pages/1player_information.py

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt

# Set page configuration
st.set_page_config(page_title="Career Insights", layout="wide")

# Load data
df = pd.read_csv("Team_Info.csv")

# Get unique player names
player_names = df["Player"].unique()

# Search box for filtering player names
search_query = st.text_input("Search Player Name:")
filtered_players = [name for name in player_names if search_query.lower() in name.lower()] if search_query else player_names

# Player selection dropdown
selected_player = st.selectbox("Select Player", filtered_players)

# Buttons for Batting and Bowling
show_batting = st.button("Show Batting Stats")
show_bowling = st.button("Show Bowling Stats")

if selected_player:
    player_data = df[df["Player"] == selected_player].iloc[0]
    labels = ["Test", "ODI", "T20", "IPL"]
    
    if show_batting:
        st.subheader(f"Batting Stats for {selected_player}")
        
        col1, col2 = st.columns(2)
        with col1:
            # Pie Chart - Matches Played
            matches = [
                player_data.get("Matches_Test", 0),
                player_data.get("Matches_ODI", 0),
                player_data.get("Matches_T20", 0),
                player_data.get("Matches_IPL", 0)
            ]
            fig, ax = plt.subplots()
            ax.pie(matches, labels=labels, autopct="%1.1f%%", startangle=90)
            ax.set_title(f"Matches Played by {selected_player}")
            st.pyplot(fig)
        
        with col2:
            # Bar Chart - Runs Scored
            batting_runs = [
                player_data.get("batting_Runs_Test", 0),
                player_data.get("batting_Runs_ODI", 0),
                player_data.get("batting_Runs_T20", 0),
                player_data.get("batting_Runs_IPL", 0)
            ]
            fig, ax = plt.subplots()
            ax.bar(labels, batting_runs, color=["gold", "green", "blue", "red"])
            ax.set_ylabel("Runs Scored")
            ax.set_title(f"Runs Scored by {selected_player}")
            st.pyplot(fig)

    if show_bowling:
        st.subheader(f"Bowling Stats for {selected_player}")

        col1, col2 = st.columns(2)
        with col1:
            # Pie Chart - Wickets Taken
            wickets = [
                0 if pd.isna(player_data.get("bowling_Test_Avg", 0)) else float(player_data.get("bowling_Test_Avg", 0)),
                0 if pd.isna(player_data.get("bowling_ODI_Wickets", 0)) else int(player_data.get("bowling_ODI_Wickets", 0)),
                0 if pd.isna(player_data.get("bowling_T20_Wickets", 0)) else int(player_data.get("bowling_T20_Wickets", 0)),
                0 if pd.isna(player_data.get("bowling_IPL_Wickets", 0)) else int(player_data.get("bowling_IPL_Wickets", 0))
            ]
            fig, ax = plt.subplots()
            ax.pie(wickets, labels=labels, autopct="%1.1f%%", startangle=90)
            ax.set_title(f"Wickets Taken by {selected_player}")
            st.pyplot(fig)

        with col2:
            # Bar Chart - Maidens Bowled
            maidens_bowled = [
                player_data.get("bowling_Maidens_Test", 0),
                player_data.get("bowling_Maidens_ODI", 0),
                player_data.get("bowling_Maidens_T20", 0),
                player_data.get("bowling_Maidens_IPL", 0)
            ]
            fig, ax = plt.subplots(figsize=(5, 3))
            ax.bar(labels, maidens_bowled, color=["cyan", "magenta", "yellow", "black"])
            ax.set_ylabel("Maidens Bowled")
            ax.set_title(f"Maidens Bowled by {selected_player}")
            st.pyplot(fig)