Create app.py

app.py

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+import gradio as gr
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+import numpy as np
+
+# Load model
+model = load_model("waste_classifier_model.h5")
+class_names = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
+
+def predict_image(image):
+    # Preprocess
+    img = image.resize((224, 224))
+    img_array = img_to_array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    
+    # Predict
+    predictions = model.predict(img_array)[0]
+    pred_index = np.argmax(predictions)
+    confidence = float(np.max(predictions))
+    label = class_names[pred_index]
+    
+    return {label: confidence}
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title="Waste Classification with MobileNetV2",
+    description="Upload an image of waste (plastic, metal, paper, etc.), and this model will classify it."
+)
+
+iface.launch()