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app.py

@@ -2,32 +2,29 @@ import pandas as pd
 import numpy as np
 import plotly.graph_objects as go
 import gradio as gr
-import os
+from io import StringIO
 
-# Debugging information
-print("Current working directory:", os.getcwd())
-print("Contents of current directory:", os.listdir())
+# 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
+'''
 
-# Try multiple possible locations for the CSV file
-possible_locations = [
-    'INSTITUTIONAL_INVESTORS_SCORE.csv',
-    '/home/user/app/INSTITUTIONAL_INVESTORS_SCORE.csv',
-    '/content/INSTITUTIONAL_INVESTORS_SCORE.csv',
-    os.path.join(os.getcwd(), 'INSTITUTIONAL_INVESTORS_SCORE.csv')
-]
-
-df = None
-for location in possible_locations:
-    try:
-        print(f"Trying to read from: {location}")
-        df = pd.read_csv(location)
-        print(f"Successfully read from: {location}")
-        break
-    except FileNotFoundError:
-        print(f"File not found at: {location}")
-
-if df is None:
-    raise FileNotFoundError("Could not find the CSV file in any of the expected locations.")
+# Load the data from the embedded CSV string
+df = pd.read_csv(StringIO(csv_data))
 
 # Clean the data
 df = df.dropna(subset=['final_score'])