Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+from tensorflow import keras
+
+import huggingface_hub
+from huggingface_hub import from_pretrained_keras
+
+
+# load custom pre-trained model from HuggingFace models
+model_api_link = 'chaninder/trashtacks-model-v1'
+pre_trained_model = from_pretrained_keras(model_api_link)
+
+# classification labels
+labels = ['compost', 'e-waste', 'recycle', 'trash']
+
+
+def classify_image(inp):
+    inp = inp.reshape((-1, 224, 224, 3))
+    #inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
+    prediction = pre_trained_model.predict(inp).flatten()
+    confidences = {labels[i]: float(prediction[i]) for i in range(4)}
+    return confidences
+
+# create Gradio interface
+
+iface = gr.Interface(fn=classify_image, 
+             inputs=gr.Image(shape=(224, 224)),
+             outputs=gr.Label(num_top_classes=4),
+             examples=["banana.jpg", 'can.jpg', 'battery.jpg'])
+             
+iface.launch(share=True)