Update app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Class labels (same order as training)
+class_names = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash']
+
+# Load your trained model
+model = tf.keras.models.load_model("garbage_model.h5")
+
+# Prediction function
+def predict_image(img):
+    img = img.resize((124, 124))
+    img_array = tf.keras.preprocessing.image.img_to_array(img)
+    img_array = tf.expand_dims(img_array, axis=0)
+
+    predictions = model.predict(img_array)[0]
+    return {class_names[i]: float(predictions[i]) for i in range(len(class_names))}
+
+# Gradio interface
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title="🗑️ Garbage Classifier with EfficientNet",
+    description="Upload a garbage image to predict its type: plastic, paper, metal, etc."
+)
+
+interface.launch()
+