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app.py

@@ -58,11 +58,13 @@ def predict_capacity():
 
         # Convert to DataFrame
         input_data = pd.DataFrame([sample])
+        input_data["Date"] = pd.to_datetime(input_data["Date"])  # ⚡ convert to datetime
         data_set = input_data.copy()  # optional backup
 
         # Drop any IDs if your pipeline doesn’t need them
         # input_data.drop(["Store_Id"], axis=1, inplace=True)  # example
 
+
         # Predict using the trained pipeline
         prediction = pipeline.predict(input_data).tolist()[0]
 
@@ -116,6 +118,7 @@ def predict_capacity_batch():
 
         # Convert list of dicts to DataFrame
         input_data = pd.DataFrame(data_list)
+        input_data["Date"] = pd.to_datetime(input_data["Date"])  # ⚡ convert to datetime
 
         # Predict using pipeline
         predictions = pipeline.predict(input_data).tolist()