Update streamlit_simulation/app.py

streamlit_simulation/app.py

@@ -183,15 +183,14 @@ def predict_transformer_step(model, dataset, idx, device):
 
 
 def init_simulation_layout():
-    """Creates layout containers for plot and info sections."""
-    col1, spacer, col2 = st.columns([3, 0.2, 1])
-    plot_title = col1.empty()
-    plot_container = col1.empty()
-    x_axis_label = col1.empty()
-    info_container = col2.empty()
+    plot_title = st.empty()
+    plot_container = st.empty()
+    x_axis_label = st.empty()
+    info_container = st.container()
     return plot_title, plot_container, x_axis_label, info_container
 
 
+
 def create_prediction_plot(pred_timestamps, pred_vals, true_timestamps, true_vals, window_hours, y_min=None, y_max=None):
     """Generates the matplotlib figure for plotting prediction vs. actual."""
     fig, ax = plt.subplots(figsize=(8, 5), constrained_layout=False, facecolor=PLOT_COLOR)
@@ -243,37 +242,37 @@ def render_simulation_view(timestamp, prediction, actual, progress, fig, paused=
         unsafe_allow_html=True
     )
 
-    with info_container.container():
-        st.markdown("<div style='margin-top: 5rem;'></div>", unsafe_allow_html=True)
+    with info_container:
+        st.markdown("---")
         st.markdown(
             f"<span style='font-size: 24px; font-weight: 600; color: {HEADER_COLOR} !important;'>Time: {timestamp}</span>",
             unsafe_allow_html=True
         )
-
+    
         st.metric("Prediction", f"{prediction:,.0f} MW" if prediction is not None else "–")
         st.metric("Actual", f"{actual:,.0f} MW" if actual is not None else "–")
         st.caption("Simulation Progress")
         st.progress(progress)
 
-        if len(st.session_state.true_vals) > 1:
-            true_arr = np.array(st.session_state.true_vals)
-            pred_arr = np.array(st.session_state.pred_vals[:-1])
-
-            min_len = min(len(true_arr), len(pred_arr)) #just start if there are 2 actual values
-            if min_len >= 1:
-                errors = np.abs(true_arr[:min_len] - pred_arr[:min_len])
-                mape = np.mean(errors / np.where(true_arr[:min_len] == 0, 1e-10, true_arr[:min_len])) * 100
-                mae = np.mean(errors)
-                max_error = np.max(errors)
-
-                st.divider()
-                st.markdown(
-                    f"<span style='font-size: 24px; font-weight: 600; color: {HEADER_COLOR} !important;'>Interim Metrics</span>",
-                    unsafe_allow_html=True
-                )
-                st.metric("MAPE (so far)", f"{mape:.2f} %")
-                st.metric("MAE (so far)", f"{mae:,.0f} MW")
-                st.metric("Max Error", f"{max_error:,.0f} MW")
+        #if len(st.session_state.true_vals) > 1:
+           # true_arr = np.array(st.session_state.true_vals)
+            #pred_arr = np.array(st.session_state.pred_vals[:-1])
+
+           # min_len = min(len(true_arr), len(pred_arr)) #just start if there are 2 actual values
+           # if min_len >= 1:
+               # errors = np.abs(true_arr[:min_len] - pred_arr[:min_len])
+                #mape = np.mean(errors / np.where(true_arr[:min_len] == 0, 1e-10, true_arr[:min_len])) * 100
+                #mae = np.mean(errors)
+                #max_error = np.max(errors)
+
+               #st.divider()
+                #st.markdown(
+                #    f"<span style='font-size: 24px; font-weight: 600; color: {HEADER_COLOR} !important;'>Interim Metrics</span>",
+                #    unsafe_allow_html=True
+               # )
+                #st.metric("MAPE (so far)", f"{mape:.2f} %")
+               # st.metric("MAE (so far)", f"{mae:,.0f} MW")
+               #st.metric("Max Error", f"{max_error:,.0f} MW")