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app.py

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+import pandas as pd
+import numpy as np
+import plotly.graph_objects as go
+import gradio as gr
+
+# Load the data
+df = pd.read_csv('/content/INSTITUTIONAL_INVESTORS_SCORE.csv')
+
+# Clean the data
+df = df.dropna(subset=['final_score'])
+df = df.assign(institution=df['institution'].str.strip())
+
+def create_radar_chart(institution):
+    # Get the data for the selected institution
+    inst_data = df[df['institution'] == institution].iloc[0]
+    
+    # Prepare the data for the radar chart
+    categories = ['Long-term Performance', 'Conviction & Strategy', 'Research Quality',
+                  'Influence & Governance', 'Top Holdings Performance']
+    values = [inst_data['long_term_performance_score'],
+              inst_data['conviction_and_strategy_score'],
+              inst_data['research_quality_and_adaptability_score'],
+              inst_data['influence_and_governance_score'],
+              inst_data['top_holdings_performance_score']]
+    
+    # Create the radar chart
+    fig = go.Figure()
+
+    fig.add_trace(go.Scatterpolar(
+        r=values,
+        theta=categories,
+        fill='toself',
+        name=institution
+    ))
+
+    fig.update_layout(
+        polar=dict(
+            radialaxis=dict(
+                visible=True,
+                range=[0, max(values)]
+            )),
+        showlegend=False,
+        title=f"{institution} Performance Metrics"
+    )
+    
+    return fig
+
+def create_bar_chart(institution):
+    # Get the data for the selected institution
+    inst_data = df[df['institution'] == institution].iloc[0]
+    
+    # Prepare the data for the bar chart
+    categories = ['Final Score', 'Long-term Performance', 'Conviction & Strategy', 'Research Quality',
+                  'Influence & Governance', 'Top Holdings Performance']
+    values = [inst_data['final_score'],
+              inst_data['long_term_performance_score'],
+              inst_data['conviction_and_strategy_score'],
+              inst_data['research_quality_and_adaptability_score'],
+              inst_data['influence_and_governance_score'],
+              inst_data['top_holdings_performance_score']]
+    
+    # Create the bar chart
+    fig = go.Figure(go.Bar(
+        x=categories,
+        y=values,
+        text=values,
+        textposition='auto',
+    ))
+
+    fig.update_layout(
+        title=f"{institution} Scores Comparison",
+        xaxis_title="Metrics",
+        yaxis_title="Score",
+        yaxis=dict(range=[0, 100])
+    )
+    
+    return fig
+
+def update_dashboard(institution):
+    radar_chart = create_radar_chart(institution)
+    bar_chart = create_bar_chart(institution)
+    
+    inst_data = df[df['institution'] == institution].iloc[0]
+    cik = inst_data['cik']
+    final_score = inst_data['final_score']
+    
+    return (f"CIK: {cik}", 
+            f"Final Score: {final_score:.2f}", 
+            radar_chart, 
+            bar_chart)
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=update_dashboard,
+    inputs=gr.Dropdown(choices=sorted(df['institution'].unique()), label="Select an Institution"),
+    outputs=[
+        gr.Textbox(label="CIK"),
+        gr.Textbox(label="Final Score"),
+        gr.Plot(label="Performance Metrics Radar Chart"),
+        gr.Plot(label="Scores Comparison Bar Chart")
+    ],
+    title="Institution Performance Dashboard",
+    description="Select an institution to view its performance metrics and scores."
+)
+
+# For Hugging Face Spaces deployment
+iface.launch()