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from tensorflow import keras | |
import gradio as gr | |
model = keras.models.load_model('facial_expression1.h5') | |
class_names = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise'] | |
def predict_input_image(img): | |
img_4d=img.reshape(-1,48,48,1) | |
prediction=model.predict(img_4d)[0] | |
return {class_names[i]: float(prediction[i]) for i in range(len(class_names))} | |
image = gr.inputs.Image(shape=(48,48)) | |
label = gr.outputs.Label(num_top_classes=len(class_names)) | |
gr.Interface(fn=predict_input_image, inputs=image, outputs=label,interpretation='default').launch(debug='True') | |