Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# App Title and Description
+st.title("🏏 Cricket Player Dashboard")
+st.markdown("""
+### 📊 Analyze Player Performance
+- Select a **Team** and choose a **Player** to view their **Batting and Bowling Metrics**.
+""")
+
+# Load CSV directly from Hugging Face repository
+@st.cache_data
+def load_data():
+    return pd.read_csv("player_stats.csv")
+
+df = load_data()
+
+# Sidebar - Team and Player selection
+teams = df['Team'].unique()
+selected_team = st.sidebar.selectbox("Select Team", teams)
+
+# Filter players by selected team
+players = df[df['Team'] == selected_team]['Player_Name'].unique()
+selected_player = st.sidebar.selectbox("Select Player", players)
+
+# Filter the data by selected player
+player_data = df[(df['Team'] == selected_team) & (df['Player_Name'] == selected_player)]
+
+# Display Player Info
+st.title(f"📊 {selected_player} - {selected_team}")
+st.write(f"**Format:** {player_data['Format'].values[0]}")
+st.write(f"**Matches:** {player_data['Matches'].values[0]}")
+st.write(f"**Innings:** {player_data['Innings'].values[0]}")
+
+# Batting Performance Visualization
+st.subheader("🏏 Batting Metrics")
+
+fig, ax = plt.subplots(2, 2, figsize=(12, 10))
+
+# Runs vs Balls Faced
+ax[0, 0].bar(['Runs', 'Balls Faced'], [player_data['Runs'].values[0], player_data['Balls_Faced'].values[0]], color=['blue', 'orange'])
+ax[0, 0].set_title("Runs vs Balls Faced")
+
+# Batting Average and Strike Rate
+ax[0, 1].bar(['Average', 'Strike Rate'], [player_data['Avg'].values[0], player_data['SR'].values[0]], color=['green', 'purple'])
+ax[0, 1].set_title("Average and Strike Rate")
+
+# Fifties and Hundreds
+ax[1, 0].bar(['50s', '100s'], [player_data['50s'].values[0], player_data['100s'].values[0]], color=['cyan', 'magenta'])
+ax[1, 0].set_title("50s and 100s")
+
+# Bowling Performance Visualization
+st.subheader("🎯 Bowling Metrics")
+
+# Balls vs Wickets
+ax[1, 1].bar(['Balls', 'Wickets'], [player_data['Balls'].values[0], player_data['Wickets'].values[0]], color=['red', 'yellow'])
+ax[1, 1].set_title("Balls vs Wickets")
+
+plt.tight_layout()
+st.pyplot(fig)
+
+# Advanced Bowling Metrics
+fig, ax = plt.subplots(2, 2, figsize=(12, 10))
+
+# Economy Rate
+ax[0, 0].bar(['Economy Rate'], [player_data['Eco'].values[0]], color='purple')
+ax[0, 0].set_title("Economy Rate")
+
+# Best Bowling Figures in Innings (BBI) & Match (BBM)
+ax[0, 1].bar(['BBI', 'BBM'], [player_data['BBI'].values[0], player_data['BBM'].values[0]], color=['blue', 'orange'])
+ax[0, 1].set_title("Best Bowling Figures")
+
+# 4w and 5w hauls
+ax[1, 0].bar(['4w', '5w', '10w'], [player_data['4w'].values[0], player_data['5w'].values[0], player_data['10w'].values[0]], color=['green', 'red', 'yellow'])
+ax[1, 0].set_title("4W, 5W, and 10W Hauls")
+
+plt.tight_layout()
+st.pyplot(fig)
+
+# Display Summary Metrics
+st.write("### Player Summary Metrics")
+st.write(player_data[['Matches', 'Innings', 'Runs', 'Balls_Faced', 'Avg', 'SR', '50s', '100s',
+                      'Balls', 'Wickets', 'Eco', 'BBI', 'BBM', '4w', '5w', '10w']])