Update app.py

app.py

@@ -1,11 +1,15 @@
-from fastapi import FastAPI
+
+from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 import joblib
 import numpy as np
 
 # Load the trained model and scaler
-model = joblib.load('LR1.pkl')  # Replace with the path to your saved model
-scaler = joblib.load('scaler.pkl')  # Ensure the scaler is saved during training and available here
+try:
+    model = joblib.load('LR1.pkl')  # Replace with the path to your saved model
+    scaler = joblib.load('scaler.pkl')  # Ensure the scaler is saved during training and available here
+except Exception as e:
+    raise RuntimeError(f"Failed to load model or scaler: {e}")
 
 app = FastAPI()
 
@@ -39,16 +43,19 @@ class PCOSPrediction(BaseModel):
 # Define the prediction endpoint
 @app.post("/predict/", response_model=PCOSPrediction)
 def predict(data: PCOSInput):
-    # Convert input data to array
-    input_data = np.array([[data.Follicle_No_R, data.Follicle_No_L, data.Skin_darkening, data.hair_growth,
-                            data.Weight_gain, data.Cycle_length, data.AMH, data.Fast_food, data.Cycle_R_I,
-                            data.FSH_LH, data.PRL, data.Pimples, data.Age, data.BMI]])
-    
-    # Scale the input data using the loaded scaler
-    scaled_input = scaler.transform(input_data)
-
-    # Make prediction
-    prediction = model.predict(scaled_input)
-    probability = model.predict_proba(scaled_input)[0][1]  # Probability for class 1 (PCOS)
-
-    return PCOSPrediction(prediction=int(prediction[0]), probability=probability)
+    try:
+        # Convert input data to array
+        input_data = np.array([[data.Follicle_No_R, data.Follicle_No_L, data.Skin_darkening, data.hair_growth,
+                                data.Weight_gain, data.Cycle_length, data.AMH, data.Fast_food, data.Cycle_R_I,
+                                data.FSH_LH, data.PRL, data.Pimples, data.Age, data.BMI]])
+        
+        # Scale the input data using the loaded scaler
+        scaled_input = scaler.transform(input_data)
+
+        # Make prediction
+        prediction = model.predict(scaled_input)
+        probability = model.predict_proba(scaled_input)[0][1]  # Probability for class 1 (PCOS)
+
+        return PCOSPrediction(prediction=int(prediction[0]), probability=probability)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Prediction error: {e}")