Update app.py

app.py

@@ -1,94 +1,71 @@
 import streamlit as st
 import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-# Set Streamlit page configuration
-st.set_page_config(page_title="CrickMetrics", page_icon="🏏", layout="wide")
-
-# Title and description
-st.title("🏏 CrickMetrics: Player Performance Dashboard")
-st.markdown("Upload batting and bowling CSVs to explore player metrics with interactive visualizations.")
-
-# Sidebar for uploading CSV files
-st.sidebar.header("Upload CSV Files")
-batting_file = st.sidebar.file_uploader("Upload Batting CSV", type=["csv"])
-bowling_file = st.sidebar.file_uploader("Upload Bowling CSV", type=["csv"])
-
-# Check if both files are uploaded
-if batting_file and bowling_file:
-    # Load CSVs
-    batting_df = pd.read_csv(batting_file)
-    bowling_df = pd.read_csv(bowling_file)
-
-    # Extract unique teams from both CSVs
-    teams = sorted(set(batting_df['Team'].unique()).union(bowling_df['Team'].unique()))
-    selected_team = st.sidebar.selectbox("Select a Team", teams)
-
-    # Extract unique players for the selected team from both CSVs
-    batting_players = set(batting_df[batting_df['Team'] == selected_team]['Player'].unique())
-    bowling_players = set(bowling_df[bowling_df['Team'] == selected_team]['Player'].unique())
-
-    # Combine players from both CSVs
-    all_players = sorted(batting_players.union(bowling_players))
-    selected_player = st.sidebar.selectbox("Select a Player", all_players)
-
-    # Filter the data for the selected player
-    player_batting_data = batting_df[(batting_df['Player'] == selected_player) & (batting_df['Team'] == selected_team)]
-    player_bowling_data = bowling_df[(bowling_df['Player'] == selected_player) & (bowling_df['Team'] == selected_team)]
-
-    if not player_batting_data.empty and not player_bowling_data.empty:
-        # Display the team and player name
-        st.sidebar.markdown(f"### 🏏 **Team:** {selected_team}")
-        st.sidebar.markdown(f"### 👤 **Player:** {selected_player}")
-
-        # Display metrics side by side
-        col1, col2 = st.columns(2)
-
-        # Batting Metrics Visualization
-        with col1:
-            st.subheader(f"{selected_player} - Batting Metrics")
-
-            fig, ax = plt.subplots(1, 2, figsize=(12, 5))
-
-            # Batting Runs vs Matches
-            sns.barplot(x="Matches", y="Runs", data=player_batting_data, ax=ax[0], color='blue')
-            ax[0].set_title("Matches vs Runs")
-            ax[0].set_xlabel("Matches")
-            ax[0].set_ylabel("Runs")
-
-            # Batting Average over Years
-            sns.lineplot(x="Year", y="Average", data=player_batting_data, ax=ax[1], color='green')
-            ax[1].set_title("Batting Average over Years")
-            ax[1].set_xlabel("Year")
-            ax[1].set_ylabel("Average")
-
-            plt.tight_layout()
-            st.pyplot(fig)
-
-        # Bowling Metrics Visualization
-        with col2:
-            st.subheader(f"{selected_player} - Bowling Metrics")
-
-            fig, ax = plt.subplots(1, 2, figsize=(12, 5))
-
-            # Bowling Wickets vs Matches
-            sns.barplot(x="Matches", y="Wickets", data=player_bowling_data, ax=ax[0], color='orange')
-            ax[0].set_title("Matches vs Wickets")
-            ax[0].set_xlabel("Matches")
-            ax[0].set_ylabel("Wickets")
-
-            # Bowling Economy over Years
-            sns.lineplot(x="Year", y="Economy", data=player_bowling_data, ax=ax[1], color='red')
-            ax[1].set_title("Bowling Economy over Years")
-            ax[1].set_xlabel("Year")
-            ax[1].set_ylabel("Economy")
-
-            plt.tight_layout()
-            st.pyplot(fig)
-
-    else:
-        st.warning(f"No data found for {selected_player}. Please upload valid CSV files.")
-
+import plotly.express as px
+import os
+
+# Streamlit App
+st.set_page_config(page_title="Cricket Player Stats 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")
+
+# Upload CSVs only if they are not already saved
+if not (os.path.exists(batting_path) and os.path.exists(bowling_path)):
+    uploaded_batting = st.sidebar.file_uploader("Upload Batting CSV", type=["csv"], key="batting")
+    uploaded_bowling = st.sidebar.file_uploader("Upload Bowling CSV", type=["csv"], key="bowling")
+    
+    if uploaded_batting and uploaded_bowling:
+        batting_df = pd.read_csv(uploaded_batting)
+        bowling_df = pd.read_csv(uploaded_bowling)
+        
+        # Save the files permanently
+        batting_df.to_csv(batting_path, index=False)
+        bowling_df.to_csv(bowling_path, index=False)
+        st.success("CSV files saved successfully!")
 else:
-    st.info("Please upload both Batting and Bowling CSV files to view player metrics.")
+    # Load saved CSVs
+    batting_df = pd.read_csv(batting_path)
+    bowling_df = pd.read_csv(bowling_path)
+
+# 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")
+
+# Batting Visualization
+if not player_batting.empty:
+    st.subheader("Batting Stats")
+    col1, col2 = st.columns(2)
+    with col1:
+        fig_bat_runs = px.bar(player_batting, x='Format', y='Runs', color='Format', title='Runs by Format')
+        st.plotly_chart(fig_bat_runs, use_container_width=True)
+    with col2:
+        fig_bat_sr = px.line(player_batting, x='Format', y='SR', title='Strike Rate by Format')
+        st.plotly_chart(fig_bat_sr, use_container_width=True)
+
+# Bowling Visualization
+if not player_bowling.empty:
+    st.subheader("Bowling Stats")
+    col3, col4 = st.columns(2)
+    with col3:
+        fig_bowl_wickets = px.bar(player_bowling, x='Format', y='Wickets', color='Format', title='Wickets by Format')
+        st.plotly_chart(fig_bowl_wickets, use_container_width=True)
+    with col4:
+        fig_bowl_eco = px.line(player_bowling, x='Format', y='Eco', title='Economy Rate by Format')
+        st.plotly_chart(fig_bowl_eco, use_container_width=True)