Update main.py

main.py

@@ -29,7 +29,7 @@ app.add_middleware(
 def train_the_model(data,page): 
     if str(page) == "2":
 
-        new_data = data
+        new_data = da
         encoders = load('transexpress_encoders.joblib')
         xgb_model = load('transexpress_xgb_model.joblib')
         
@@ -48,8 +48,8 @@ def train_the_model(data,page):
                     new_data_filled[col] = encoder.transform(new_data_filled[col])
                 else:
                     new_data_filled[col] = encoder.transform(new_data_filled[col])
-        X_new = new_data_filled.drop('status.name', axis=1)
-        y_new = new_data_filled['status.name']
+        X_new = new_data_filled.drop('status_name', axis=1)
+        y_new = new_data_filled['status_name']
         X_train, X_test, y_train, y_test = train_test_split(X_new, y_new, test_size=0.2, random_state=42)
 
 
@@ -66,7 +66,7 @@ def train_the_model(data,page):
         #xgb = XGBClassifier(use_label_encoder=False, eval_metric='logloss')
         
         # Setup GridSearchCV
-        grid_search = GridSearchCV(xgb_model, param_grid, cv=2, n_jobs=-1, scoring='accuracy')
+        grid_search = GridSearchCV(xgb_model, param_grid, cv=40, n_jobs=-1, scoring='accuracy')
         
         # Fit the grid search to the data
         grid_search.fit(X_train, y_train)
@@ -117,7 +117,7 @@ def train_the_model(data,page):
         xgb = XGBClassifier(use_label_encoder=False, eval_metric='logloss')
         
         # Setup GridSearchCV
-        grid_search = GridSearchCV(xgb, param_grid, cv=2, n_jobs=-1, scoring='accuracy')
+        grid_search = GridSearchCV(xgb, param_grid, cv=40, n_jobs=-1, scoring='accuracy')
         
         # Fit the grid search to the data
         grid_search.fit(X_train, y_train)