Update app.py

app.py

@@ -42,29 +42,22 @@ def predictPPM(df, split):
     print(predictions)
     
     #combine all into one table
-    ts_df=df.copy()
-    ts_df.rename(columns={'#PPM':'PPM'},inplace=True)
+    ts_df=df
     train= ts_df[:int (len(ts_df)*ttSplit)]
     test= ts_df[int(len(ts_df)*ttSplit):] 
 
     df2['Date']=pd.to_datetime(df2['Date'])
     df2.rename(columns={'#PPM':'PPM'},inplace=True)
-    df3=predictions
-    df2['origin']='ground truth'
+    df3= predictions
+    df2['origin']='status '
     df3['origin']='prediction'
     df4=pd.concat([df2, df3])
     print(df4)
     return df4
-
-demo=gr.Interface(
-    fn =predictPPM,inputs = [gr.Blocks(label="Input for the timeseries"),
+demo = gr.Interface(
+    fn =predictPPM,inputs = [gr.UploadButton(label="Input data for PPM TimeSeries"),
         gr.Slider(1, 100, value=75, step=1, label="Train test split percentage"),
     ],
-    outputs=[gr.LinePlot(x='Date', y='PPM', color='origin')#gr.Timeseries(x='Month')
-
-    ],
-    examples=[
-        [os.path.join(os.path.abspath(''), "datappm.csv"), 75],
-    ]
+    outputs=gr.LinePlot(x='Date', y='PPM', color='origin')
 )
-demo.launch()
+demo.launch(debug=True)