Update main.py

main.py

@@ -57,7 +57,7 @@ async def train_the_model(Tenant: str):
         # Select columns
         selected_columns = ['customer_name', 'customer_address', 'customer_phone',
                             'customer_email', 'cod', 'weight', 'origin_city.name',
-                            'destination_city.name', 'status.name']
+                            'destination_city.name','created_at','status.name']
         
         # Handling missing values
         #data_filled = data[selected_columns].fillna('Missing')
@@ -126,7 +126,10 @@ async def your_continuous_function(page: int,paginate: int,Tenant: str):
             
             
     # Update the payload for each page
-    url = "https://dev3.api.curfox.parallaxtec.com/api/ml/order-list?sort=id&paginate="+str(paginate)+"&page="+str(page)
+    
+    #url = "https://dev3.api.curfox.parallaxtec.com/api/ml/order-list?sort=id&paginate="+str(paginate)+"&page="+str(page)
+    url = "https://v1.api.curfox.com/api/ml/order-list?sort=id&paginate="+str(paginate)+"&page="+str(page)
+    
             
     payload = {}
     headers = {
@@ -161,7 +164,7 @@ async def your_continuous_function(page: int,paginate: int,Tenant: str):
         
         df.to_csv(f"model/{Tenant}trainer_data.csv")
         print("data created")
-    return {"message":"done","page_number":page,"data_count":data_count}
+    return {"message":"done","page_number":page,"data_count":data_count,'X-Tenant': Tenant}
 
 
     
@@ -194,7 +197,9 @@ def predict(
     pickup_address: str,
     origin_city_name: str,
     destination_city_name: str,
-    origin_country: str):
+    origin_country: str,
+    created_at: str
+    ):
 
     try:
         # Load your trained model and encoders
@@ -223,7 +228,8 @@ def predict(
         'cod': float(cod),
         'weight': float(weight),
         'origin_city.name':origin_city_name,
-        'destination_city.name':destination_city_name
+        'destination_city.name':destination_city_name,
+        'created_at':created_at
     }
     input_df = pd.DataFrame([input_data])