make linter happy

Signed-off-by: Niels Bantilan <[email protected]>

examples/time_series_modeling/time_series_modeling/neural_prophet.py

@@ -10,7 +10,7 @@
 # First, we import necessary libraries to run the training workflow.
 
 import pandas as pd
-from flytekit import current_context, task, workflow, Deck, ImageSpec
+from flytekit import Deck, ImageSpec, current_context, task, workflow
 from flytekit.types.file import FlyteFile
 
 # %% [markdown]
@@ -30,7 +30,7 @@
     ],
     # This registry is for a local flyte demo cluster. Replace this with your
     # own registry, e.g. `docker.io/<username>/<imagename>`
-    registry="localhost:30000"
+    registry="localhost:30000",
 )
 
 # %% [markdown]
@@ -41,16 +41,19 @@
 
 URL = "https://github.com/ourownstory/neuralprophet-data/raw/main/kaggle-energy/datasets/tutorial01.csv"
 
+
 @task(container_image=image)
 def load_data() -> pd.DataFrame:
     return pd.read_csv(URL)
 
+
 # %% [markdown]
 # ## Model Training Task
 #
 # This task trains the Neural Prophet model on the loaded data.
 # We train the model in the hourly frequency for ten epochs.
 
+
 @task(container_image=image)
 def train_model(df: pd.DataFrame) -> FlyteFile:
     from neuralprophet import NeuralProphet, save
@@ -62,12 +65,14 @@ def train_model(df: pd.DataFrame) -> FlyteFile:
     save(model, model_fp)
     return FlyteFile(model_fp)
 
+
 # %% [markdown]
 # ## Forecasting Task
 #
 # This task loads the trained model, makes predictions, and visualizes the
 # results using a Flyte Deck.
 
+
 @task(
     container_image=image,
     enable_deck=True,
@@ -77,7 +82,7 @@ def make_forecast(df: pd.DataFrame, model_file: FlyteFile) -> pd.DataFrame:
 
     model_file.download()
     model = load(model_file.path)
-    
+
     # Create a new dataframe reaching 365 into the future
     # for our forecast, n_historic_predictions also shows historic data
     df_future = model.make_future_dataframe(
@@ -95,12 +100,14 @@ def make_forecast(df: pd.DataFrame, model_file: FlyteFile) -> pd.DataFrame:
 
     return forecast
 
+
 # %% [markdown]
 # ## Main Workflow
 #
 # Finally, this workflow orchestrates the entire process: loading data,
 # training the model, and making forecasts.
 
+
 @workflow
 def main() -> pd.DataFrame:
     df = load_data()