Update streamlit_simulation/app.py

streamlit_simulation/app.py

@@ -183,18 +183,18 @@ def predict_transformer_step(model, dataset, idx, device):
 
 
 def init_simulation_layout():
-    plot_title = st.empty()
-    plot_container = st.empty()
-    x_axis_label = st.empty()
-    info_container = st.container()
+    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()
     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)
-    fig.tight_layout()
+    fig, ax = plt.subplots(figsize=(8, 5), constrained_layout=True, facecolor=PLOT_COLOR)
     ax.set_facecolor(PLOT_COLOR)
 
     ax.plot(pred_timestamps[-window_hours:], pred_vals[-window_hours:], label="Prediction", color="#EF233C", linestyle="--")
@@ -233,7 +233,7 @@ def render_simulation_view(timestamp, prediction, actual, progress, fig, paused=
         f"{title}</div>",
         unsafe_allow_html=True
     )
-    plot_container.pyplot(fig, use_container_width=True)
+    plot_container.pyplot(fig)
 
     st.markdown("<div style='margin-bottom: 0.5rem;'></div>", unsafe_allow_html=True)
     x_axis_label.markdown(
@@ -254,25 +254,25 @@ def render_simulation_view(timestamp, prediction, actual, progress, fig, paused=
         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")