Update app.py

app.py

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+#Importierung Pakete
+import gradio as gr
+import pandas as pd #Verarbeitung excel/csv
+
+from darts.models import LinearRegressionModel #Importierung Klasse LinearRegressionModel von darts
+from darts import TimeSeries #In darts wird für Datensätze das TimeSeries Format verwendet
+
+from sklearn.metrics import mean_absolute_error, r2_score
+
+import os
+import plotly.express as px #Erstellung von Diagrammen (dynamisch --> bspw. zoom)
+
+# Funktion, um die hochgeladene Datei zu lesen
+def upload_excel(file):
+  if file is not None:
+    # Excel-Datei lesen
+    df = pd.read_excel(file.name, index_col='time')
+    return df
+
+# Funktion, um Checkbox-Optionen automatisch zu erstellen
+def create_feature_checkbox(df):
+  if df is not None:
+    new_choices = list(df.columns)
+    return gr.update(choices=new_choices)
+  else:
+    return gr.update(choices=[])
+
+# Funktion, um Trainingsstartdatum automatisch zu laden
+def fill_start_date(df):
+  if df is not None:
+    start_date = str(df.index.min())
+    return gr.update(value=start_date)
+  else:
+    return gr.update(value='')
+
+# Funktion, um Teststartdatum automatisch zu laden
+def fill_end_date(df):
+  if df is not None:
+    end_date = str(df.index.max())
+    return gr.update(value=end_date)
+  else:
+    return gr.update(value='')
+
+#Funktion, läd die excel Datei hoch und führt außerdem die Funktionen create_feature_checkbox, fill_start_date, fill_end_date
+def process_excel(file):
+  df = upload_excel(file)
+  return df, create_feature_checkbox(df), create_feature_checkbox(df), fill_start_date(df), fill_end_date(df) #create_feature_checkbox steht zwei mal drinnen wegen target und covariate
+
+#Funktion training und training/test-split
+def data_split_and_training(df,start_train,end_train,start_test,end_test,target,covariates,lags_target,lags_covariates):
+  #Aufteilen in Train Test
+  train_df = df[start_train:end_train]
+  test_df = df[start_test:end_test]
+
+  #Umwandeln in darts.TimeSeries
+  train = TimeSeries.from_dataframe(train_df)
+  test = TimeSeries.from_dataframe(test_df)
+
+  #Lags Eingabe in Liste umwandeln für Darts Bibliothek
+  lags_target_list = list(range(-1,-lags_target-1,-1))
+  lags_covariates_list = list(range(-1,-lags_covariates-1,-1))
+
+  #model instantiation
+  model = LinearRegressionModel(lags=lags_target_list, lags_future_covariates=lags_covariates_list)
+
+  #model training
+  model_fit = model.fit(train[target], future_covariates= train[covariates])
+  return train, test, model_fit, f'Training is complete'
+
+# Funktion zur Speicherung des trainierten Modells
+def create_file(state_value):
+    file_path = "model_fit.pt"
+    with open(file_path, "w") as f:
+        f.write(f"State Value: {state_value}")
+    return file_path
+
+# Funktion für Prediction
+def test(model_fit,test,train,covariates):
+  #prediction
+  prediction = model_fit.predict(n=len(test), future_covariates= train.append(test)[covariates])
+
+  #converting to pandas
+  df_prediction = prediction.pd_dataframe()
+
+  # Visualisierung mit Plotly
+  fig = px.line()
+  fig.add_scatter(x=df_prediction.index, y=test.pd_dataframe()[df_prediction.columns[0]], mode='lines', name='test', line_color='red')
+  fig.add_scatter(x=df_prediction.index, y=df_prediction[df_prediction.columns[0]], mode='lines', name='predict', line_color='blue')
+  fig.update_yaxes(title_text=df_prediction.columns[0])
+
+  #check metrics
+  target_variable = []
+  MAE = []
+  R2 = []
+  for i in df_prediction.columns:
+    target_variable.append(i)
+    MAE.append(mean_absolute_error(df_prediction[i], test.pd_dataframe()[i]))
+    R2.append(r2_score(df_prediction[i], test.pd_dataframe()[i]))
+  metrics = pd.DataFrame({'target':target_variable,'MAE': MAE, 'R2': R2})
+  return metrics, fig
+
+
+
+# Gradio-Interface erstellen
+with gr.Blocks() as demo:
+  gr.Markdown("## ML-based Building Simulation") #Überschrift
+
+  # Datei-Upload-Komponente
+  file_input = gr.File(label="Drop an excel file", file_types=[".xls", ".xlsx"]) #Hochladebereich
+
+  #Upload Button
+  upload_button = gr.Button("Upload Excel") #Button für Funktion proceed_excel()
+
+  #Feature checkboxen erstellen
+  checkbox_group_target = gr.CheckboxGroup(label="Select target variable", choices=[], interactive=True) #Auswahl target variable
+  checkbox_group_covariate = gr.CheckboxGroup(label="Select exogenous variables", choices=[], interactive=True) #Auswahl covariate variables
+
+  with gr.Row():
+    # Eingabe Teilung Training und Testdaten
+    start_train = gr.Textbox(label='start_train:', lines=1, interactive=True)
+    end_train = gr.Textbox(label='end_train:', lines=1, interactive=True, value = '2019-06-04 02:15:00')
+    start_test = gr.Textbox(label='start_test:', lines=1, interactive=True, value = '2019-06-04 02:30:00')
+    end_test = gr.Textbox(label='end_test:', lines=1, interactive=True)
+
+  # Eingabe Verzögerung
+  with gr.Row():
+    lags_target = gr.Number(value=1, label='Number of laged values for target', interactive=True)
+    lags_covariates = gr.Number(value=1, label='Number of laged values for exogenous variables', interactive=True)
+
+  # DataFrame df wird als Zustand gespeichert, damit er in in anderen Funktionen weiter verwendet werden kann
+  df_state = gr.State(None)
+  train_state = gr.State(None)
+  test_state = gr.State(None)
+  model_fit_state = gr.State(None)
+
+  #Training Button
+  training_button = gr.Button("Training") #Button für Funktion data_split_and_training()
+
+  with gr.Row():
+    #Training Status
+    status_output = gr.Textbox(label="Training status")
+    with gr.Column():
+      #Download Button
+      download_button = gr.DownloadButton("Download model")
+      test_button = gr.Button("Test")
+
+  # Tabellenausgabe Metrik
+  metrics_table = gr.DataFrame(label="Metrics", headers=['target','MAE','R2'])
+
+  # Plotausgabe Vorhersagen
+  plot = gr.Plot()
+
+  # Event-Verknüpfungen
+  upload_button.click(process_excel, inputs=file_input, outputs=[df_state, checkbox_group_target, checkbox_group_covariate, start_train, end_test])
+  training_button.click(data_split_and_training,
+                        inputs=[df_state, start_train, end_train, start_test, end_test, checkbox_group_target, checkbox_group_covariate, lags_target, lags_covariates],
+                        outputs=[train_state, test_state, model_fit_state, status_output])
+  download_button.click(create_file,inputs=[model_fit_state],outputs=[download_button])
+  test_button.click(test,inputs=[model_fit_state,test_state,train_state,checkbox_group_covariate],outputs=[metrics_table,plot])
+
+# Demo starten
+demo.launch(show_error=True)