bug fix

app.py

@@ -109,14 +109,6 @@ def init_model():
     return model
     
 def main():
-    ## Initiate Data
-    parameters, df = load_data()
-    model = init_model()
-    rain_mapping = {
-        "Yes" : 1,
-        "No" : 0
-    }
-    
     # Start App
     st.title("Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting")
     
@@ -141,29 +133,36 @@ def main():
     
         ### Experiments
     """)
-    rain = st.radio("Rain Indicator", ('Default', 'Yes', 'No'), key = "rain")
-    
-    temperature = st.slider('Change in Temperature', min_value=-10.0, max_value=10.0, value=0.0, step=0.25, key = "temperature")
     
-    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")
-
-
     try:
     # check if the key exists in session state
-        _ = st.session_state.pressed
+        _ = st.session_state.rain
+        _ = st.session_state.temperature
+        _ = st.session_state.date
     except AttributeError:
         # otherwise set it to false
-        st.session_state.pressed = False
+        st.session_state.rain = 'Default'
+        st.session_state.temperature = 0.0
+        st.session_state.date = datetime.date(2022, 10, 24)
 
-    if st.button("Forecast Sales", type="primary") or st.session_state.pressed:
-        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)
+    RAIN_MAPPING = {
+        "Yes" : 1,
+        "No" : 0
+    }
+    
+    parameters, df = load_data()
+    model = init_model()
 
-        data_plot = adjust_data_for_plot(df.copy(), preds)
-        fig, axs = generate_plot(data_plot)
-        st.session_state.pressed = True
-        st.pyplot(fig)
+    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)
 
+    data_plot = adjust_data_for_plot(df.copy(), preds)
+    fig, _ = generate_plot(data_plot)
+    
+    st.pyplot(fig)
+    rain = st.radio("Rain Indicator", ('Default', 'Yes', 'No'), key = "rain")
+    temperature = st.slider('Change in Temperature', min_value=-10.0, max_value=10.0, value=0.0, step=0.25, key = "temperature")
+    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")
 
 if __name__ == '__main__':
     main()