bug fix

app.py

@@ -111,10 +111,37 @@ def main():
     # Start App
     st.title("Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting")
 
-
     image = Image.open('data/image.png')
-
     st.image(image, caption='Coding.Waterkant Festival for AI')
+
+    st.markdown(body = """
+        ### Experiments
+        We implemented TFT for sales multi-horizon sales forecast during Coding.Waterkant. 
+        Please try our implementation and adjust some of the training data.
+
+        Adjustments to the model and extention with Quantile forecast are coming soon ;) 
+    """)
+
+    RAIN_MAPPING = {
+        "Yes" : 1,
+        "No" : 0
+    }
+    
+    parameters, df = load_data()
+    model = init_model()
+    
+    datepicker = st.date_input("Start of Forecast", value = 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")
+    temperature = st.slider('Change in Temperature', min_value=-10.0, max_value=10.0, value=0.0, step=0.25, key = "temperature")
+    rain = st.radio("Rain Indicator", ('Default', 'Yes', 'No'), key = "rain")
+
+    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)
+    
     st.markdown(body = """
         ### Abstract
         Multi-horizon forecasting often contains a complex mix of inputs – including
@@ -141,37 +168,6 @@ def main():
         Adjustments to the model and extention with Quantile forecast are coming soon ;) 
     """)
     
-    try:
-    # check if the key exists in session state
-        _ = st.session_state.rain
-        _ = st.session_state.temperature
-        _ = st.session_state.date
-    except AttributeError:
-        # otherwise set it to false
-        st.session_state.rain = 'Default'
-        st.session_state.temperature = 0.0
-#        st.session_state.date = datetime.date(2022, 10, 24)
-
-    RAIN_MAPPING = {
-        "Yes" : 1,
-        "No" : 0
-    }
-    
-    parameters, df = load_data()
-    model = init_model()
-
-    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)
-    datepicker = st.date_input("Start of Forecast", value = 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")
-
-    st.pyplot(fig)
-    
-    temperature = st.slider('Change in Temperature', min_value=-10.0, max_value=10.0, value=st.session_state.temperature, step=0.25, key = "temperature")
-    rain = st.radio("Rain Indicator", ('Default', 'Yes', 'No'), key = "rain")
-    
 if __name__ == '__main__':
     main()