import streamlit as st
import pandas as pd
import numpy as np
import cv2
import joblib
from PIL import Image
import plotly.express as px

# Load models and data
@st.cache_resource
def load_model():
    return joblib.load("svc_face_classifier (1).pkl")

@st.cache_resource
def load_label_encoder():
    return joblib.load("label_encoder.pkl")

@st.cache_data
def load_data():
    return pd.read_csv("Final_Data.csv")

# Haar Cascade
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")

# Detect + Predict
def detect_and_predict_face(image_file, model, label_encoder):
    image = Image.open(image_file).convert("RGB")
    gray = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)

    if len(faces) == 0:
        return None, "⚠️ No face detected!"

    x, y, w, h = faces[0]
    face = gray[y:y+h, x:x+w]
    resized = cv2.resize(face, (64, 64))
    flattened = resized.flatten().reshape(1, -1)

    pred_label = model.predict(flattened)[0]
    pred_name = label_encoder.inverse_transform([pred_label])[0]
    return pred_name, None

# Player stat functions
def get_player_details(df, name):
    return df[df['Player'] == name].iloc[0]

def plot_matches_pie(player_data):
    matches_stats = {
        'Format': ['Test', 'ODI', 'T20', 'IPL'],
        'Matches': [player_data['Matches_Test'], player_data['Matches_ODI'], player_data['Matches_T20'], player_data['Matches_IPL']]
    }
    fig = px.pie(pd.DataFrame(matches_stats), names='Format', values='Matches',
                 title=f"🏏 {player_data['Player']} - Matches Played 🎯")
    st.plotly_chart(fig)

def plot_batting_stats(player_data):
    stats = {
        'Format': ['Test', 'ODI', 'T20', 'IPL'],
        'Runs': [player_data['batting_Runs_Test'], player_data['batting_Runs_ODI'], player_data['batting_Runs_T20'], player_data['batting_Runs_IPL']],
        'Average': [player_data['batting_Average_Test'], player_data['batting_Average_ODI'], player_data['batting_Average_T20'], player_data['batting_Average_IPL']],
        'Sixes': [player_data['batting_Sixes_Test'], player_data['batting_Sixes_ODI'], player_data['batting_Sixes_T20'], player_data['batting_Sixes_IPL']],
        'Fours': [player_data['batting_Fours_Test'], player_data['batting_Fours_ODI'], player_data['batting_Fours_T20'], player_data['batting_Fours_IPL']],
        'Strike Rate': [player_data['batting_SR_Test'], player_data['batting_SR_ODI'], player_data['batting_SR_T20'], player_data['batting_SR_IPL']]
    }
    df = pd.DataFrame(stats)
    st.plotly_chart(px.bar(df, x='Format', y='Runs', title=f"🏃‍♂️ {player_data['Player']} - Runs Scored 💯", text='Runs'))
    st.plotly_chart(px.bar(df, x='Format', y='Average', title=f"📊 {player_data['Player']} - Batting Average ⭐", text='Average'))
    st.plotly_chart(px.bar(df, x='Format', y='Sixes', title=f"🔥 {player_data['Player']} - Sixes 🚀", text='Sixes'))
    st.plotly_chart(px.bar(df, x='Format', y='Fours', title=f"🎯 {player_data['Player']} - Fours ⚡", text='Fours'))
    st.plotly_chart(px.line(df, x='Format', y='Strike Rate', title=f"⚡ {player_data['Player']} - Strike Rate 🎯", markers=True, text='Strike Rate'))

def plot_bowling_stats(player_data):
    stats = {
        'Format': ['Test', 'ODI', 'T20', 'IPL'],
        'Wickets': [player_data['bowling_Test_Wickets'], player_data['bowling_ODI_Wickets'], player_data['bowling_T20_Wickets'], player_data['bowling_IPL_Wickets']],
        'Average': [player_data['bowling_Test_Avg'], player_data['bowling_ODI_Avg'], player_data['bowling_T20_Avg'], player_data['bowling_IPL_Avg']],
        'Economy': [player_data['bowling_Test_Eco'], player_data['bowling_ODI_Eco'], player_data['bowling_T20_Eco'], player_data['bowling_IPL_Eco']],
        'Strike Rate': [player_data['bowling_Test_SR'], player_data['bowling_ODI_SR'], player_data['bowling_T20_SR'], player_data['bowling_IPL_SR']]
    }
    df = pd.DataFrame(stats)
    st.plotly_chart(px.bar(df, x='Format', y='Wickets', title=f"🎳 {player_data['Player']} - Wickets Taken 🏆", text='Wickets'))
    st.plotly_chart(px.bar(df, x='Format', y='Average', title=f"📈 {player_data['Player']} - Bowling Average 🌟", text='Average'))
    st.plotly_chart(px.line(df, x='Format', y='Economy', title=f"💰 {player_data['Player']} - Economy Rate 📉", markers=True, text='Economy'))
    st.plotly_chart(px.line(df, x='Format', y='Strike Rate', title=f"⚡ {player_data['Player']} - Bowling Strike Rate 🎯", markers=True, text='Strike Rate'))

# Main Streamlit App
def main():
    st.title("📸 Upload Two Player Images to Compare Stats")

    model = load_model()
    label_encoder = load_label_encoder()
    df = load_data()

    col1, col2 = st.columns(2)
    img1 = col1.file_uploader("Upload Image for Player 1", type=["jpg", "jpeg", "png"], key="img1")
    img2 = col2.file_uploader("Upload Image for Player 2", type=["jpg", "jpeg", "png"], key="img2")

    if img1 and img2:
        with st.spinner("Detecting and predicting players..."):
            pred1, err1 = detect_and_predict_face(img1, model, label_encoder)
            pred2, err2 = detect_and_predict_face(img2, model, label_encoder)

        if err1: st.error(f"Player 1: {err1}")
        if err2: st.error(f"Player 2: {err2}")

        if not err1 and not err2:
            st.success(f"✅ Predicted Players: {pred1} vs {pred2}")
            try:
                p1 = get_player_details(df, pred1)
                p2 = get_player_details(df, pred2)

                col1, col2 = st.columns(2)

                with col1:
                    st.header(f"📌 {pred1}")
                    plot_matches_pie(p1)
                    plot_batting_stats(p1)
                    plot_bowling_stats(p1)

                with col2:
                    st.header(f"📌 {pred2}")
                    plot_matches_pie(p2)
                    plot_batting_stats(p2)
                    plot_bowling_stats(p2)

            except Exception as e:
                st.error(f"⚠️ Player stats not found: {e}")
 
if __name__ == "__main__":
    main()