Update pages/2player_comparison.py

pages/2player_comparison.py

@@ -17,9 +17,9 @@ def load_model_and_encoder():
 # Load player dataset
 @st.cache_data
 def load_data():
-    df = pd.read_csv('Reduced_final_teams.csv')
-    return df
+    return pd.read_csv('Reduced_final_teams.csv')
 
+# Fuzzy match player name
 def get_matching_player(name_from_file, player_list):
     name_lower = name_from_file.lower()
     for player in player_list:
@@ -27,12 +27,13 @@ def get_matching_player(name_from_file, player_list):
             return player
     return None
 
+# Horizontal bar chart
 def plot_horizontal_bar(df, player1, player2):
     st.subheader("📊 Stat Comparison - Horizontal Bar Chart")
     num_cols = df.select_dtypes(include='number').columns
     df_num = df[num_cols].T
     df_num.columns = [player1, player2]
-    df_num = df_num.sort_values(by=player1, ascending=False).head(15)  # top 15 metrics
+    df_num = df_num.sort_values(by=player1, ascending=False).head(15)
 
     fig, ax = plt.subplots(figsize=(10, 7))
     df_num.plot(kind='barh', ax=ax)
@@ -42,31 +43,42 @@ def plot_horizontal_bar(df, player1, player2):
     ax.legend(loc="lower right")
     st.pyplot(fig)
 
-def plot_radar_chart(player1_data, player2_data, player1, player2):
-    st.subheader("🕸 Radar Chart Comparison")
-    categories = ['Matches_ODI', 'Runs_ODI', 'Batting_Average_ODI', 'Strike_Rate_ODI', 'Wickets_ODI', 'Economy_ODI']
-    categories = [cat for cat in categories if cat in player1_data.index]
-
-    values1 = [player1_data[cat] for cat in categories]
-    values2 = [player2_data[cat] for cat in categories]
-
-    angles = np.linspace(0, 2 * np.pi, len(categories), endpoint=False).tolist()
-    values1 += values1[:1]
-    values2 += values2[:1]
-    angles += angles[:1]
-
-    fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))
-    ax.plot(angles, values1, label=player1, linewidth=2)
-    ax.fill(angles, values1, alpha=0.25)
-
-    ax.plot(angles, values2, label=player2, linewidth=2)
-    ax.fill(angles, values2, alpha=0.25)
+# Pie chart comparison
+def plot_pie_charts(player1_data, player2_data, player1, player2):
+    st.subheader("🥧 Batting vs Bowling Contribution")
+    col1, col2 = st.columns(2)
 
-    ax.set_thetagrids(np.degrees(angles[:-1]), categories)
-    ax.set_title("Overall Performance Comparison")
-    ax.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1))
+    for col, player_data, player_name in zip([col1, col2], [player1_data, player2_data], [player1, player2]):
+        batting_total = player_data.get('Runs_ODI', 0) + player_data.get('Runs_T20', 0) + player_data.get('Runs_Test', 0)
+        bowling_total = player_data.get('Wickets_ODI', 0) + player_data.get('Wickets_T20', 0) + player_data.get('Wickets_Test', 0)
+
+        labels = ['Batting', 'Bowling']
+        sizes = [batting_total, bowling_total]
+
+        fig, ax = plt.subplots()
+        ax.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90, colors=['#4CAF50', '#2196F3'])
+        ax.axis('equal')
+        col.pyplot(fig)
+        col.caption(f"{player_name}'s Batting vs Bowling")
+
+# Bowling metric bar chart
+def plot_bowling_comparison(df, player1, player2):
+    st.subheader("🎯 Bowling Metrics Comparison")
+
+    bowling_cols = [col for col in df.columns if 'Wickets' in col or 'Economy' in col or 'Bowling_Average' in col]
+    df_bowling = df[bowling_cols].T
+    df_bowling.columns = [player1, player2]
+    df_bowling = df_bowling.dropna().sort_values(by=player1, ascending=False)
+
+    fig, ax = plt.subplots(figsize=(10, 6))
+    df_bowling.plot(kind='bar', ax=ax)
+    ax.set_title("Bowling Stats")
+    ax.set_ylabel("Value")
+    ax.set_xticklabels(df_bowling.index, rotation=45, ha='right')
+    ax.legend(loc="upper right")
     st.pyplot(fig)
 
+# Main app
 def main():
     st.set_page_config(layout="wide")
     st.title("Cricket Player Comparison Tool 🏏")
@@ -101,21 +113,21 @@ def main():
             with col4:
                 st.image(img2, caption=player2_name, use_container_width=True)
 
-            # Comparison DataFrame
+            # Display comparison table
             comparison_df = pd.DataFrame([player1_data, player2_data])
             comparison_df.set_index('Player', inplace=True)
-
-            st.subheader("📋 Detailed Stats")
+            st.subheader("📋 Full Stats Table")
             st.dataframe(comparison_df.T)
 
-            # Plot visualizations
+            # Visualizations
             plot_horizontal_bar(comparison_df, player1_name, player2_name)
-            plot_radar_chart(player1_data, player2_data, player1_name, player2_name)
+            plot_pie_charts(player1_data, player2_data, player1_name, player2_name)
+            plot_bowling_comparison(comparison_df, player1_name, player2_name)
 
         else:
-            st.error("Player names from image files don't match or are the same. Please check file names.")
+            st.error("❌ Player names from image files don't match or are the same. Please check file names.")
     else:
-        st.info("Please upload two player images to continue.")
+        st.info("📸 Please upload two player images to continue.")
 
 if __name__ == "__main__":
     main()