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

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

st.title("South Africa Cricket Players - Career Visualizations")

# Enter Player Name
player_input = st.text_input("Enter Player Name:")

if player_input:
    selected_player = player_input.strip()
    if selected_player in df["Player"].values:
        player_data = df[df["Player"] == selected_player].iloc[0]

        # Pie Chart - Matches Played Across Formats
        matches = [
            player_data["Matches_Test"],
            player_data["Matches_ODI"],
            player_data["Matches_T20"],
            player_data["Matches_IPL"]
        ]
        labels = ["Test", "ODI", "T20", "IPL"]
        fig, ax = plt.subplots()
        ax.pie(matches, labels=labels, autopct="%1.1f%%", startangle=90, colors=["blue", "green", "red", "purple"])
        ax.set_title(f"Matches Played by {selected_player}")
        st.pyplot(fig)

        # Bar Chart - Runs Scored in Different Formats
        batting_runs = [
            player_data["batting_Runs_Test"],
            player_data["batting_Runs_ODI"],
            player_data["batting_Runs_T20"],
            player_data["batting_Runs_IPL"]
        ]
        fig, ax = plt.subplots()
        ax.bar(labels, batting_runs, color=["blue", "green", "red", "purple"])
        ax.set_ylabel("Runs Scored")
        ax.set_title(f"Runs Scored by {selected_player}")
        st.pyplot(fig)

        # Scatter Plot - Matches vs Runs
        fig, ax = plt.subplots()
        sns.scatterplot(x=df["Matches_ODI"], y=df["batting_Runs_ODI"], ax=ax)
        ax.set_xlabel("Matches Played")
        ax.set_ylabel("Runs Scored")
        ax.set_title("Matches vs Runs in ODIs (All Players)")
        st.pyplot(fig)

        # Line Chart - Batting Average Over Formats
        batting_average = [
            player_data["batting_Runs_Test"] / max(1, player_data["batting_Innings_Test"]),
            player_data["batting_Runs_ODI"] / max(1, player_data["batting_Innings_ODI"]),
            player_data["batting_Runs_T20"] / max(1, player_data["batting_Innings_T20"]),
            player_data["batting_Runs_IPL"] / max(1, player_data["batting_Innings_IPL"])
        ]
        fig, ax = plt.subplots()
        ax.plot(labels, batting_average, marker='o', linestyle='-', color='orange')
        ax.set_ylabel("Batting Average")
        ax.set_title(f"Batting Average of {selected_player}")
        st.pyplot(fig)

        # Histogram - Distribution of Runs Scored by All Players in ODIs
        fig, ax = plt.subplots()
        sns.histplot(df["batting_Runs_ODI"], bins=20, kde=True, color='green', ax=ax)
        ax.set_xlabel("Runs Scored")
        ax.set_ylabel("Frequency")
        ax.set_title("Distribution of Runs Scored in ODIs (All Players)")
        st.pyplot(fig)
    else:
        st.error("Player not found! Please enter a valid player name.")