fixed cosmetics

Removed the broken st.experimental_rerun() and replaced it with st.session_state logic to reset filters.

Wrapped long row labels into multiple lines via a helper (using <br>), which compacts the heatmap.

Simplified Cell Details: clicking lists only the names & values in a table—no extra plots.

app.py

@@ -1,7 +1,7 @@
 import streamlit as st
 import pandas as pd
 import numpy as np
-import base64, pickle
+import base64, pickle, textwrap
 import plotly.express as px
 from streamlit_plotly_events import plotly_events
 
@@ -14,35 +14,58 @@ df_encoded = "gASVJhkAAAAAAACMEXBhbmRhcy5jb3JlLmZyYW1llIwJRGF0YUZyYW1llJOUKYGUfZ
 filled_matrices = pickle.loads(base64.b64decode(filled_matrices_encoded))
 df = pickle.loads(base64.b64decode(df_encoded))
 
+# ---------------------------------------------------
+# Helper: wrap long labels
+# ---------------------------------------------------
+def wrap_labels(labels, width=40):
+    return ["<br>".join(textwrap.wrap(lbl, width)) for lbl in labels]
+
 # ---------------------------------------------------
 # Sidebar: Filters
 # ---------------------------------------------------
 st.sidebar.title("Filters")
-if st.sidebar.button("Clear All Filters"):
-    st.experimental_rerun()
+# Ensure session state keys exist
+if 'standing' not in st.session_state:
+    st.session_state.standing = sorted(df['Standing'].dropna().astype(str).unique())
+if 'dept' not in st.session_state:
+    st.session_state.dept = sorted(df['Dept Track'].dropna().astype(str).unique())
+
+clear = st.sidebar.button("Clear All Filters")
+if clear:
+    st.session_state.standing = sorted(df['Standing'].dropna().astype(str).unique())
+    st.session_state.dept = sorted(df['Dept Track'].dropna().astype(str).unique())
 
 standing_opts = sorted(df['Standing'].dropna().astype(str).unique())
 dept_opts = sorted(df['Dept Track'].dropna().astype(str).unique())
 
 standing_sel = st.sidebar.multiselect(
-    "Filter by Standing:", standing_opts,
-    default=standing_opts
+    "Filter by Standing:",
+    options=standing_opts,
+    default=st.session_state.standing,
+    key='standing'
 )
 dept_sel = st.sidebar.multiselect(
-    "Filter by Dept Track:", dept_opts,
-    default=dept_opts
+    "Filter by Dept Track:",
+    options=dept_opts,
+    default=st.session_state.dept,
+    key='dept'
 )
 
-# Auto‑filter names based on Standing & Dept
+# Auto-filter names based on filters
 mask = (
     df['Standing'].astype(str).isin(standing_sel) &
     df['Dept Track'].astype(str).isin(dept_sel)
 )
-all_names = sorted(df['Name'].astype(str).unique())
 name_options = sorted(df.loc[mask, 'Name'].astype(str).unique())
+
+if 'names' not in st.session_state or clear:
+    st.session_state.names = name_options
+
 name_sel = st.sidebar.multiselect(
-    "Select Faculty:", name_options,
-    default=name_options
+    "Select Faculty:",
+    options=name_options,
+    default=st.session_state.names,
+    key='names'
 )
 
 # ---------------------------------------------------
@@ -50,26 +73,30 @@ name_sel = st.sidebar.multiselect(
 # ---------------------------------------------------
 st.title("Faculty Heatmap Explorer")
 
-# 1) Heatmap
+# 1) Heatmap display
 if not name_sel:
-    st.write("**No faculty selected** — please select at least one name.")
+    st.warning("No faculty selected — please choose at least one.")
     fig = px.imshow(
         [[0]], labels={'x':'','y':'','color':'value'},
         text_auto='.2f', title="No faculty selected"
     )
     st.plotly_chart(fig, use_container_width=True)
 else:
-    # Sum and average matrices
+    # Sum & average
     sum_df = None
     for name in name_sel:
         mat = filled_matrices[name]
         sum_df = mat if sum_df is None else sum_df.add(mat, fill_value=0)
     avg_df = sum_df.div(len(name_sel))
 
+    # Wrap y labels (rows)
+    wrapped_y = wrap_labels(list(avg_df.index), width=40)
+    wrapped_x = list(avg_df.columns)  # x labels usually short
+
     fig = px.imshow(
-        avg_df,
-        x=avg_df.columns,
-        y=avg_df.index,
+        avg_df.values,
+        x=wrapped_x,
+        y=wrapped_y,
         labels={'color':'Avg value'},
         text_auto='.2f',
         title=f"Avg Heatmap for {len(name_sel)} Faculty"
@@ -77,60 +104,37 @@ else:
     fig.update_yaxes(autorange='reversed')
     st.plotly_chart(fig, use_container_width=True)
 
-    # 2) Click to show details
+    # 2) Click-to-list values
     st.subheader("Cell Details")
     events = plotly_events(fig, click_event=True, key="heatmap")
     if events:
-        x_lab = events[0]['x']
-        y_lab = events[0]['y']
+        # Determine original row & col from pointNumber
+        n_cols = avg_df.shape[1]
+        pt = events[0]
+        pt_num = pt.get('pointNumber', None)
+        if pt_num is not None:
+            row_idx = pt_num // n_cols
+            col_idx = pt_num % n_cols
+            y_lab = avg_df.index[row_idx]
+            x_lab = avg_df.columns[col_idx]
+        else:
+            # fallback: use string labels (unlikely)
+            y_lab_wrapped = pt['y']
+            x_lab = pt['x']
+            # map back wrapped to original
+            y_lab = avg_df.index[wrapped_y.index(y_lab_wrapped)]
 
         # Gather non-zero values
         records = []
         for name in name_sel:
             val = filled_matrices[name].at[y_lab, x_lab]
             if val != 0:
-                row = df[df['Name']==name].iloc[0]
-                records.append({
-                    'Name': name,
-                    'Value': val,
-                    'Dept Track': str(row['Dept Track']),
-                    'Standing': str(row['Standing'])
-                })
+                records.append({'Name': name, 'Value': val})
 
         if records:
-            detail_df = pd.DataFrame(records)
-            # Dept Track distribution
-            dept_df = (
-                detail_df.groupby(['Dept Track','Value'])
-                         .size()
-                         .reset_index(name='Count')
-            )
-            fig_dept = px.line(
-                dept_df,
-                x='Value', y='Count',
-                color='Dept Track', markers=True,
-                title=f"Distribution by Dept Track (x={x_lab}, y={y_lab})"
-            )
-            st.plotly_chart(fig_dept, use_container_width=True)
-
-            # Standing distribution
-            stand_df = (
-                detail_df.groupby(['Standing','Value'])
-                         .size()
-                         .reset_index(name='Count')
-            )
-            fig_st = px.line(
-                stand_df,
-                x='Value', y='Count',
-                color='Standing', markers=True,
-                title=f"Distribution by Standing (x={x_lab}, y={y_lab})"
-            )
-            st.plotly_chart(fig_st, use_container_width=True)
-
-            # Table of non-zero values
-            table_df = detail_df[['Name','Value']].sort_values('Value', ascending=False)
-            st.dataframe(table_df)
+            detail_df = pd.DataFrame(records).sort_values('Value', ascending=False)
+            st.table(detail_df)
         else:
-            st.write("**No non-zero values for this cell.**")
+            st.info("No non-zero values for this cell.")
     else:
-        st.write("Click on a heatmap cell to see Dept/Standing distributions & values.")
+        st.write("Click on a heatmap cell to list faculty and their values.")