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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')
# Fuzzy match player name
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 comparison
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
])
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 metric 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)
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
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)
col4.metric("Economy (ODI)", round(player_data.get("Economy_ODI", 0) or 0, 2))
col5.metric("Bowling Avg (Test)", round(player_data.get("Bowling_Average_Test", 0) or 0, 2))
# 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)
# Display comparison 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)
# Stats 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()
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