Update pages/2player_comparison.py

pages/2player_comparison.py

@@ -0,0 +1,71 @@
+import streamlit as st
+import pandas as pd
+import pickle
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Load model and encoder
+@st.cache_resource
+def load_model_and_encoder():
+    with open('best_rf_pipeline.pkl', 'rb') as f:
+        model = pickle.load(f)
+    with open('label_encoder.pkl', 'rb') as f:
+        encoder = pickle.load(f)
+    return model, encoder
+
+# Load player dataset
+@st.cache_data
+def load_data():
+    df = pd.read_csv('Reduced_final_teams.csv')
+    return df
+
+# Main App
+def main():
+    st.title("Cricket Player Comparison Tool 🏏")
+    st.write("Upload two player images and compare their performance visually.")
+
+    df = load_data()
+    model, encoder = load_model_and_encoder()
+
+    # Upload Images
+    col1, col2 = st.columns(2)
+    with col1:
+        img1 = st.file_uploader("Upload Image for Player 1", type=['png', 'jpg', 'jpeg'], key='img1')
+    with col2:
+        img2 = st.file_uploader("Upload Image for Player 2", type=['png', 'jpg', 'jpeg'], key='img2')
+
+    if img1 and img2:
+        # Placeholder: Extract player name from uploaded file name
+        player1_name = img1.name.split('.')[0]
+        player2_name = img2.name.split('.')[0]
+
+        if player1_name in df['Player'].values and player2_name in df['Player'].values:
+            player1_data = df[df['Player'] == player1_name].squeeze()
+            player2_data = df[df['Player'] == player2_name].squeeze()
+
+            st.success(f"Comparing **{player1_name}** vs **{player2_name}**")
+
+            # Combine both players into one DataFrame for visualization
+            comparison_df = pd.DataFrame([player1_data, player2_data])
+            comparison_df.set_index('Player', inplace=True)
+
+            # Display Side-by-Side Metrics
+            st.subheader("🔍 Key Metrics")
+            st.dataframe(comparison_df.T)
+
+            # Radar chart / Barplot for comparison
+            st.subheader("📊 Visual Comparison")
+            num_cols = comparison_df.select_dtypes(include='number').columns.tolist()
+
+            # Plot
+            plt.figure(figsize=(10, 5))
+            comparison_df[num_cols].T.plot(kind='bar', figsize=(10, 6))
+            plt.title(f"Stat Comparison: {player1_name} vs {player2_name}")
+            plt.ylabel("Metric Value")
+            plt.xticks(rotation=45)
+            st.pyplot(plt.gcf())
+        else:
+            st.error("Player names extracted from images do not match any in the dataset. Ensure filenames match player names in the CSV.")
+
+if __name__ == "__main__":
+    main()