Delete app.py

app.py

@@ -1,47 +0,0 @@
-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()