| import gradio as gr | |
| import numpy as np | |
| import tensorflow as tf | |
| import tensorflow_hub as hub | |
| loaded_model = tf.keras.models.load_model( | |
| ('CatDogmodel2.h5'), | |
| custom_objects={'KerasLayer':hub.KerasLayer} | |
| ) | |
| def model(image): | |
| im_scaled = image/255 | |
| im_reshape = np.reshape(im_scaled,[1,160,160,3]) | |
| pred = loaded_model.predict(im_reshape) | |
| pred_label = np.argmax(pred) | |
| if (pred_label == 0): | |
| return "The Image is of a Dog." | |
| if (pred_label == 1): | |
| return "The Image is of a Cat." | |
| image = gr.inputs.Image(shape=(160,160)) | |
| background='body{background-image:url("https://d2gg9evh47fn9z.cloudfront.net/800px_COLOURBOX35440124.jpg");}' | |
| title = "Cat & Dog Classifier" | |
| iface = gr.Interface(fn=model, inputs=image, outputs='text', css=background, title=title) | |
| iface.launch(debug=True) | |