sort date index

app.py

@@ -14,23 +14,41 @@ def offset_calculation(prediction_length, rolling_windows, length):
     return row_offset
 
 
-def preprocess(input_data, prediction_length, rolling_windows, progress=gr.Progress(track_tqdm=True)):
+def preprocess(
+    input_data,
+    prediction_length,
+    rolling_windows,
+    item_id,
+    progress=gr.Progress(track_tqdm=True),
+):
     df = pd.read_csv(input_data.name, index_col=0, parse_dates=True)
+    df.sort_index(inplace=True)
     row_offset = offset_calculation(prediction_length, rolling_windows, len(df))
     return plot_train_test(df.iloc[:row_offset], df.iloc[row_offset:])
 
 
-def train_and_forecast(input_data, prediction_length, rolling_windows, epochs, progress=gr.Progress(track_tqdm=True)):
+def train_and_forecast(
+    input_data,
+    prediction_length,
+    rolling_windows,
+    epochs,
+    progress=gr.Progress(track_tqdm=True),
+):
     if not input_data:
         raise gr.Error("Upload a file with the Upload button")
     try:
         df = pd.read_csv(input_data.name, index_col=0, parse_dates=True)
+        df.sort_index(inplace=True)
     except AttributeError:
         raise gr.Error("Upload a file with the Upload button")
 
     row_offset = offset_calculation(prediction_length, rolling_windows, len(df))
 
-    gluon_df = PandasDataset(df, target=df.columns[0])
+    try:
+        gluon_df = PandasDataset(df, target=df.columns[0])
+    except TypeError:
+        freq = pd.infer_freq(df.index[:3])
+        gluon_df = PandasDataset(df, target=df.columns[0], freq=freq)
 
     training_data, test_gen = split(gluon_df, offset=row_offset)