Isabel Gwara commited on
Commit
5507b4f
·
1 Parent(s): 46b3682

Create app.py

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  1. app.py +29 -0
app.py ADDED
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+ import keras
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+ from keras.models import load_model
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+ import gradio as gr
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+ import numpy as np
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+ from skimage import transform
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+ import h5py
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+
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+ model = load_model('model.h5') # single file model
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+ labels = ['cheetah', 'hyena', 'jaguar', 'tiger'] # will need to be loaded from file in the order of your directories
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+
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+ def preprocess(image):
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+ image = np.array(image) / 255
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+ image = transform.resize(image, (300, 300, 3))
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+ image = np.expand_dims(image, axis=0)
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+ return image
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+
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+ def match_prediction_with_label(test_img):
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+ image = preprocess(test_img)
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+ pred = model.predict(image)
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+ results_dict = {}
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+ for row in pred:
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+ for idx, item in enumerate(row):
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+ results_dict[labels[idx]] = float(item)
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+ return results_dict
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+
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+ image = gr.inputs.Image(shape=(300, 300), label="Upload Your Image Here")
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+ label = gr.outputs.Label(num_top_classes=4)
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+
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+ gr.Interface(fn=match_prediction_with_label, inputs=image, outputs=label, capture_session=True).launch(share=True, debug=True)