Update app.py

app.py

@@ -16,12 +16,13 @@ st.title("Faculty Collaboration Network Analysis -FY23 & FY24")
 # Load data
 @st.cache_data
 def load_data():
-    award_df = pd.read_csv('award.csv')
-    faculty_college_df = pd.read_csv('faculty_college_20250226.csv')
-    return award_df, faculty_college_df
+    award_df = pd.read_csv('award_cleaned.csv')
+    award_df_remove_amount = pd.read_csv('award_cleaned_remove_amount.csv')
+    faculty_college_df = pd.read_csv('faculty_college_jenny_manual.csv')
+    return award_df, award_df_remove_amount, faculty_college_df
     
 
-award_df, faculty_college_df = load_data()
+award_df, award_df_remove_amount, faculty_college_df = load_data()
 award_df.columns = award_df.columns.str.strip()  # Clean column names
 faculty_college_df = faculty_college_df.apply(lambda x: x.str.strip() if x.dtype == "object" else x)
 
@@ -41,12 +42,31 @@ def create_network(df, college_map):
     # faculty_colleges = defaultdict(list)
     faculty_amounts = defaultdict(float)
 
-    colorblind_palette =[
-    '#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', 
-    '#FFEEAD', '#D4A5A5', '#779ECB', '#FFB347',
-    '#B1DDF3', '#A8E6CF', '#DCEDC1', '#FFD3B6',
-    '#FFAAA5', '#C8C6A7', '#92967D'
-    ]
+    # colorblind_palette =[
+    # '#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', 
+    # '#FFEEAD', '#D4A5A5', '#779ECB', '#FFB347',
+    # '#B1DDF3', '#A8E6CF', '#DCEDC1', '#FFD3B6',
+    # '#FFAAA5', '#C8C6A7', '#92967D'
+    # ]
+
+    colorblind_palette = [
+    '#E6194B',  # Bright red
+    '#3CB44B',  # Green
+    '#4363D8',  # Blue
+    '#FFE119',  # Yellow
+    '#911EB4',  # Purple
+    '#F58231',  # Orange
+    '#42D4F4',  # Cyan
+    '#FABEBE'   # Light pink
+]
+
+
+#     colorblind_palette = [
+#     '#FF1E1E', '#00E5D0', '#00AAFF', '#7AFF86', 
+#     '#FFDD00', '#FF7575', ''#8A2BE2', '#FF9500',
+#     '#83EAFF', '#59FFAA', '#BFFF59', '#FFB380',
+#     '#FF6666', '#E0FF4D', '#66FFC2'
+# ]
 
     for _, row in df.iterrows():
         # Process PI information
@@ -102,7 +122,7 @@ def create_network(df, college_map):
         college_colors[college] = colorblind_palette[i % len(colorblind_palette)]
 
     # Add explicit color for Unknown
-    college_colors['Unknown'] = '#888888'
+    # college_colors['Unknown'] = '#888888'
     # unique_colleges = sorted(list(set(college_map.values())))
     # colormap = plt.cm.get_cmap('tab20', len(unique_colleges))
     # college_colors = {college: to_hex(colormap(i)) for i, college in enumerate(unique_colleges)}
@@ -127,8 +147,7 @@ def create_network(df, college_map):
         college = college_assignment.get(node, 'Unknown')
         G.nodes[node]['color'] = college_colors.get(college_map.get(node, 'Unknown'), '#888888')
         G.nodes[node]['size'] = node_sizes[i]
-        G.nodes[node]['title'] = (f"{node} | College: {college_map.get(node, 'Unknown')}"
-                                  f" | Total Funding: ${faculty_amounts.get(node, 0):,.2f}")
+        G.nodes[node]['title'] = (f"{node} | College: {college_map.get(node, 'Unknown')}")
     
     return G, college_colors
 
@@ -156,7 +175,7 @@ with open('network.html', 'r', encoding='utf-8') as f:
 st.markdown("""
 **Network Interaction Guide:**
 - Drag nodes to rearrange the network
-- Scroll to zoom in/out to see the details: Faculty Name | College | Total Funding
+- Scroll to zoom in/out to see the details: Faculty Name | College 
 - Click and drag background to pan
 - Hover over nodes to see details
 - Use the control panel (click the gear icon) to adjust physics settings
@@ -175,5 +194,8 @@ st.components.v1.html(html, height=800, scrolling=True)
 
 # Show raw data
 st.subheader("Award Data")
-st.dataframe(award_df, use_container_width=True)
+st.dataframe(award_df_remove_amount, use_container_width=True)
+
+st.subheader("Faculty Data")
+st.dataframe(faculty_college_df, use_container_width=True)