Create app.py

app.py

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+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  # Disable GPU and force CPU mode
+
+import gradio as gr
+import numpy as np
+import tensorflow as tf
+
+model_path = "sleep_cognition_model.h5"
+
+# Check if the model file exists before loading
+if not os.path.exists(model_path):
+    raise FileNotFoundError(f"⚠️ Model file not found: {model_path}. Please upload it to Hugging Face Space.")
+
+
+# Load the trained model
+model = tf.keras.models.load_model("sleep_cognition_model.h5")  
+
+# Define prediction function
+def predict(TST, SE, WASO, REM_Sleep, Deep_Sleep, Number_of_Awakenings):
+    input_data = np.array([[TST, SE, WASO, REM_Sleep, Deep_Sleep, Number_of_Awakenings]])
+    prediction = model.predict(input_data)
+    return {
+        "Reaction Time": float(prediction[0][0]),
+        "Memory Recall Accuracy": float(prediction[0][1]),
+        "Attention Score": float(prediction[0][2]),
+        "Executive Function Score": float(prediction[0][3]),
+        "Mental Fatigue Index": float(prediction[0][4]),
+    }
+
+# Define Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Number(label="Total Sleep Time (TST)"),
+        gr.Number(label="Sleep Efficiency (SE)"),
+        gr.Number(label="Wake After Sleep Onset (WASO)"),
+        gr.Number(label="REM Sleep (%)"),
+        gr.Number(label="Deep Sleep (%)"),
+        gr.Number(label="Number of Awakenings"),
+    ],
+    outputs="json",
+    title="Sleep & Cognitive Function Predictor",
+    description="Enter sleep parameters to predict cognitive function scores.",
+)
+
+# Run the app
+iface.launch()