app.py

import pandas as pd
import numpy as np
import plotly.graph_objects as go
import gradio as gr
from io import StringIO
import os
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Force CPU-only mode
os.environ['CUDA_VISIBLE_DEVICES'] = ''

logger.info("Starting application setup")

# Embed the CSV data directly in the code
csv_data = '''cik,institution,final_score,long_term_performance_score,conviction_and_strategy_score,research_quality_and_adaptability_score,influence_and_governance_score,top_holdings_performance_score
0001843237,CEPHEI CAPITAL MANAGEMENT (HONG KONG) LTD,36,14,11,7,3,1
0001407024,NNS HOLDING,24,5,8,5,4,2
0001451928,SAC CAPITAL ADVISORS LP,50,20,10,12,6,2
0001905393,"PCG WEALTH ADVISORS, LLC",68,25,15,14,11,3
0001425362,"WHITE RIVER INVESTMENT PARTNERS, LLC",31,7,10,8,4,2
0001730210,"TRH FINANCIAL, LLC",72,28,16,15,10,3
0001165880,MONTPELIER RE HOLDINGS LTD,68,25,18,12,10,3
0001352776,REGIS MANAGEMENT CO LLC,73,28,17,14,11,3
0001582272,"BATTERY GLOBAL ADVISORS, LLC",65,22,16,14,10,3
0001466715,TRISHIELD CAPITAL MANAGEMENT LLC,59,18,,,,
0001092688,IBBOTSON ASSOCIATES INC,69,28,16,12,10,3
0001054880,HUTCHENS INVESTMENT MANAGEMENT INC,69,25,,,,3
0001650781,AXAR CAPITAL MANAGEMENT L.P.,51,16,14,12,6,3
0001425160,MERCHANTS' GATE CAPITAL LP,55,23,12,10,8,2
0001921487,"BEACON CAPITAL MANAGEMENT, LLC",78,25,22,15,12,4
'''

# Load the data from the embedded CSV string
df = pd.read_csv(StringIO(csv_data))

logger.info(f"Data loaded. Shape: {df.shape}")

# Clean the data
df = df.dropna(subset=['final_score'])
df = df.assign(institution=df['institution'].str.strip())

logger.info(f"Data cleaned. Shape after cleaning: {df.shape}")

def create_radar_chart(institution):
    logger.info(f"Creating radar chart for {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):
    logger.info(f"Creating bar chart for {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):
    logger.info(f"Updating dashboard for {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)

logger.info("Creating Gradio interface")

# 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."
)

logger.info("Launching Gradio interface")

# For Hugging Face Spaces deployment
iface.launch(
    share=False,  # Disable sharing as it's not needed in Spaces
    debug=True    # Enable debug mode for more detailed error messages
)

logger.info("Application setup complete")