Create app.py

app.py

@@ -0,0 +1,151 @@
+import streamlit as st
+import pandas as pd
+import networkx as nx
+from pyvis.network import Network
+from matplotlib.colors import to_hex
+import matplotlib.pyplot as plt
+import numpy as np
+from collections import defaultdict
+
+# Set page configuration
+st.set_page_config(layout="wide")
+
+# Title
+st.title("Faculty Collaboration Network Analysis -FY23 & FY24")
+
+# Load data
+@st.cache_data
+def load_data():
+    return pd.read_csv('award.csv')
+
+df = load_data()
+df.columns = df.columns.str.strip()  # Clean column names
+
+def convert_amount(amount_str):   
+    amount_str = amount_str.replace("$", "").replace(",", "")
+    amount_str = round(float(amount_str), 2)
+    return amount_str
+
+df['Authorized Amount'] = df['Authorized Amount'].apply(convert_amount)
+
+# Create graph and process data
+@st.cache_resource
+def create_network(df):
+    G = nx.Graph()
+    faculty_colleges = defaultdict(list)
+    faculty_amounts = defaultdict(float)
+
+    for _, row in df.iterrows():
+        # Process PI information
+        pi = str(row['PI Name']).strip()
+        if not pi or pi == 'nan':
+            continue
+            
+        college = str(row['College']).strip()
+        amount = row['Authorized Amount']
+        
+        # Process Co-PI information
+        co_pi_names = row['Co PI Name']
+        if pd.isna(co_pi_names):
+            co_pis = []
+        else:
+            co_pis = [name.strip() for name in str(co_pi_names).split('|') if name.strip() not in ['', 'nan']]
+
+        # Add PI node and attributes
+        G.add_node(pi)
+        faculty_colleges[pi].append(college)
+        faculty_amounts[pi] += amount
+
+        # Add Co-PI nodes and edges
+        for co_pi in co_pis:
+            if co_pi and co_pi != pi:  # Prevent self-loops
+                G.add_node(co_pi)
+                faculty_colleges[co_pi].append(college)
+                faculty_amounts[co_pi] += amount
+                G.add_edge(pi, co_pi)
+
+    # Determine dominant college for each faculty member
+    college_map = {}
+    for faculty, colleges in faculty_colleges.items():
+        college_counts = defaultdict(int)
+        for c in colleges:
+            if c and c != 'nan':
+                college_counts[c] += 1
+        if college_counts:
+            college_map[faculty] = max(college_counts, key=college_counts.get)
+        else:
+            college_map[faculty] = 'Unknown'
+
+    # Create color mapping
+    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)}
+
+    # Calculate node sizes based on total funding
+    amounts = list(faculty_amounts.values())
+    if amounts:
+        min_amount = min(amounts)
+        max_amount = max(amounts)
+        size_range = (20, 40)  # Min and max node sizes
+        if max_amount == min_amount:
+            node_sizes = [size_range[0]] * len(amounts)
+        else:
+            node_sizes = [size_range[0] + (size_range[1] - size_range[0]) * 
+                         (amt - min_amount) / (max_amount - min_amount) 
+                         for amt in amounts]
+    else:
+        node_sizes = [size_range[0]] * len(faculty_amounts)
+
+    # Add attributes to nodes
+    for i, node in enumerate(G.nodes()):
+        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}")
+    
+    return G, college_colors
+
+# Create network
+G, college_colors = create_network(df)
+
+# Create pyvis network
+nt = Network(
+    height='800px',
+    width='100%',
+    bgcolor='#ffffff',
+    font_color='#333333',
+    notebook=True
+)
+nt.from_nx(G)
+nt.toggle_hide_edges_on_drag(True)
+nt.show_buttons(filter_=['physics', 'nodes'])
+
+# Save and show network
+nt.save_graph('network.html')
+with open('network.html', 'r', encoding='utf-8') as f:
+    html = f.read()
+
+# Add some explanation
+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
+- Click and drag background to pan
+- Hover over nodes to see details
+- Use the control panel (click the gear icon) to adjust physics settings
+""")
+
+# Show college color legend
+st.subheader("College Legend")
+cols = st.columns(4)
+for i, (college, color) in enumerate(college_colors.items()):
+    cols[i%4].markdown(f"<span style='color:{color}'>■</span> {college}", unsafe_allow_html=True)
+
+# Display network
+st.subheader("Collaboration Network")
+st.components.v1.html(html, height=800, scrolling=True)
+
+
+# Show raw data
+st.subheader("Award Data")
+st.dataframe(df, use_container_width=True)