Upload app.py with huggingface_hub

app.py

@@ -23,8 +23,6 @@ engineered_cols = [
     "AVRR_PNN50", "CV_SDNN", "RMSSD_sq", "SDNN_sq"
 ]
 
-athlete_id_cols = [c for c in feature_cols if c.startswith("athlete_")]
-
 
 class FatigueInput(BaseModel):
     AVRR: float
@@ -38,6 +36,7 @@ class FatigueInput(BaseModel):
 
 
 def build_features(data: dict) -> pd.DataFrame:
+    """Compute engineered features from the 8 base inputs the backend sends."""
     row = dict(data)
     row["RMSSD_SDNN_ratio"] = row["RMSSD"] / (abs(row["SDNN"]) + 0.001)
     row["HRV_index"] = (row["SDNN"] + row["RMSSD"]) / 2
@@ -47,8 +46,6 @@ def build_features(data: dict) -> pd.DataFrame:
     row["CV_SDNN"] = row["Coefficient_of_Variation"] * row["SDNN"]
     row["RMSSD_sq"] = row["RMSSD"] ** 2
     row["SDNN_sq"] = row["SDNN"] ** 2
-    for col in athlete_id_cols:
-        row[col] = 0
     return pd.DataFrame([row])[feature_cols]
 
 
@@ -81,4 +78,5 @@ def predict(input_data: FatigueInput):
 
 @app.post("/predictGMM")
 def predict_gmm(input_data: FatigueInput):
+    """Legacy endpoint -- redirects to the supervised classifier."""
     return predict(input_data)