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| import streamlit as st | |
| import pandas as pd | |
| import plotly.express as px | |
| import os | |
| # Streamlit App | |
| st.set_page_config(page_title="Player Analytics Dashboard", layout="wide") | |
| # Create a folder to save CSVs if not exists | |
| data_folder = "data" | |
| os.makedirs(data_folder, exist_ok=True) | |
| # File paths | |
| batting_path = os.path.join(data_folder, "batting.csv") | |
| bowling_path = os.path.join(data_folder, "bowling.csv") | |
| # Define file paths | |
| batting_path = "batting_data.csv" | |
| bowling_path = "bowling_data.csv" | |
| # Function to load data based on file type | |
| def load_data(file): | |
| if file.name.endswith(".csv"): | |
| return pd.read_csv(file) | |
| elif file.name.endswith((".xls", ".xlsx")): | |
| return pd.read_excel(file) | |
| elif file.name.endswith(".json"): | |
| return pd.read_json(file) | |
| else: | |
| st.error(f"Unsupported file format: {file.name}") | |
| return None | |
| # Upload only if files are not already saved | |
| if not (os.path.exists(batting_path) and os.path.exists(bowling_path)): | |
| uploaded_batting = st.sidebar.file_uploader("Import Batting Data File", type=["csv", "xlsx", "xls", "json"], key="batting") | |
| uploaded_bowling = st.sidebar.file_uploader("Import Bowling Data File", type=["csv", "xlsx", "xls", "json"], key="bowling") | |
| if uploaded_batting and uploaded_bowling: | |
| batting_df = load_data(uploaded_batting) | |
| bowling_df = load_data(uploaded_bowling) | |
| # Save only if both files are valid | |
| if batting_df is not None and bowling_df is not None: | |
| batting_df.to_csv(batting_path, index=False) | |
| bowling_df.to_csv(bowling_path, index=False) | |
| st.success("Files uploaded and saved successfully as CSV!") | |
| else: | |
| # Load saved CSVs | |
| batting_df = pd.read_csv(batting_path) | |
| bowling_df = pd.read_csv(bowling_path) | |
| # Display message when files are loaded successfully | |
| # if 'batting_df' in locals() and 'bowling_df' in locals(): | |
| # st.success("Batting and Bowling Data Loaded Successfully!") | |
| # **NEW: Display message if no data is available** | |
| if 'batting_df' not in locals() or 'bowling_df' not in locals(): | |
| st.title("Welcome to the Cricket Player 🏏 Analytics Dashboard!") | |
| st.write(" #### Please upload Batting and Bowling CSV files from the sidebar to start the analysis.") | |
| st.markdown("<p style='text-align: center;'>", unsafe_allow_html=True) | |
| st.image("Cricket.jpg", width=400) # Adjust width, height not directly supported | |
| st.markdown("</p>", unsafe_allow_html=True) | |
| st.stop() # Stop execution to prevent errors | |
| # Extract unique teams and players | |
| teams = sorted(set(batting_df['Country'].unique()).union(bowling_df['Country'].unique())) | |
| team = st.sidebar.selectbox("Select Team", teams) | |
| # Filter players by team | |
| batting_players = batting_df[batting_df['Country'] == team]['player_name'].unique() | |
| bowling_players = bowling_df[bowling_df['Country'] == team]['player_name'].unique() | |
| players = sorted(set(batting_players).union(bowling_players)) | |
| player = st.sidebar.selectbox("Select Player", players) | |
| # Filter data for the selected player | |
| player_batting = batting_df[(batting_df['player_name'] == player) & (batting_df['Country'] == team)] | |
| player_bowling = bowling_df[(bowling_df['player_name'] == player) & (bowling_df['Country'] == team)] | |
| st.title(f"{player}'s Performance Dashboard") | |
| # Display player data | |
| st.subheader("Player Records") | |
| if not player_batting.empty: | |
| st.write("### Batting Scorecard") | |
| st.dataframe(player_batting) | |
| if not player_bowling.empty: | |
| st.write("### Bowling Scorecard") | |
| st.dataframe(player_bowling) | |
| # Visualization | |
| # if not player_batting.empty: | |
| # st.subheader("Batting Figures") | |
| # col1, col2 = st.columns(2) | |
| # with col1: | |
| # # Runs by Format - Using Horizontal Bar Chart with High-Contrast Colors | |
| # fig_bat_runs = px.bar(player_batting, x='Runs', y='Format', orientation='h', | |
| # title='Runs Breakdown Across Format', text='Runs', | |
| # color='Runs', color_continuous_scale='magma') | |
| # fig_bat_runs.update_traces(textposition='outside') | |
| # st.plotly_chart(fig_bat_runs, use_container_width=True) | |
| # with col2: | |
| # # Strike Rate by Format - Using Cyan Shade for Visibility | |
| # fig_bat_sr = px.area(player_batting, x='Format', y='SR', title='Strike Rate Across Formats', | |
| # color_discrete_sequence=['#00FFFF']) # Neon Cyan | |
| # st.plotly_chart(fig_bat_sr, use_container_width=True) | |
