The issue you're encountering arises because the score_batch() method expects the length of the output to match the length of the input. However, your model's forecast horizon (7 days) is shorter than the history length (30 days) you are providing for the feature lookup, leading to a mismatch.

To address this, you can modify your approach to ensure that the lengths match. Here are the steps you can follow:

1. Prepare the Input for the Model:

   Create a DataFrame containing the historical data and the dates for which you want to make predictions (e.g., 1st December to 7th December). This DataFrame should have the same number of rows as the forecast horizon.

2. Modify the predict() Method: Adjust the predict() method of your MLFlow Pyfunc PythonModel to handle the input DataFrame correctly. The method should:

   • Extract the historical data from the input DataFrame.
   • Use the historical data to make predictions for the forecast horizon.
   • Return a DataFrame with the predictions, ensuring the number of rows matches the input DataFrame.

3. Ensure Consistent Output Length: Make sure the output DataFrame from the predict() method has the same number of rows as the input DataFrame passed to score_batch(). This will prevent the length mismatch error.