bug fix

app.py

@@ -38,6 +38,7 @@ def raw_preds_to_df(raw,quantiles = None):
     preds_df['pred_idx'] = preds_df['time_idx'] + preds_df['h'] - 1
     return preds_df
 
+@st.cache_data
 def prepare_dataset(parameters, df, rain, temperature, datepicker, mapping):
     if rain != "Default":
         df["MTXWTH_Day_precip"] = mapping[rain]
@@ -59,25 +60,21 @@ def predict(_model, _dataloader, datepicker):
     preds = raw_preds_to_df(out, quantiles = None)
     return preds[["pred_idx", "Group", "pred"]]
 
-def update_plot(df, preds, axs, fig):
+@st.cache_data
+def adjust_data_for_plot(df, preds):
     df = pd.merge(df, preds, left_on=["time_idx", "Group"], right_on=["pred_idx", "Group"], how = "left")
     df = df[~df["pred"].isna()]
-    df[["sales", "pred"]] = df[["sales", "pred"]].replace(0.0, np.nan)
+    df["sales"] = df["sales"].replace(0.0, np.nan)
+    return df
 
-    axs[0, 0].plot(df.loc[df['Group'] == '4', 'Date'], df.loc[df['Group'] == '4', 'pred'], color = 'red')
-    axs[0, 1].plot(df.loc[df['Group'] == '7', 'Date'], df.loc[df['Group'] == '4', 'pred'], color = 'red')
-    axs[1, 0].plot(df.loc[df['Group'] == '1', 'Date'], df.loc[df['Group'] == '4', 'pred'], color = 'red')
-    axs[1, 1].plot(df.loc[df['Group'] == '6', 'Date'], df.loc[df['Group'] == '4', 'pred'], color = 'red')
-    return fig, axs
 
-#@st.cache_resource(
 def generate_plot(df):
     fig, axs = plt.subplots(2, 2, figsize=(8, 6))
-    df["sales"] = df["sales"].replace(0.0, np.nan)
+    
     # Plot scatter plots for each group
     axs[0, 0].plot(df.loc[df['Group'] == '4', 'Date'], df.loc[df['Group'] == '4', 'sales'], color='grey')
-    
     axs[0, 0].set_title('Article Group 1')
+    
     axs[0, 1].plot(df.loc[df['Group'] == '7', 'Date'], df.loc[df['Group'] == '7', 'sales'], color='grey')
     axs[0, 1].set_title('Article Group 2')
     
@@ -86,6 +83,12 @@ def generate_plot(df):
     
     axs[1, 1].plot(df.loc[df['Group'] == '6', 'Date'], df.loc[df['Group'] == '6', 'sales'], color='grey')
     axs[1, 1].set_title('Article Group 4')
+
+    axs[0, 0].plot(df.loc[df['Group'] == '4', 'Date'], df.loc[df['Group'] == '4', 'pred'], color = 'red')
+    axs[0, 1].plot(df.loc[df['Group'] == '7', 'Date'], df.loc[df['Group'] == '4', 'pred'], color = 'red')
+    axs[1, 0].plot(df.loc[df['Group'] == '1', 'Date'], df.loc[df['Group'] == '4', 'pred'], color = 'red')
+    axs[1, 1].plot(df.loc[df['Group'] == '6', 'Date'], df.loc[df['Group'] == '4', 'pred'], color = 'red')
+    
     plt.tight_layout()
     return fig, axs
 
@@ -141,14 +144,14 @@ def main():
     
     datepicker = st.date_input("Start of Forecast", datetime.date(2022, 10, 24), min_value=datetime.date(2022, 6, 26) + datetime.timedelta(days = 35), max_value=datetime.date(2023, 6, 26) - datetime.timedelta(days = 30), key = "date")
 
-    fig, axs = generate_plot(df.copy())
-    st.pyplot(fig)
 
     if st.button("Forecast Sales", type="primary"):
         dataloader = prepare_dataset(parameters, df.copy(), st.session_state.rain, st.session_state.temperature, st.session_state.date, rain_mapping)
         preds = predict(model, dataloader, st.session_state.date)
-        update_plot(df, preds, axs, fig)
 
+        data_plot = adjust_data_for_plot(df.copy(), preds)
+        fig, axs = generate_plot(df.copy())
+        st.pyplot(fig)
 
 
 if __name__ == '__main__':