Namibia.jpg",
+    "Donkey": "https://upload.wikimedia.org/wikipedia/commons/8/85/%C3%82ne_d%27Ethiopie.jpg",
+    "Cow": "https://upload.wikimedia.org/wikipedia/commons/0/0c/Cow_female_black_white.jpg",
+    "Cat": "https://upload.wikimedia.org/wikipedia/commons/6/6e/Black_and_white_cat_on_rug.jpg",
+    "Dog": "https://upload.wikimedia.org/wikipedia/commons/d/db/Heterochromia_dog%2C_Struga.jpg",
+    "Sheep": "https://upload.wikimedia.org/wikipedia/commons/2/2c/Flock_of_sheep.jpg",
+    "Frog": "https://upload.wikimedia.org/wikipedia/commons/c/cb/The_Green_and_Golden_Bell_Frog.jpg",
+    "Bird": "https://upload.wikimedia.org/wikipedia/commons/f/fb/Brown_thrasher_in_CP_%2802147%29.jpg",
+    "Monkey": "https://upload.wikimedia.org/wikipedia/commons/c/c8/Monkey_eating.jpg",
+    "Chicken": "https://upload.wikimedia.org/wikipedia/commons/b/b2/Gallus_gallus_domesticus_487567856.jpg"
+}
+
+def download_image(url):
+    """Download image with error handling"""
+    try:
+        response = requests.get(url, timeout=10)
+        response.raise_for_status()
+        return Image.open(BytesIO(response.content))
+    except Exception:
+        # Return blank image if download fails
+        return Image.new('RGB', (300, 200), color='gray')
+
+def preprocess(audio_path):
+    """Audio preprocessing with error handling"""
+    try:
+        y, sr = librosa.load(audio_path, sr=22050, duration=3)  # Limit to 3s
+        mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=40)
+        mfcc_mean = np.mean(mfcc, axis=1)
+        return mfcc_mean.reshape(1, 40, 1, 1).astype(np.float32)
+    except Exception as e:
+        raise ValueError(f"Audio processing error: {str(e)}")
+
+def predict(audio_path):
+    try:
+        # Preprocess audio
+        X = preprocess(audio_path)
+        
+        # Make prediction
+        pred = model.predict(X, verbose=0)  # Disable verbose output
+        class_id = np.argmax(pred)
+        confidence = pred[0][class_id]
+        class_name = class_names.get(class_id, f"Unknown ({class_id})")
+        
+        # Get image
+        img = download_image(image_urls.get(class_name, ""))
+        
+        # Format output
+        text_output = (f"Predicted animal: {class_name}\n"
+                      f"Confidence: {confidence*100:.2f}%")
+        return text_output, img
+    
+    except Exception as e:
+        return f"Error: {str(e)}", download_image("")  # Return error with blank image
+
+# Create interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 🐾 Animal Sound Classifier")
+    gr.Markdown("Upload an animal sound (3-5 seconds) to identify the animal")
+    
+    with gr.Row():
+        audio_input = gr.Audio(
+            sources=["upload", "microphone"],
+            type="filepath",
+            label="Animal Sound",
+            max_length=5  # Limit recording to 5 seconds
+        )
+        
+    btn = gr.Button("Identify Animal", variant="primary")
+    
+    with gr.Row():
+        text_output = gr.Textbox(label="Prediction Result", interactive=False)
+        image_output = gr.Image(label="Animal Image", height=300)
+    
+    btn.click(
+        fn=predict,
+        inputs=audio_input,
+        outputs=[text_output, image_output]
+    )
+    
+    gr.Examples(
+        examples=[
+            ["examples/lion_roar.wav"],
+            ["examples/dog_bark.wav"],
+            ["examples/bird_chirp.wav"]
+        ],
+        inputs=audio_input,
+        outputs=[text_output, image_output],
+        fn=predict,
+        cache_examples=True
+    )
+
+# Launch with error display enabled
+demo.launch(show_error=True)