Update pages/Automatic_Machine_learning_app.py

pages/Automatic_Machine_learning_app.py

@@ -1 +1,76 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+
+# Set page title
+st.title("Cricket Icons")
+
+# Load datasets
+batting_data = pd.read_csv("ireland_batting.csv")
+bowling_data = pd.read_csv("ireland_bowling.csv")
+
+# Get unique player names
+players = sorted(set(batting_data["Player"]).intersection(set(bowling_data["Player"])))
+
+# Create two columns for Batting and Bowling sections
+col1, col2 = st.columns(2)
+
+# ---------------- BATSMAN SECTION ---------------- #
+with col1:
+    st.subheader("Batting Statistics")
+
+    # Select player for batting stats
+    batting_player = st.selectbox("Select a Player (Batting):", players, key="batting_player")
+
+    # Filter the player's batting data
+    batting_info = batting_data[batting_data["Player"] == batting_player]
+
+    # Display batting details
+    st.write(f"Batting Information About: {batting_player}")
+    st.dataframe(batting_info)
+
+    # Generate charts
+    if not batting_info.empty:
+        fig = px.pie(batting_info, names="Cricket Matches", values="Matches", title="Matches Distribution")
+        st.plotly_chart(fig)
+
+        fig = px.bar(batting_info, x="Cricket Matches", y=["Matches", "Innings"], title="Matches vs Innings", barmode="group", text_auto=True)
+        st.plotly_chart(fig)
+
+        fig = px.bar(batting_info, x="Cricket Matches", y=["Matches", "Innings", "Not Out"], title="Matches, Innings & Not Outs", barmode="group", text_auto=True)
+        st.plotly_chart(fig)
+
+        fig = px.bar(batting_info, x="Cricket Matches", y=["Runs", "SR"], title="Runs vs Strike Rate", barmode="group", text_auto=True)
+        st.plotly_chart(fig)
+
+        fig = px.bar(batting_info, x="Cricket Matches", y=["Runs", "Balls"], title="Runs vs Balls", barmode="group", text_auto=True)
+        st.plotly_chart(fig)
+
+        fig = px.bar(batting_info, x="Cricket Matches", y=["Runs", "Fours", "Sixes"], title="Runs vs Fours vs Sixes", barmode="group", text_auto=True)
+        st.plotly_chart(fig)
+
+        fig = px.bar(batting_info, x="Cricket Matches", y=["Fours", "Sixes"], title="Boundary Count (Fours & Sixes)", barmode="group", text_auto=True)
+        st.plotly_chart(fig)
+
+        fig = px.bar(batting_info, x="Cricket Matches", y=["50s", "100s", "200s", "300s", "400s"], title="Player Century Breakdown", barmode="group", text_auto=True)
+        st.plotly_chart(fig)
+
+        fig = px.pie(batting_info, names="Cricket Matches", values="Ducks", title="Players with Ducks")
+        st.plotly_chart(fig)
+
+
+# ---------------- BOWLER SECTION ---------------- #
+with col2:
+    st.subheader("Bowling Statistics")
+
+    # Select player for bowling stats
+    bowling_player = st.selectbox("Select a Player (Bowling):", players, key="bowling_player")
+
+    # Filter the player's bowling data
+    bowling_info = bowling_data[bowling_data["Player"] == bowling_player]
+
+    # Display bowling details
+    st.write(f"Bowling Information About: {bowling_player}")
+    st.dataframe(bowling_info)