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| import requests | |
| import gradio as gr | |
| from tensorflow import keras | |
| from keras.models import Model | |
| from tensorflow.keras import Model | |
| #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) | |