Create app.py

app.py

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+import requests
+import gradio as gr
+
+#loading the model
+model1=load_model('model.h5')
+
+#providing the labels of our dataset
+
+labels = ['rain', 'glaze', 'rime', 'snow', 'fogsmog', 'frost', 'lightning', 'rainbow', 'hail', 'sandstorm', 'dew']
+print(labels)
+
+#function to classify the image
+from gc import set_debug
+def classify_image(inp):
+  inp = inp.reshape((-1, 300, 300, 3))
+  prediction = model1.predict(inp).flatten()
+  confidences = {labels[i]: float(prediction[i]) for i in range(10)}
+  print(confidences)
+  return confidences
+  
+  
+#gradio interface to check/test the classification of the images
+gr.Interface(fn=classify_image, 
+             inputs=gr.inputs.Image(shape=(300, 300)),
+             outputs=gr.outputs.Label(num_top_classes=3),
+             examples=["banana.jpg", "car.jpg"]).launch(debug=False)
+