Merge pull request #3 from Avaiga/feature/doc#986-document-best-pract…

…ice-on-main-script Feature/doc#986 - Document best practice on main script
Avaiga · Jul 29, 2024 · 80b7d09 · 80b7d09
2 parents c3a4ad2 + 30a4cb3
commit 80b7d09
Showing 1 changed file with 46 additions and 47 deletions.
diff --git a/src/main.py b/src/main.py
@@ -4,77 +4,76 @@
 
 # Create a Taipy App that will output the 7 best movies for a genre
 
-# Taipy Core - backend definition
-
 # Filter function for Task
 def filtering_genre(initial_dataset: pd.DataFrame, selected_genre):
     filtered_dataset = initial_dataset[initial_dataset['genres'].str.contains(selected_genre)]
     filtered_data = filtered_dataset.nlargest(7, 'Popularity %')
     return filtered_data
 
 
-# Input Data Nodes configuration
-initial_dataset_cfg = Config.configure_data_node(id="initial_dataset",
-                                                 storage_type="csv",
-                                                 path="data.csv",
-                                                 scope=Scope.GLOBAL)
-
-selected_genre_cfg = Config.configure_data_node(id="selected_genre_node",
-                                                 default_data="ACTION",
-                                                 scope=Scope.GLOBAL)
+# Callback definition
+def modify_df(state):
+    scenario.selected_genre_node.write(state.selected_genre)
+    tp.submit(scenario)
+    state.df = scenario.filtered_data.read()    
 
-# Output Data Node configuration
-filtered_data_cfg = Config.configure_data_node(id="filtered_data",
-                                                 scope=Scope.GLOBAL)
 
+if __name__ == "__main__":
+    # Taipy Core - backend definition
 
-# Task configuration
-filter_task_cfg = Config.configure_task(id="filter_genre",
-                                            function=filtering_genre,
-                                            input=[initial_dataset_cfg, selected_genre_cfg],
-                                            output=filtered_data_cfg,
-                                            skippable=True)
+    # Input Data Nodes configuration
+    initial_dataset_cfg = Config.configure_data_node(id="initial_dataset",
+                                                    storage_type="csv",
+                                                    path="data/data.csv",
+                                                    scope=Scope.GLOBAL)
 
-# Pipeline configuration
-pipeline_cfg = Config.configure_pipeline(id="pipeline",
-                                         task_configs=[filter_task_cfg])
-# Scenario configuration
-scenario_cfg = Config.configure_scenario(id="scenario", pipeline_configs=[pipeline_cfg])
+    selected_genre_cfg = Config.configure_data_node(id="selected_genre_node",
+                                                    default_data="ACTION",
+                                                    scope=Scope.GLOBAL)
 
-# Run of the Taipy Core service
-tp.Core().run()
+    # Output Data Node configuration
+    filtered_data_cfg = Config.configure_data_node(id="filtered_data",
+                                                    scope=Scope.GLOBAL)
 
-# Creation of my scenario
-scenario = tp.create_scenario(scenario_cfg)
+    # Task configuration
+    filter_task_cfg = Config.configure_task(id="filter_genre",
+                                                function=filtering_genre,
+                                                input=[initial_dataset_cfg, selected_genre_cfg],
+                                                output=filtered_data_cfg,
+                                                skippable=True)
 
+    # Pipeline configuration
+    pipeline_cfg = Config.configure_pipeline(id="pipeline",
+                                            task_configs=[filter_task_cfg])
+    # Scenario configuration
+    scenario_cfg = Config.configure_scenario(id="scenario", pipeline_configs=[pipeline_cfg])
 
+    # Run of the Taipy Core service
+    tp.Core().run()
 
-# Taipy GUI- front end definition
+    # Creation of my scenario
+    scenario = tp.create_scenario(scenario_cfg)
 
-# Callback definition
-def modify_df(state):
-    scenario.selected_genre_node.write(state.selected_genre)
-    tp.submit(scenario)
-    state.df = scenario.filtered_data.read()    
+    # Taipy GUI- front end definition
 
-# Get list of genres
-list_genres = ['Action', 'Adventure', 'Animation', 'Children', 'Comedy', 'Fantasy', 'IMAX', 'Romance',
-               'Sci-FI', 'Western', 'Crime', 'Mystery', 'Drama', 'Horror', 'Thriller', 'Film-Noir',
-               'War', 'Musical', 'Documentary']
+    # Get list of genres
+    list_genres = ['Action', 'Adventure', 'Animation', 'Children', 'Comedy', 'Fantasy', 'IMAX', 'Romance',
+                'Sci-FI', 'Western', 'Crime', 'Mystery', 'Drama', 'Horror', 'Thriller', 'Film-Noir',
+                'War', 'Musical', 'Documentary']
 
-# Initialization of variables
-df = pd.DataFrame(columns=['Title', 'Popularity %'])
-selected_genre = None
+    # Initialization of variables
+    df = pd.DataFrame(columns=['Title', 'Popularity %'])
+    selected_genre = None
 
-# movie_genre_app
-movie_genre_app = """
+    # movie_genre_app
+    movie_genre_app = """
 # Film recommendation
 
 ## Choose your favorite genre
 <|{selected_genre}|selector|lov={list_genres}|on_change=modify_df|dropdown|>
 
 ## Here are the top 7 picks
 <|{df}|chart|x=Title|y=Popularity %|type=bar|title=Film Popularity|>
-"""
-# run the app
-Gui(page=movie_genre_app).run()
+    """
+    # run the app
+    Gui(page=movie_genre_app).run()