Update main.py

main.py

@@ -52,11 +52,12 @@ def train_the_model(data):
         xgb_model.fit(X_new, y_new)
         dump(xgb_model,'xgb_model.joblib')
         
-        print("Model updated with new data.")
+
         y_pred = xgb_model.predict(X_test)
         accuracy = accuracy_score(y_test, y_pred)
-        print("Accuracy:", accuracy)
-        print("Classification Report:\n", classification_report(y_test, y_pred))
+        classification_rep = classification_report(y_test, y_pred)
+        return accuracy,classification_rep,"Model finetuned with new data."
+
         
     except:
         data = data
@@ -113,11 +114,6 @@ def train_the_model(data):
         accuracy = accuracy_score(y_test, y_pred)
         classification_rep = classification_report(y_test, y_pred)
         
-        # Print the results
-        print("Accuracy:", accuracy)
-        print("Classification Report:\n", classification_report(y_test, y_pred))
-    
-        
         # Save the model
         model_filename = 'xgb_model.joblib'
         dump(best_xgb, model_filename)
@@ -126,9 +122,7 @@ def train_the_model(data):
         encoders_filename = 'encoders.joblib'
         dump(encoders, encoders_filename)
         
-        print(f"Model saved as {model_filename}")
-        print(f"Encoders saved as {encoders_filename}")
-        print("new base model trained")
+        return accuracy,classification_rep,"base Model trained"
     
 @app.get("/trigger_the_data_fecher")
 async def your_continuous_function(page: int,paginate: int,Tenant: str):
@@ -164,9 +158,9 @@ async def your_continuous_function(page: int,paginate: int,Tenant: str):
     print("data collected from page : "+str(page))
     #data.to_csv("new.csv")
     
-    train_the_model(data)
+    accuracy,classification_rep,message = train_the_model(data)
 
-    return {"model trained details":{"page_number":page,"data_count":data_count}}
+    return {"message":message,"page_number":page,"data_count":data_count,"accuracy":accuracy,"classification_rep":classification_rep}