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app.py

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+
+import gradio as gr
+import numpy as np
+import cv2
+import tensorflow as tf
+
+# Load model
+model = tf.keras.models.load_model("trash_sorter.keras")
+
+def classify_waste(image):
+    # Preprocess image
+    img = cv2.resize(image, (224, 224))
+    img = img / 255.0
+    img = np.expand_dims(img, axis=0)
+    
+    # Predict
+    predictions = model.predict(img, verbose=0)[0]
+    
+    # Get results
+    class_names = ["🗑️ ORGANIC", "♻️ RECYCLABLE"]
+    confidence = np.max(predictions)
+    predicted_class = class_names[np.argmax(predictions)]
+    
+    # Format output
+    result = f"""
+    ## {predicted_class}
+    
+    **Confidence:** {confidence:.2%}
+    
+    **Breakdown:**
+    - Organic: {predictions[0]:.2%}
+    - Recyclable: {predictions[1]:.2%}
+    
+    **What to do:**
+    { "Compost or dispose in green bin" if predicted_class == "🗑️ ORGANIC" else "Clean and place in recycling bin" }
+    """
+    
+    return result
+
+# Create interface
+demo = gr.Interface(
+    fn=classify_waste,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Markdown(),
+    title="🗑️ AI Trash Sorter",
+    description="Upload a photo of waste to classify as Organic or Recyclable",
+    examples=[
+        ["example_organic.jpg"],
+        ["example_recyclable.jpg"]
+    ]
+)
+
+# Launch
+if __name__ == "__main__":
+    demo.launch()