Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+from tensorflow.keras.models import load_model
+
+model2=load_model("/content/best_mobilenetv2_model.keras")
+
+def classify_image(img):
+
+    img = img.convert("RGB")
+    img = img.resize((224, 224))
+
+    img_tensor = tf.convert_to_tensor(np.array(img), dtype=tf.float32)
+
+    img_tensor = tf.expand_dims(img_tensor, axis=0)
+
+    prediction = model2.predict(img_tensor)
+    predicted_class_index = np.argmax(prediction)
+    predicted_class_name = class_names[predicted_class_index]
+    confidence = prediction[0][predicted_class_index]
+
+    return f"Predicted: {predicted_class_name} (Confidence: {confidence:.2%})"
+
+
+iface = gr.Interface(
+   fn=classify_image,
+    inputs=gr.Image(type="pil", label="Upload Waste Image"),
+    outputs=gr.Textbox(label="Prediction"),
+    title="♻️ Waste Classifier",
+    description="Upload an image of cardboard, plastic, metal, paper, trash, or glass to classify it."
+)
+
+
+# Launch the interface
+iface.launch()  # Start the Gradio interface for user interaction