Created app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import tensorflow.keras as keras
+from tensorflow.keras.models import load_model
+ 
+# load model
+model = load_model('model520.h5')
+
+#prediction classes
+classnames = ['paper', 'cardboard', 'plastic', 'metal', 'food', 'battery', 'shoes', 'clothes', 'glass', 'medical']
+
+#prediction function
+def predict_image(img):
+  img_4d=img.reshape(-1,224, 224,3)
+  prediction=model.predict(img_4d)[0]
+  return {classnames[i]: float(prediction[i]) for i in range(len(classnames))}
+
+#Gradio interface
+image = gr.inputs.Image(shape=(224, 224))
+label = gr.outputs.Label(num_top_classes=3)
+article="<p style='text-align: center; font-weight:bold;'>Model based on the VGG-16 CNN</p>"
+
+gr.Interface(fn=predict_image, inputs=image,  title="Garbage Classifier VGG-16",
+    description="This is a Garbage Classification Model Trained using VGG-16 architecture. Deployed to Hugging Face using Gradio.", outputs=label, article=article, enable_queue=True, interpretation='default').launch(share="True")