Update README.md

README.md

@@ -121,9 +121,9 @@ from huggingface_hub import hf_hub_download
 from sklearn.preprocessing import LabelEncoder
 
 # Load models and preprocessor
-preprocessor_path = hf_hub_download(repo_id='Briankabiru/FertiliserApplication', filename='preprocessor.joblib')
-numerical_model_path = hf_hub_download(repo_id='Briankabiru/FertiliserApplication', filename='numerical_model.joblib')
-categorical_model_path = hf_hub_download(repo_id='Briankabiru/FertiliserApplication', filename='categorical_model.joblib')
+preprocessor_path = hf_hub_download(repo_id='DNgigi/FertiliserApplication', filename='preprocessor.joblib')
+numerical_model_path = hf_hub_download(repo_id='DNgigi/FertiliserApplication', filename='numerical_model.joblib')
+categorical_model_path = hf_hub_download(repo_id='DNgigi/FertiliserApplication', filename='categorical_model.joblib')
 
 preprocessor = load(preprocessor_path)
 numerical_model = load(numerical_model_path)
@@ -166,7 +166,7 @@ numerical_targets = [
 ]
 
 # Load label encoders
-label_encoders = {col: load(hf_hub_download(repo_id='Briankabiru/FertiliserApplication', filename=f'label_encoder_{col}.joblib')) for col in categorical_targets}
+label_encoders = {col: load(hf_hub_download(repo_id='DNgigi/FertiliserApplication', filename=f'label_encoder_{col}.joblib')) for col in categorical_targets}
 
 def make_predictions(input_data):
     # Convert input data to DataFrame