Update app.py

app.py

@@ -2,7 +2,7 @@ import streamlit as st
 import yfinance as yf
 import pandas as pd
 
-@st.cache_resource
+@st.experimental_singleton
 def get_sp500_list():
     table = pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')
     return table[0]['Symbol'].tolist()
@@ -14,13 +14,15 @@ def fetch_stock_data(ticker_symbol):
     ticker = yf.Ticker(ticker_symbol)
     info = ticker.info
     financials = {
-        'P/E Ratio': info.get('forwardPE', None),
-        'P/B Ratio': info.get('priceToBook', None),
-        'P/S Ratio': info.get('priceToSalesTrailing12Months', None),
-        'Debt to Equity Ratio': info.get('debtToEquity', None),
-        'Return on Equity': info.get('returnOnEquity', None),
-        'Book-to-Market Ratio': 1 / info.get('priceToBook', float('nan')) if info.get('priceToBook') else None
+        'P/E Ratio': info.get('forwardPE'),
+        'P/B Ratio': info.get('priceToBook'),
+        'P/S Ratio': info.get('priceToSalesTrailing12Months'),
+        'Debt to Equity Ratio': info.get('debtToEquity'),
+        'Return on Equity': info.get('returnOnEquity'),
+        'Book-to-Market Ratio': 1 / info.get('priceToBook') if info.get('priceToBook') else None
     }
+    # Debug: Print to see if financials are fetched correctly
+    print(f"Financials for {ticker_symbol}: {financials}")
     return financials, info
 
 def compare_to_index(stock_ratios, index_averages):
@@ -63,34 +65,47 @@ sp500_list = get_sp500_list()
 sp500_averages = load_sp500_averages('sp500_averages.csv')
 
 scores_df = calculate_combined_scores_for_stocks(sp500_list, sp500_averages)
+
+# Debug: Print the DataFrame before filtering to see its content
+print("Scores DataFrame before filtering:", scores_df.head())
+
 required_columns = ['P/E Ratio', 'P/B Ratio', 'P/S Ratio', 'Debt to Equity Ratio', 'Return on Equity', 'Book-to-Market Ratio']
 
-scores_df_filtered = filter_incomplete_stocks(scores_df, required_columns)
+# Attempt a lenient filtering approach or skip filtering to debug
+# scores_df_filtered = filter_incomplete_stocks(scores_df, required_columns)
+scores_df_filtered = scores_df  # Temporarily bypass filtering to debug
 scores_df_sorted = scores_df_filtered.sort_values(by='Combined Score', ascending=False)
 
+# Debug: Print the DataFrame after sorting to see if it's empty
+print("Scores DataFrame after sorting:", scores_df_sorted.head())
+
 col1, col2 = st.columns([3, 5])
 
 with col1:
     st.subheader("Stock Overview")
-    styled_scores_df = scores_df_sorted.style.applymap(color_combined_score, subset=['Combined Score'])
-    st.dataframe(styled_scores_df)
+    if not scores_df_sorted.empty:
+        styled_scores_df = scores_df_sorted.style.applymap(color_combined_score, subset=['Combined Score'])
+        st.dataframe(styled_scores_df)
+    else:
+        st.write("No data available after filtering.")
 
 with col2:
     st.subheader("Stock Details")
-    sorted_tickers = scores_df_sorted['Stock'].tolist()
-    ticker_symbol = st.selectbox('Select a stock for details', options=sorted_tickers)
-    if ticker_symbol:
-        with st.spinner(f'Fetching data for {ticker_symbol}...'):
-            stock_data, info = fetch_stock_data(ticker_symbol)
-            comparison, _ = compare_to_index(stock_data, sp500_averages)
-            
-            st.write(f"**{info.get('longName', 'N/A')}** ({ticker_symbol})")
-            st.write(info.get('longBusinessSummary', 'N/A'))
-            
-            for ratio in required_columns:
-                value = stock_data.get(ratio, 'N/A')
-                average = sp500_averages.loc[ratio, 'Average'] if ratio in sp500_averages.index else 'N/A'
-                status = comparison.get(ratio, 'N/A')
-                st.write(f"{ratio}: {value} (Your Ratio) | {average} (S&P 500 Avg) - {status}")
-
-
+    if not scores_df_sorted.empty:
+        sorted_tickers = scores_df_sorted['Stock'].tolist()
+        ticker_symbol = st.selectbox('Select a stock for details', options=sorted_tickers)
+        if ticker_symbol:
+            with st.spinner(f'Fetching data for {ticker_symbol}...'):
+                stock_data, info = fetch_stock_data(ticker_symbol)
+                comparison, _ = compare_to_index(stock_data, sp500_averages)
+                
+                st.write(f"**{info.get('longName', 'N/A')}** ({ticker_symbol})")
+                st.write(info.get('longBusinessSummary', 'N/A'))
+                
+                for ratio in required_columns:
+                    value = stock_data.get(ratio, 'N/A')
+                    average = sp500_averages.loc[ratio, 'Average'] if ratio in sp500_averages.index else 'N/A'
+                    status = comparison.get(ratio, 'N/A')
+                    st.write(f"{ratio}: {value} (Your Ratio) | {average} (S&P 500 Avg) - {status}")
+    else:
+        st.write("No stocks to display.")