Update app.py

app.py

@@ -3,25 +3,25 @@ import joblib
 import pandas as pd
 import json
 
-# Load your model and preprocessing tools
+# Load the model, scaler, and mapping
 scaler = joblib.load("scaler.joblib")
 gmm = joblib.load("gmm_model.joblib")
+
 with open("cluster_fatigue_map.json") as f:
     cluster_fatigue_map = json.load(f)
 
-# Define the input features
+# Define expected input features
 feature_cols = [
     'AVRR', 'SDNN', 'RMSSD', 'PNN50', 'Coefficient_of_Variation',
     'Age', 'Weight', 'Height'
 ]
 
-# Main prediction function
+# Inference function — accepts Python list of values
 def predict_fatigue(
     AVRR, SDNN, RMSSD, PNN50, Coefficient_of_Variation,
     Age, Weight, Height
 ):
     try:
-        # Prepare input as dict
         input_dict = {
             'AVRR': AVRR,
             'SDNN': SDNN,
@@ -33,35 +33,35 @@ def predict_fatigue(
             'Height': Height
         }
 
-        # Convert to DataFrame and scale
         df = pd.DataFrame([input_dict])[feature_cols]
         scaled = scaler.transform(df)
-
-        # Predict cluster and map to fatigue level
         cluster = gmm.predict(scaled)[0]
         fatigue_level = cluster_fatigue_map[str(cluster)]
 
-        return f"Predicted Fatigue Level: {fatigue_level} (Cluster {cluster})"
+        return {
+            "cluster": int(cluster),
+            "fatigue_level": fatigue_level
+        }
 
     except Exception as e:
-        return f"Error: {str(e)}"
+        return {"error": str(e)}
 
-# Gradio UI definition
-iface = gr.Interface(
-    fn=predict_fatigue,
-    inputs=[
-        gr.Number(label='AVRR'),
-        gr.Number(label='SDNN'),
-        gr.Number(label='RMSSD'),
-        gr.Number(label='PNN50'),
-        gr.Number(label='Coefficient of Variation'),
-        gr.Number(label='Age'),
-        gr.Number(label='Weight'),
-        gr.Number(label='Height'),
-    ],
-    outputs="text",
-    title="Fatigue Level Predictor",
-    description="Enter HRV metrics and demographic data to estimate fatigue level using a Gaussian Mixture Model."
-)
+# Gradio app in REST-friendly format
+with gr.Blocks() as demo:
+    gr.Interface(
+        fn=predict_fatigue,
+        inputs=[
+            gr.Number(label='AVRR'),
+            gr.Number(label='SDNN'),
+            gr.Number(label='RMSSD'),
+            gr.Number(label='PNN50'),
+            gr.Number(label='Coefficient of Variation'),
+            gr.Number(label='Age'),
+            gr.Number(label='Weight'),
+            gr.Number(label='Height'),
+        ],
+        outputs="json",
+        live=False
+    )
 
-iface.launch()
+demo.launch()