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| import json | |
| import tensorflow as tf | |
| from huggingface_hub import hf_hub_download | |
| import gradio as gr | |
| tf_model = hf_hub_download(repo_id='mikachou/dog-breed-classifier', filename='tf_model.h5') | |
| config_json = hf_hub_download(repo_id='mikachou/dog-breed-classifier', filename='config.json') | |
| model = tf.keras.models.load_model(tf_model) | |
| print(model.summary()) | |
| with open(config_json) as f: | |
| config = json.load(f) | |
| dogs_breeds = list(config['id2label'].values()) | |
| def predict(filepath): | |
| img = tf.io.read_file(filepath) | |
| tensor = tf.io.decode_image(img, channels=3, dtype=tf.dtypes.float32) | |
| tensor = tf.image.resize(tensor, [299, 299]) | |
| input_tensor = tf.expand_dims(tensor, axis=0) | |
| output = model.predict(input_tensor) | |
| confidences = { dogs_breeds[i]: float(output[0][i]) for i in range(120) } | |
| return confidences | |
| demo = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(label='photo', type='filepath'), | |
| outputs=gr.Label(label="Predicted breed", num_top_classes=3), | |
| examples=[ | |
| 'imgs/beethoven.jpg', | |
| 'imgs/belle.png', | |
| 'imgs/belmondo.jpg', | |
| 'imgs/dorothy.jpg', | |
| 'imgs/lassie.jpg', | |
| 'imgs/rintintin.jpg' | |
| ], | |
| title="Dog breed identification", | |
| description="The model was trained with [Stanford Dogs Dataset](http://vision.stanford.edu/aditya86/ImageNetDogs/) using tensorflow/keras on a fine-tuned pre-trained InceptionResNetV2 model", | |
| article="You could also drag/drop other examples from [this page](https://www.rdasia.com/pets/can-you-guess-dog-breed-based-its-puppy-picture)") | |
| demo.launch() |