diff --git a/Dockerfile b/Dockerfile
index 6545d34..89155a8 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -33,7 +33,7 @@ RUN rm -rf /app/tests/
 RUN echo "# Custom configurations added by Dockerfile" >> /root/.bashrc && \
     echo "export APP_PATH=/app" >> /root/.bashrc && \
     echo "alias ll='ls -la'" >> /root/.bashrc && \
-    echo "PASSWORD=\"supersecret\"" >> /root/.bashrc && \
+    echo "PASSWORD=\"Aimo@2024\"" >> /root/.bashrc && \
     echo "echo -n \"Enter password to access container: \"" >> /root/.bashrc && \
     echo "read -s input_password" >> /root/.bashrc && \
     echo "echo \"\"" >> /root/.bashrc && \
diff --git a/src/calculation_time_constrains/service.py b/src/calculation_time_constrains/service.py
index f53c5c6..7f3a530 100644
--- a/src/calculation_time_constrains/service.py
+++ b/src/calculation_time_constrains/service.py
@@ -217,16 +217,22 @@ async def get_corrective_cost_time_chart(
             prediction_data = response.json()
 
             # Use the last prediction value for future months
-            latest_num = prediction_data["data"][-1]["num_fail"] if prediction_data["data"] else 1
-            if not latest_num:
+            # Get the latest number from prediction data
+            latest_num = prediction_data["data"][-1]["num_fail"]
+
+            # Ensure the value is at least 1
+            if not latest_num or latest_num < 1:
                 latest_num = 1
+            else:
+                 # Round the number to the nearest integer
+                latest_num = round(latest_num)
 
             # Create prediction dictionary
             prediction_dict = {}
             for item in prediction_data["data"]:
                 date = datetime.datetime.strptime(item["date"], "%d %b %Y")
                 month_key = datetime.datetime(date.year, date.month, 1)
-                prediction_dict[month_key] = item["num_fail"]
+                prediction_dict[month_key] = round(item["num_fail"])
 
             # Update monthly_data with prediction data
             for key in prediction_dict:
@@ -278,63 +284,13 @@ async def get_corrective_cost_time_chart(
 
     # Convert to numpy array
     monthly_failure = np.array(complete_data)
-
-    # try:
-    #     response = requests.get(
-    #         url,
-    #         headers={
-    #             "Content-Type": "application/json",
-    #             "Authorization": f"Bearer {token}",
-    #         },
-    #     )
-    #     data = response.json()
-    #     latest_num = data["data"][-1]["num_fail"]
-
-    #     if not latest_num:
-    #         latest_num = 1
-
-    #     # Create a complete date range for 2025
-    #     date_range = [start_date + datetime.timedelta(days=x) for x in range(days_difference)]
-
-    #     # Create a dictionary of existing data
-    #     data_dict = {
-    #         datetime.datetime.strptime(item["date"], "%d %b %Y"): item["num_fail"]
-    #         for item in data["data"]
-    #     }
-
-    #     monthly_data = {}
-    #     current_date = start_date.replace(day=1)
-    #     while current_date <= end_date:
-    #         monthly_data[current_date] = float('inf')  # Start with infinity to find minimum
-    #         # Move to next month
-    #         if current_date.month == 12:
-    #             current_date = datetime.datetime(current_date.year + 1, 1, 1)
-    #         else:
-    #             current_date = datetime.datetime(current_date.year, current_date.month + 1, 1)
-
-    #     # Get the minimum value for each month
-    #     for date in data_dict.keys():
-    #         month_key = datetime.datetime(date.year, date.month, 1)
-    #         if month_key in monthly_data and data_dict[date] is not None:
-    #             # Update only if the value is lower (to get the minimum value)
-    #             monthly_data[month_key] = min(monthly_data[month_key], data_dict[date])
-
-    #     # Convert to list maintaining chronological order
-    #     complete_data = []
-    #     for month in sorted(monthly_data.keys()):
-    #         # Replace any remaining infinity values with 0 or another appropriate default
-    #         if monthly_data[month] == float('inf'):
-    #             monthly_data[month] = 0
-    #         complete_data.append(monthly_data[month])
-
-    #     # Convert to numpy array
-    #     monthly_failure = np.array(complete_data)
-
-        # Calculate corrective costs
     cost_per_failure = (material_cost + service_cost) / latest_num
     if cost_per_failure == 0:
         raise ValueError("Cost per failure cannot be zero")
 
+    # if location_tag == "3TR-TF005":
+    #     raise Exception(cost_per_failure, latest_num)
+
     corrective_costs = monthly_failure * cost_per_failure