Update app.py

app.py

@@ -58,7 +58,7 @@ if ("0" != ""):
 			break
 	adf = pd.read_csv(StringIO(csvString), sep=",")
 	df = adf
-	df.columns = df.columns.to_series().apply(lambda x: x.strip())
+#	df.columns = df.columns.to_series().apply(lambda x: x.strip())
 #	df = adf.reindex(columns=mytitles)
 #	df = adf.reset_index()
 #	df = adf.reindex(pd.RangeIndex(0, mybacklogmax).to_series())
@@ -182,12 +182,12 @@ end = start + train_split
 #train_data = train_data.reset_index()
 #train_data = train_data.reindex(pd.RangeIndex(0, mybacklogmax).to_series())
 #acolumns = train_data.columns
-acolumns = pd.RangeIndex(0, mybacklogmax).to_series()
-st.write(acolumns)
-train_data = train_data.reindex(columns=acolumns)
+myrangeend = mybacklogmax
+mycolumns = pd.RangeIndex(0, myrangeend).to_series()
+train_data = train_data.reindex(columns=mycolumns)
 #train_data[acolumns] = train_data[acolumns].astype(int)
 
-x_train = train_data[[i for i in range(mybacklogmax)]].values
+x_train = train_data[[i for i in range(myrangeend)]].values
 y_train = features.iloc[start:end][[1]]
 
 sequence_length = int(past / step)
@@ -195,7 +195,7 @@ x_end = len(val_data) - past - future
 
 label_start = train_split + past + future
 
-x_val = val_data.iloc[:x_end][[i for i in range(mybacklogmax)]].values
+x_val = val_data.iloc[:x_end][[i for i in range(myrangeend)]].values
 y_val = features.iloc[label_start:][[1]]
 
 dataset_val = keras.preprocessing.timeseries_dataset_from_array(