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("

", unsafe_allow_html=True) st.image("Cricket.jpg", width=400) # Adjust width, height not directly supported st.markdown("

", 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)