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Create app.py
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app.py
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import gradio as gr
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import numpy as np
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import tensorflow as tf
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# Load the trained model
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model = tf.keras.models.load_model("sleep_cognition_model")
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# Define prediction function
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def predict(TST, SE, WASO, REM_Sleep, Deep_Sleep, Number_of_Awakenings):
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input_data = np.array([[TST, SE, WASO, REM_Sleep, Deep_Sleep, Number_of_Awakenings]])
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prediction = model.predict(input_data)
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return {
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"Reaction Time": float(prediction[0][0]),
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"Memory Recall Accuracy": float(prediction[0][1]),
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"Attention Score": float(prediction[0][2]),
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"Executive Function Score": float(prediction[0][3]),
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"Mental Fatigue Index": float(prediction[0][4]),
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}
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# Define Gradio interface
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iface = gr.Interface(
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fn=predict,
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inputs=[
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gr.Number(label="Total Sleep Time (TST)"),
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gr.Number(label="Sleep Efficiency (SE)"),
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gr.Number(label="Wake After Sleep Onset (WASO)"),
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gr.Number(label="REM Sleep (%)"),
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gr.Number(label="Deep Sleep (%)"),
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gr.Number(label="Number of Awakenings"),
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],
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outputs="json",
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title="Sleep & Cognitive Function Predictor",
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description="Enter sleep parameters to predict cognitive function scores.",
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)
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# Run the app
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iface.launch()
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