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

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

# Light-Themed Background
page_bg = """
<style>
    body {
        background-color: #f5f7fa;
    }
    [data-testid="stAppViewContainer"] {
        background-color: #f5f7fa;
    }
    [data-testid="stSidebar"] {
        background-color: #ffffff;
        border-right: 1px solid #ddd;
    }
    h1, h2, h3, h4, h5, h6 {
        color: #333333 !important;
    }
    .stTextInput>div>div>input {
        background-color: white;
        color: black;
        border-radius: 5px;
        padding: 10px;
        border: 1px solid #ccc;
    }
</style>
"""
st.markdown(page_bg, unsafe_allow_html=True)

# App Title
st.title("🏏 Cricket Legends: Career Insights & Visualizations")

# Load data
file_path = "Final.csv"  # Ensure this file exists in your working directory
df = pd.read_csv(file_path)

# 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=["#87CEEB", "#90EE90", "#FFA07A", "#9370DB"])
        ax.set_title(f"Matches Played by {selected_player}", fontsize=14)
        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=["#FFD700", "#008000", "#1E90FF", "#FF4500"])
        ax.set_ylabel("Runs Scored", fontsize=12)
        ax.set_title(f"Runs Scored by {selected_player}", fontsize=14)
        st.pyplot(fig)

        # Scatter Plot - Matches vs Runs (ODIs)
        fig, ax = plt.subplots()
        sns.scatterplot(x=df["Matches_ODI"], y=df["batting_Runs_ODI"], ax=ax, color="#4682B4", edgecolor='black')
        ax.set_xlabel("Matches Played", fontsize=12)
        ax.set_ylabel("Runs Scored", fontsize=12)
        ax.set_title("Matches vs Runs in ODIs (All Players)", fontsize=14)
        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='#FFA500', linewidth=2)
        ax.set_ylabel("Batting Average", fontsize=12)
        ax.set_title(f"Batting Average of {selected_player}", fontsize=14)
        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='#3CB371', ax=ax)
        ax.set_xlabel("Runs Scored", fontsize=12)
        ax.set_ylabel("Frequency", fontsize=12)
        ax.set_title("Distribution of Runs Scored in ODIs (All Players)", fontsize=14)
        st.pyplot(fig)

    else:
        st.error("🚨 Player not found! Please enter a valid player name.")