Update pages/2player_comparison.py

pages/2player_comparison.py

@@ -19,7 +19,7 @@ def load_model_and_encoder():
 def load_data():
     return pd.read_csv('Reduced_final_teams.csv')
 
-# Fuzzy match player name
+# Match player name exactly (case-insensitive)
 def get_matching_player(name_from_file, player_list):
     name_lower = name_from_file.lower()
     for player in player_list:
@@ -44,7 +44,7 @@ def plot_horizontal_bar(df, player1, player2):
     ax.legend(loc="lower right")
     st.pyplot(fig)
 
-# Pie chart comparison
+# Pie chart with NaN-safe checks
 def plot_pie_charts(player1_data, player2_data, player1, player2):
     st.subheader("🥧 Batting vs Bowling Contribution")
     col1, col2 = st.columns(2)
@@ -55,12 +55,16 @@ def plot_pie_charts(player1_data, player2_data, player1, player2):
             player_data.get('Runs_T20', 0) or 0,
             player_data.get('Runs_Test', 0) or 0
         ])
-
         bowling_total = sum([
             player_data.get('Wickets_ODI', 0) or 0,
             player_data.get('Wickets_T20', 0) or 0,
             player_data.get('Wickets_Test', 0) or 0
         ])
+        total = batting_total + bowling_total
+
+        if total == 0 or np.isnan(total):
+            col.warning(f"⚠️ Not enough data to plot pie chart for {player_name}")
+            continue
 
         labels = ['Batting', 'Bowling']
         sizes = [batting_total, bowling_total]
@@ -71,7 +75,7 @@ def plot_pie_charts(player1_data, player2_data, player1, player2):
         col.pyplot(fig)
         col.caption(f"{player_name}'s Batting vs Bowling")
 
-# Bowling metric bar chart
+# Bowling metrics bar chart
 def plot_bowling_comparison(df, player1, player2):
     st.subheader("🎯 Bowling Metrics Comparison")
 
@@ -80,6 +84,10 @@ def plot_bowling_comparison(df, player1, player2):
     df_bowling.columns = [player1, player2]
     df_bowling = df_bowling.fillna(0).sort_values(by=player1, ascending=False)
 
+    if df_bowling.empty:
+        st.warning("⚠️ No bowling metrics available for comparison.")
+        return
+
     fig, ax = plt.subplots(figsize=(10, 6))
     df_bowling.plot(kind='bar', ax=ax)
     ax.set_title("Bowling Stats")
@@ -88,7 +96,7 @@ def plot_bowling_comparison(df, player1, player2):
     ax.legend(loc="upper right")
     st.pyplot(fig)
 
-# Bowling stats summary
+# Bowling stats summary per player
 def show_bowling_summary(player_data, player_name):
     st.subheader(f"🎳 Bowling Summary: {player_name}")
     col1, col2, col3 = st.columns(3)
@@ -97,8 +105,10 @@ def show_bowling_summary(player_data, player_name):
     col3.metric("Wickets (Test)", int(player_data.get("Wickets_Test", 0) or 0))
 
     col4, col5 = st.columns(2)
-    col4.metric("Economy (ODI)", round(player_data.get("Economy_ODI", 0) or 0, 2))
-    col5.metric("Bowling Avg (Test)", round(player_data.get("Bowling_Average_Test", 0) or 0, 2))
+    economy = player_data.get("Economy_ODI", 0)
+    bowling_avg = player_data.get("Bowling_Average_Test", 0)
+    col4.metric("Economy (ODI)", f"{economy:.2f}" if pd.notna(economy) else "N/A")
+    col5.metric("Avg (Test)", f"{bowling_avg:.2f}" if pd.notna(bowling_avg) else "N/A")
 
 # Main app
 def main():
@@ -135,13 +145,13 @@ def main():
             with col4:
                 st.image(img2, caption=player2_name, use_container_width=True)
 
-            # Display comparison table
+            # Full Stats Table
             comparison_df = pd.DataFrame([player1_data, player2_data])
             comparison_df.set_index('Player', inplace=True)
             st.subheader("📋 Full Stats Table")
             st.dataframe(comparison_df.T)
 
-            # Stats summaries
+            # Stat Summaries
             show_bowling_summary(player1_data, player1_name)
             show_bowling_summary(player2_data, player2_name)