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import gradio as gr |
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import os |
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import numpy as np |
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import tensorflow as tf |
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import keras |
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import keras_cv |
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from keras.models import load_model |
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import cv2 |
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def image_predict(img_): |
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model = load_model('efficientnet_b0.keras') |
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img = cv2.resize(img_, dsize = [224, 224]) |
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img = img / 255.0 |
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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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classes = ['angry', 'happy', 'neutral', 'sad', 'suprised', 'tired'] |
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if pred == 0: |
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answer = f"Facial Expression detected is: {classes[0].capitalize()}" |
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elif pred == 1: |
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answer = f"Facial Expression detected is: {classes[1].capitalize()}" |
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elif pred == 2: |
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answer = f"Facial Expression detected is: {classes[2].capitalize()}" |
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elif pred == 3: |
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answer = f"Facial Expression detected is: Depressed" |
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elif pred == 4: |
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answer = f"Facial Expression detected is: {classes[4].capitalize()}" |
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elif pred == 5: |
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answer = f"Facial Expression detected is: {classes[5].capitalize()}" |
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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) |
