Update app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 import pandas as pd
 import Generate
+import plotly.express as px
 
 example_df_aircomp=pd.read_csv('data/aircompressordata.csv')
 example_df_ener=pd.read_csv('data/energymeter.csv')
@@ -20,19 +21,6 @@ def pred_boiler(date):
     preds=Generate.inference_boiler(date,example_df_boiler)
     return preds
 
-def plotgraphsAircomp():
-    preds=Generate.inference_Aircomp(date,example_df_aircomp)
-
-    # Assuming 'df' is your DataFrame object
-    for col in preds.columns[1:]: # skip first column (datetime)
-        plt.figure()
-        plt.plot(df['timestamp'], df[col])
-        plt.xlabel('Datetime')
-        plt.ylabel(f'{col} value')
-        plt.title(f'Prediction of {col} vs Date')
-        
-    return plt
-
 with demo:  
     gr.Markdown("Tool for predicting the next seven days of data in the future using the last 200 points of data incoming")
     with gr.Tabs():
@@ -65,10 +53,20 @@ with demo:
             with gr.Column():
                 Boiler_output_btn=gr.Button("Forecast")
                 boiler_plot_forecast=gr.Button("Plot")
-                
+
+    def plotg_air(dataframe):
+        fig=px.line(dataframe,x='timestamp',y=dataframe.columns,
+                   hover_data={"timestamp":"|%B %d, %Y"},
+                   title="Predictions")
+        fig.update_xaxes(
+            dtick="D1",
+            tickformat="%b\n%Y"
+        )
+        return fig
     Aircomp_output_btn.click(pred_air,inputs=Air_input,outputs=Air_dataframe_output)
     Energy_output_btn.click(pred_ener,inputs=ener_input,outputs=Ener_dataframe_output)
     Boiler_output_btn.click(pred_boiler,inputs=boiler_input,outputs=boiler_dataframe_output)
-    Air_plot_forecast.click(plotgraphAircomp,outputs=Air_plots)
+
+    Air_plot_forecast.click(inputs=Air_dataframe_output,outputs=Air_plots)
     
 demo.launch(share=True)