import streamlit as st import pandas as pd import pickle import matplotlib.pyplot as plt import seaborn as sns import numpy as np # Load model and encoder @st.cache_resource def load_model_and_encoder(): with open('best_rf_pipeline.pkl', 'rb') as f: model = pickle.load(f) with open('label_encoder.pkl', 'rb') as f: encoder = pickle.load(f) return model, encoder # Load player dataset @st.cache_data def load_data(): return pd.read_csv('Reduced_final_teams.csv') # 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: if player.lower() == name_lower: 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.fillna(0) 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) ax.set_title(f"{player1} vs {player2} - Key Stats") ax.set_xlabel("Value") ax.set_ylabel("Metric") ax.legend(loc="lower right") st.pyplot(fig) # 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) for col, player_data, player_name in zip([col1, col2], [player1_data, player2_data], [player1, player2]): batting_total = sum([ player_data.get('Runs_ODI', 0) or 0, 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] 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 metrics 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.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") ax.set_ylabel("Value") ax.set_xticklabels(df_bowling.index, rotation=45, ha='right') ax.legend(loc="upper right") st.pyplot(fig) # 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) col1.metric("Wickets (ODI)", int(player_data.get("Wickets_ODI", 0) or 0)) col2.metric("Wickets (T20)", int(player_data.get("Wickets_T20", 0) or 0)) col3.metric("Wickets (Test)", int(player_data.get("Wickets_Test", 0) or 0)) col4, col5 = st.columns(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(): st.set_page_config(layout="wide") st.title("Cricket Player Comparison Tool 🏏") df = load_data() model, encoder = load_model_and_encoder() player_list = df['Player'].tolist() # Upload images col1, col2 = st.columns(2) with col1: img1 = st.file_uploader("Upload Image for Player 1", type=['png', 'jpg', 'jpeg'], key='img1') with col2: img2 = st.file_uploader("Upload Image for Player 2", type=['png', 'jpg', 'jpeg'], key='img2') if img1 and img2: name1_raw = img1.name.rsplit('.', 1)[0] name2_raw = img2.name.rsplit('.', 1)[0] player1_name = get_matching_player(name1_raw, player_list) player2_name = get_matching_player(name2_raw, player_list) if player1_name and player2_name and player1_name != player2_name: player1_data = df[df['Player'].str.lower() == player1_name.lower()].fillna(0).squeeze() player2_data = df[df['Player'].str.lower() == player2_name.lower()].fillna(0).squeeze() st.success(f"Comparing **{player1_name}** vs **{player2_name}**") col3, col4 = st.columns(2) with col3: st.image(img1, caption=player1_name, use_container_width=True) with col4: st.image(img2, caption=player2_name, use_container_width=True) # 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) # Stat Summaries show_bowling_summary(player1_data, player1_name) show_bowling_summary(player2_data, player2_name) # Visualizations plot_horizontal_bar(comparison_df, 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.") else: st.info("📸 Please upload two player images to continue.") if __name__ == "__main__": main()