| # if not player_bowling.empty: | |
| # st.subheader("Bowling Figures") | |
| # col3, col4 = st.columns(2) | |
| # with col3: | |
| # # Economy Rate by Format - Lollipop Chart with Vibrant Colors | |
| # fig_bowl_eco = px.scatter(player_bowling, x='Format', y='Eco', size=[10]*len(player_bowling), | |
| # title='Economy Rate Across Formats', color='Eco', | |
| # color_continuous_scale='viridis') # High-contrast green-blue | |
| # fig_bowl_eco.add_trace(px.line(player_bowling, x='Format', y='Eco').data[0]) | |
| # st.plotly_chart(fig_bowl_eco, use_container_width=True) | |
| # with col4: | |
| # # Wickets Distribution - Using High-Contrast Pie Chart Colors | |
| # fig_bowl_wickets = px.pie(player_bowling, names='Format', values='Wickets', | |
| # title='Wickets Breakdown Across Format', | |
| # color_discrete_sequence=px.colors.qualitative.Bold) # High contrast for dark mode | |
| # st.plotly_chart(fig_bowl_wickets, use_container_width=True) | |
| # st.title("Cricket Player Stats") | |
| # option = st.radio("Select Statistics:", ['Batting', 'Bowling'], horizontal=True) | |
| # if option == 'Batting' and not player_batting.empty: | |
| # st.subheader("Batting Figures") | |
| # col1, col2 = st.columns(2) | |
| # with col1: | |
| # fig_bat_runs = px.bar(player_batting, x='Runs', y='Format', orientation='h', | |
| # title='Runs Breakdown Across Formats', text='Runs', | |
| # color='Runs', color_continuous_scale='magma') | |
| # fig_bat_runs.update_traces(textposition='outside') | |
| # st.plotly_chart(fig_bat_runs, use_container_width=True) | |
| # with col2: | |
| # fig_bat_sr = px.area(player_batting, x='Format', y='SR', title='Strike Rate Across Formats', | |
| # color_discrete_sequence=['#00FFFF']) # Neon Cyan | |
| # st.plotly_chart(fig_bat_sr, use_container_width=True) | |
| # elif option == 'Bowling' and not player_bowling.empty: | |
| # st.subheader("Bowling Figures") | |
| # col3, col4 = st.columns(2) | |
| # with col3: | |
| # fig_bowl_eco = px.scatter(player_bowling, x='Format', y='Eco', size=[10]*len(player_bowling), | |
| # title='Economy Rate Across Formats', color='Eco', | |
| # color_continuous_scale='viridis') # High-contrast green-blue | |
| # fig_bowl_eco.add_trace(px.line(player_bowling, x='Format', y='Eco').data[0]) | |
| # st.plotly_chart(fig_bowl_eco, use_container_width=True) | |
| # with col4: | |
| # fig_bowl_wickets = px.pie(player_bowling, names='Format', values='Wickets', | |
| # title='Wickets Breakdown Across Formats', | |
| # color_discrete_sequence=px.colors.qualitative.Bold) # High contrast for dark mode | |
| # st.plotly_chart(fig_bowl_wickets, use_container_width=True) | |
| # Create card-style buttons for Batting and Bowling | |
| col_a, col_b = st.columns(2) | |
| with col_a: | |
| batting_click = st.button("🏏 Batting Statistics", use_container_width=True) | |
| with col_b: | |
| bowling_click = st.button("🎯 Bowling Statistics", use_container_width=True) | |
| # Flags for selection | |
| show_batting = show_bowling = False | |
| if batting_click: | |
| show_batting = True | |
| elif bowling_click: | |
| show_bowling = True | |
| # ---- BATTING VISUALS ---- | |
| if show_batting and not player_batting.empty: | |
| st.subheader("Batting Figures") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| fig_bat_runs = px.bar(player_batting, x='Runs', y='Format', orientation='h', | |
| title='Runs Breakdown Across Formats', text='Runs', | |
| color='Runs', color_continuous_scale='magma') | |
| fig_bat_runs.update_traces(textposition='outside') | |
| st.plotly_chart(fig_bat_runs, use_container_width=True) | |
| with col2: | |
| fig_bat_sr = px.area(player_batting, x='Format', y='SR', title='Strike Rate Across Formats', | |
| color_discrete_sequence=['#00FFFF']) # Neon Cyan | |
| st.plotly_chart(fig_bat_sr, use_container_width=True) | |
| # ---- BOWLING VISUALS ---- | |
| elif show_bowling and not player_bowling.empty: | |
| st.subheader("Bowling Figures") | |
| col3, col4 = st.columns(2) | |
| with col3: | |
| fig_bowl_eco = px.scatter(player_bowling, x='Format', y='Eco', size=[10]*len(player_bowling), | |
| title='Economy Rate Across Formats', color='Eco', | |
| color_continuous_scale='viridis') # High-contrast green-blue | |
| fig_bowl_eco.add_trace(px.line(player_bowling, x='Format', y='Eco').data[0]) | |
| st.plotly_chart(fig_bowl_eco, use_container_width=True) | |
| with col4: | |
| fig_bowl_wickets = px.pie(player_bowling, names='Format', values='Wickets', | |
| title='Wickets Breakdown Across Formats', | |
| color_discrete_sequence=px.colors.qualitative.Bold) # High contrast for dark mode | |
| st.plotly_chart(fig_bowl_wickets, use_container_width=True) | |