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import gradio as gr |
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import cv2 |
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import numpy as np |
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from PIL import Image |
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from keras.preprocessing import image |
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import tensorflow as tf |
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model = tf.keras.models.load_model(r"model.keras") |
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def predict(img): |
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print("hi") |
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print("hi") |
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Retina_classes = ['DR', 'No_DR'] |
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img = np.resize(img,(224,224,3)) |
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img = np.expand_dims(img, axis=0) |
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prediction=model.predict(img)[0] |
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print(prediction) |
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return {Retina_classes[i]: float(prediction[i]) for i in range(2)} |
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image = gr.Image(label="Upload Here") |
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label = gr.Label(num_top_classes=2) |
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gr.Interface(fn=predict, inputs="image", outputs=label).launch() |
