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import gradio as gr |
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import os |
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os.environ['KERAS-BACKEND'] = 'tensorflow' |
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import keras_core as keras |
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import numpy as np |
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from keras.models import load_model |
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import cv2 |
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import tensorflow as tf |
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import tensorflow.image |
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def image_predict(img_): |
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model = load_model('models/final_model.h5') |
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img = img_ / 255.0 |
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img = tf.image.central_crop(img, central_fraction = .85).numpy() |
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img = cv2.resize(img, dsize = [224, 224]) |
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img = np.expand_dims(img, axis = 0) |
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pred = model.predict(img, verbose = 1) |
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pred = np.argmax(pred, axis = 1) |
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if pred == 0: |
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answer = "The inputted image is an Airplane" |
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elif pred == 1: |
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answer = "The inputted image is a Car" |
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return answer |
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with gr.Blocks() as demo: |
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image_ = gr.Image(label = 'Input Image to be predicted') |
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output = gr.Textbox(label = 'Prediction') |
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btn = gr.Button('Predict') |
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btn.click(fn = image_predict, inputs = [image_], outputs = output) |
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demo.launch(share = False) |
