| import gradio as gr | |
| from huggingface_hub import from_pretrained_keras | |
| from tensorflow.keras.preprocessing.image import load_img | |
| from tensorflow.keras.preprocessing.image import img_to_array | |
| from tensorflow.keras.preprocessing import image | |
| import numpy as np | |
| model = from_pretrained_keras("yusyel/clothing") | |
| class_names=["dress", | |
| "hat", | |
| "longsleee", | |
| "outwear", | |
| "pants", | |
| "shirt", | |
| "shoes", | |
| "shorts", | |
| "skirt", | |
| "t-shirt"] | |
| def preprocess_image(img): | |
| img = load_img(img, target_size=(299, 299, 3)) | |
| img = image.img_to_array(img) | |
| img = np.expand_dims(img, axis=0) | |
| img /= 255.0 | |
| print(img.shape) | |
| return img | |
| def predict(img): | |
| img = preprocess_image(img) | |
| pred = model.predict(img) | |
| pred = np.squeeze(np.exp(pred)/np.sum(np.exp(pred))).astype(float) | |
| print(dict(zip(class_names, pred))) | |
| return dict(zip(class_names, pred)) | |
| with gr.Interface( | |
| fn=predict, | |
| inputs=[gr.inputs.Image(type="filepath")], | |
| outputs=gr.outputs.Label(), | |
| examples=[ | |
| ["./img/dress.jpg"], | |
| ["./img/hat.jpg"], | |
| ["./img/longsleeve.jpg"], | |
| ["./img/outwear.jpg"], | |
| ["./img/pants.jpg"], | |
| ["./img/shirt.jpg"], | |
| ["./img/shoes.jpg"], | |
| ["./img/short.jpg"], | |
| ["./img/skirt.jpg"], | |
| ["./img/t-shirt.jpg"], | |
| ], | |
| title="Fashion Classification") as demo: | |
| gr.Markdown("# This Demo For Free Machine Learning Zoomcamp", elem_classes=['center']) | |
| gr.Markdown("Course github Links [github](https://github.com/DataTalksClub/machine-learning-zoomcamp)", | |
| elem_classes=['center', 'small-big']) | |
| demo.launch(server_name="0.0.0.0", server_port=7860) | |