Update app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+
+# Load model
+model = tf.keras.models.load_model("model.h5")
+
+# Labels
+class_names = ['batteries', 'clothes', 'e-waste', 'glass', 'light blubs',
+               'metal', 'organic', 'paper', 'plastic']
+
+# Image preprocessing and prediction
+def predict_image(img):
+    img = tf.image.resize(img, (224, 224))
+    img = tf.cast(img, tf.float32) / 255.0
+    img = tf.expand_dims(img, axis=0)
+    preds = model.predict(img)
+    pred_idx = np.argmax(preds)
+    confidence = float(np.max(preds)) * 100
+    return {class_names[pred_idx]: confidence}
+
+# Gradio app
+demo = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Label(num_top_classes=3),
+    title="♻️ Waste Classifier",
+    description="Classify waste into 9 categories: batteries, clothes, e-waste, glass, light bulbs, metal, organic, paper, plastic."
+)
+
+demo.launch()