Update main.py

main.py

@@ -26,7 +26,8 @@ app.add_middleware(
 
 
 
-def train_the_model(data):  
+def train_the_model(data,page): 
+    if str(page) == "2":
 
         new_data = data
         encoders = load('transexpress_encoders.joblib')
@@ -63,7 +64,7 @@ def train_the_model(data):
         }
         
         # Initializing GridSearchCV
-        grid_search = GridSearchCV(xgb_model, param_grid, cv=50, n_jobs=-1, scoring='accuracy')
+        grid_search = GridSearchCV(xgb_model, param_grid, cv=1, n_jobs=-1, scoring='accuracy')
         
         # Fitting GridSearchCV
         grid_search.fit(X_train, y_train)
@@ -81,7 +82,7 @@ def train_the_model(data):
         return accuracy, classification_rep, "Model finetuned with new data."
         
 
-        
+    if str(page) == "1":    
 
         data = data
         
@@ -116,7 +117,7 @@ def train_the_model(data):
         xgb = XGBClassifier(use_label_encoder=False, eval_metric='logloss')
         
         # Setup GridSearchCV
-        grid_search = GridSearchCV(xgb, param_grid, cv=50, n_jobs=-1, scoring='accuracy')
+        grid_search = GridSearchCV(xgb, param_grid, cv=1, n_jobs=-1, scoring='accuracy')
         
         # Fit the grid search to the data
         grid_search.fit(X_train, y_train)
@@ -181,7 +182,7 @@ async def your_continuous_function(page: str,paginate: str):
     #return "done"
     #data.to_csv("new.csv")
     
-    accuracy,classification_rep,message = train_the_model(df)
+    accuracy,classification_rep,message = train_the_model(df,page)
 
     return {"message":message,"page_number":page,"data_count":data_count,"accuracy":accuracy,"classification_rep":classification_rep}