Create app.py

app.py

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+import gradio as gr
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+import numpy as np
+
+# Load your trained model
+model = load_model("waste_sort_model.h5")
+class_names = ["Non-Recyclable", "Recyclable"]
+
+def classify_image(img):
+    """Classify uploaded image as recyclable or non-recyclable."""
+    img = img.resize((150, 150))  # Resize to match model input size
+    img_array = np.array(img) / 255.0  # Normalize pixel values
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+
+    predictions = model.predict(img_array)
+    predicted_class = class_names[np.argmax(predictions)]
+    return f"Prediction: {predicted_class}"
+
+# Define Gradio Interface
+interface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Waste Classification",
+    description="Upload an image of waste to classify as Recyclable or Non-Recyclable.",
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch()