Update app.py

app.py

@@ -96,14 +96,14 @@ def predict_hemoglobin(age, gender, image):
     features = extract_features(image)
     
     # Ensure gender is encoded correctly (0 for female, 1 for male)
-    features['gender'] = 1 if gender.lower() == 'male' else 0
-    features['age'] = age
+    features['Gender'] = 1 if gender.lower() == 'M' else 0
+    features['Age'] = age
     
     # Create a DataFrame for features (do not include Hgb, as it's the predicted value)
     features_df = pd.DataFrame([features])
 
     # Load the trained model, scaler, and label encoder
-    svr_model = joblib.load('svr_model.pkl')
+    svr_model = joblib.load('svr_model (1).pkl')
     scaler = joblib.load('minmax_scaler.pkl')
     label_encoder = joblib.load('label_encoder.pkl')