Create app.py

app.py

@@ -0,0 +1,42 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+import joblib
+import pandas as pd
+import json
+
+# Load models
+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)
+
+feature_cols = [
+    "AVRR", "SDNN", "RMSSD", "PNN50", "Coefficient_of_Variation",
+    "Age", "Weight", "Height"
+]
+
+# Define input schema
+class FatigueInput(BaseModel):
+    AVRR: float
+    SDNN: float
+    RMSSD: float
+    PNN50: float
+    Coefficient_of_Variation: float
+    Age: int
+    Weight: float
+    Height: float
+
+# Create FastAPI app
+app = FastAPI()
+
+@app.post("/predict")
+def predict(input_data: FatigueInput):
+    try:
+        input_dict = input_data.dict()
+        input_df = pd.DataFrame([input_dict])[feature_cols]
+        scaled_input = scaler.transform(input_df)
+        cluster = gmm.predict(scaled_input)[0]
+        fatigue_level = cluster_fatigue_map[str(cluster)]
+        return {"cluster": int(cluster), "fatigue_level": fatigue_level}
+    except Exception as e:
+        return {"error": str(e)}