Update pages/2_Players_Comparison.py

pages/2_Players_Comparison.py

@@ -6,10 +6,6 @@ import joblib
 import cv2
 from PIL import Image
 
-# ----------------------------
-# Utility Functions
-# ----------------------------
-
 @st.cache_data
 def load_data():
     return pd.read_csv("cric_final.csv")
@@ -37,6 +33,26 @@ def detect_face_and_predict(image_file, model, encoder, face_detector):
     except Exception as e:
         return None, f"Error processing image: {e}"
 
+def plot_bar_comparison(formats, p1_vals, p2_vals, p1, p2, title, yaxis):
+    df = pd.DataFrame({
+        "Format": formats * 2,
+        "Player": [p1]*len(formats) + [p2]*len(formats),
+        yaxis: p1_vals + p2_vals
+    })
+    fig = px.bar(df, x="Format", y=yaxis, color="Player", barmode="group", text=yaxis,
+                 title=title, height=400)
+    st.plotly_chart(fig, use_container_width=True)
+
+def plot_line_comparison(formats, p1_vals, p2_vals, p1, p2, title, yaxis):
+    df = pd.DataFrame({
+        "Format": formats * 2,
+        "Player": [p1]*len(formats) + [p2]*len(formats),
+        yaxis: p1_vals + p2_vals
+    })
+    fig = px.line(df, x="Format", y=yaxis, color="Player", markers=True, text=yaxis,
+                  title=title, height=400)
+    st.plotly_chart(fig, use_container_width=True)
+
 def summarize_player(player_data, formats):
     return {
         "Total Runs": sum(player_data.get(f'batting_Runs_{fmt}', 0) for fmt in formats),
@@ -44,24 +60,15 @@ def summarize_player(player_data, formats):
         "Best SR": max(player_data.get(f'batting_SR_{fmt}', 0) for fmt in formats)
     }
 
-# ----------------------------
-# App Configuration
-# ----------------------------
-
 st.set_page_config(page_title="Cricket Player Comparison", layout="centered")
 st.header("🏏 :rainbow[Face-Based Recognition and Stats Analysis of Cricket Players]")
 
-# Load Data & Models
 df = load_data()
 model, label_encoder, face_cascade = load_models()
 
 formats = ['Test', 'ODI', 'T20', 'IPL']
 indian_players = sorted(df[df['Player_Team'] == 'India']['Player'].unique())
 
-# ----------------------------
-# Image Upload & Player Override
-# ----------------------------
-
 col1, col2 = st.columns(2)
 with col1:
     img1 = st.file_uploader("Upload First Player Image", type=["jpg", "jpeg", "png"], key="img1")
@@ -98,10 +105,6 @@ if p1_name not in df['Player'].values or p2_name not in df['Player'].values:
     st.error("Selected players not found in dataset.")
     st.stop()
 
-# ----------------------------
-# Player Data & Comparison Metrics
-# ----------------------------
-
 p1_data = df[df['Player'] == p1_name].iloc[0]
 p2_data = df[df['Player'] == p2_name].iloc[0]
 
@@ -121,57 +124,45 @@ with col2:
     st.metric("Total Wickets", p2_summary["Total Wickets"])
     st.metric("Best Strike Rate", f"{p2_summary['Best SR']:.2f}")
 
-# ----------------------------
-# Visualization Tabs
-# ----------------------------
-
 st.subheader("📊 Player Comparison Dashboard")
-tabs = st.tabs(["🏏 Batting", "🎯 Bowling", "⚡ Strike Rate", "🏆 Milestones", "🧩 Matches"])
-
-with tabs[0]:
-    st.markdown("### Batting Summary")
-    bat_df = pd.DataFrame({
-        "Format": formats,
-        p1_name: [p1_data.get(f'batting_Runs_{fmt}', 0) for fmt in formats],
-        p2_name: [p2_data.get(f'batting_Runs_{fmt}', 0) for fmt in formats]
-    }).set_index("Format")
-    st.bar_chart(bat_df)
-
-with tabs[1]:
-    st.markdown("### Bowling Summary")
-    bowl_df = pd.DataFrame({
-        "Format": formats,
-        p1_name: [p1_data.get(f'bowling_{fmt}_Wickets', 0) for fmt in formats],
-        p2_name: [p2_data.get(f'bowling_{fmt}_Wickets', 0) for fmt in formats]
-    }).set_index("Format")
-    st.bar_chart(bowl_df)
-
-with tabs[2]:
-    st.markdown("### Strike Rate vs Runs")
-    sr_data = pd.DataFrame({
-        "Format": formats * 2,
-        "Player": [p1_name] * 4 + [p2_name] * 4,
-        "Runs": [p1_data.get(f'batting_Runs_{fmt}', 0) for fmt in formats] + [p2_data.get(f'batting_Runs_{fmt}', 0) for fmt in formats],
-        "SR": [p1_data.get(f'batting_SR_{fmt}', 0) for fmt in formats] + [p2_data.get(f'batting_SR_{fmt}', 0) for fmt in formats]
-    })
-    fig = px.scatter(sr_data, x="Runs", y="SR", color="Player", text="Format", title="Strike Rate vs Runs by Format")
-    st.plotly_chart(fig, use_container_width=True)
+show_bat = st.checkbox("Show Batting Comparison", value=True)
+show_bowl = st.checkbox("Show Bowling Comparison", value=True)
+
+if show_bat:
+    st.markdown(f"### Batting Comparison: {p1_name} vs {p2_name}")
+    for metric, prefix in {
+        "Runs": "batting_Runs_",
+        "50s": "batting_50s_",
+        "100s": "batting_100s_"
+    }.items():
+        p1_vals = [p1_data.get(f"{prefix}{fmt}", 0) for fmt in formats]
+        p2_vals = [p2_data.get(f"{prefix}{fmt}", 0) for fmt in formats]
+        plot_bar_comparison(formats, p1_vals, p2_vals, p1_name, p2_name, f"{metric} by Format", metric)
+
+    for metric, prefix in {
+        "Average": "batting_Average_",
+        "Strike Rate": "batting_SR_"
+    }.items():
+        p1_vals = [p1_data.get(f"{prefix}{fmt}", 0) for fmt in formats]
+        p2_vals = [p2_data.get(f"{prefix}{fmt}", 0) for fmt in formats]
+        plot_line_comparison(formats, p1_vals, p2_vals, p1_name, p2_name, f"{metric} by Format", metric)
+
+if show_bowl:
+    st.markdown(f"### Bowling Comparison: {p1_name} vs {p2_name}")
+    for metric, suffix in {
+        "Wickets": "_Wickets",
+        "Maidens": "_Maidens",
+        "Economy": "_Eco"
+    }.items():
+        p1_vals = [p1_data.get(f"bowling_{fmt}{suffix}", 0) for fmt in formats]
+        p2_vals = [p2_data.get(f"bowling_{fmt}{suffix}", 0) for fmt in formats]
+        plot_bar_comparison(formats, p1_vals, p2_vals, p1_name, p2_name, f"{metric} by Format", metric)
+
+    for metric, suffix in {
+        "Average": "_Avg",
+        "Strike Rate": "_SR"
+    }.items():
+        p1_vals = [p1_data.get(f"bowling_{fmt}{suffix}", 0) for fmt in formats]
+        p2_vals = [p2_data.get(f"bowling_{fmt}{suffix}", 0) for fmt in formats]
+        plot_line_comparison(formats, p1_vals, p2_vals, p1_name, p2_name, f"{metric} by Format", metric)
 
-with tabs[3]:
-    st.markdown("### Milestones")
-    for fmt in formats:
-        col1, col2 = st.columns(2)
-        col1.metric(f"{p1_name} - 50s ({fmt})", p1_data.get(f"batting_50s_{fmt}", 0))
-        col2.metric(f"{p2_name} - 50s ({fmt})", p2_data.get(f"batting_50s_{fmt}", 0))
-        col1.metric(f"{p1_name} - 100s ({fmt})", p1_data.get(f"batting_100s_{fmt}", 0))
-        col2.metric(f"{p2_name} - 100s ({fmt})", p2_data.get(f"batting_100s_{fmt}", 0))
-        col1.metric(f"{p1_name} - 200s ({fmt})", p1_data.get(f"batting_200s_{fmt}", 0))
-        col2.metric(f"{p2_name} - 200s ({fmt})", p2_data.get(f"batting_200s_{fmt}", 0))
-
-with tabs[4]:
-    st.markdown("### Match Distribution")
-    col1, col2 = st.columns(2)
-    for i, (player, pdata, col) in enumerate(zip([p1_name, p2_name], [p1_data, p2_data], [col1, col2])):
-        match_counts = {fmt: pdata.get(f"Matches_{fmt}", 0) for fmt in formats}
-        fig = px.pie(values=list(match_counts.values()), names=list(match_counts.keys()), title=f"{player}'s Match Distribution")
-        col.plotly_chart(fig, use_container_width=True)