Update pages/player_comparison_images.py

pages/player_comparison_images.py

@@ -0,0 +1,170 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+import cv2
+from PIL import Image
+import plotly.express as px
+import plotly.graph_objects as go
+
+# -------------------------------
+# 1. Setup
+# -------------------------------
+st.set_page_config(page_title="Cricket Player Comparision", layout="centered")
+st.title("🏏 Cricket Player Comparision Tool")
+
+# -------------------------------
+# 2. Load Data & Models
+# -------------------------------
+@st.cache_data
+def load_dataset():
+    return pd.read_csv("cric_final.csv")
+
+@st.cache_resource
+def load_assets():
+    model = joblib.load("svc_face_classifier.pkl")
+    encoder = joblib.load("label_encoder.pkl")
+    detector = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+    return model, encoder, detector
+
+df = load_dataset()
+model, encoder, detector = load_assets()
+formats = ["Test", "ODI", "T20", "IPL"]
+
+# -------------------------------
+# 3. Player Detection from Image
+# -------------------------------
+def detect_player(image_file, model, encoder, detector):
+    try:
+        img = Image.open(image_file).convert("RGB")
+        img_np = np.array(img)
+        gray = cv2.cvtColor(img_np, cv2.COLOR_RGB2GRAY)
+        faces = detector.detectMultiScale(gray, 1.3, 5)
+        if not len(faces):
+            return None, "No face detected"
+        x, y, w, h = faces[0]
+        face = cv2.resize(gray[y:y+h, x:x+w], (64, 64)).flatten().reshape(1, -1)
+        label = model.predict(face)[0]
+        name = encoder.inverse_transform([label])[0]
+        return name, None
+    except Exception as e:
+        return None, str(e)
+
+# -------------------------------
+# 4. Player Stats Summary
+# -------------------------------
+def get_summary(player_row, formats):
+    return {
+        "Total Runs": sum(player_row.get(f'batting_Runs_{f}', 0) for f in formats),
+        "Total Wickets": sum(player_row.get(f'bowling_{f}_Wickets', 0) for f in formats),
+        "Best Strike Rate": max(player_row.get(f'batting_SR_{f}', 0) for f in formats)
+    }
+
+# -------------------------------
+# 5. Upload Player Images
+# -------------------------------
+col1, col2 = st.columns(2)
+with col1:
+    img1 = st.file_uploader("Upload Player 1 Image", type=["jpg", "png", "jpeg"], key="img1")
+with col2:
+    img2 = st.file_uploader("Upload Player 2 Image", type=["jpg", "png", "jpeg"], key="img2")
+
+p1_name, p2_name = None, None
+
+if img1:
+    p1_name, err1 = detect_player(img1, model, encoder, detector)
+    if err1:
+        st.error(f"Player 1 Error: {err1}")
+    else:
+        col1.image(img1, caption=f"Player 1: {p1_name}", width=200)
+
+if img2:
+    p2_name, err2 = detect_player(img2, model, encoder, detector)
+    if err2:
+        st.error(f"Player 2 Error: {err2}")
+    else:
+        col2.image(img2, caption=f"Player 2: {p2_name}", width=200)
+
+if not (p1_name and p2_name):
+    st.warning("Upload images for both players to load the data.")
+    st.stop()
+
+if p1_name not in df["Player"].values or p2_name not in df["Player"].values:
+    st.error("One or both players are not in the dataset.")
+    st.stop()
+
+# -------------------------------
+# 6. Stats Extraction
+# -------------------------------
+player1 = df[df["Player"] == p1_name].iloc[0]
+player2 = df[df["Player"] == p2_name].iloc[0]
+stats1 = get_summary(player1, formats)
+stats2 = get_summary(player2, formats)
+
+# -------------------------------
+# 7. Display Summary Stats
+# -------------------------------
+st.subheader("📊 Player Summary")
+col1, col2 = st.columns(2)
+for col, stats in zip([col1, col2], [stats1, stats2]):
+    col.metric("Total Runs", stats["Total Runs"])
+    col.metric("Total Wickets", stats["Total Wickets"])
+    col.metric("Best SR", round(stats["Best Strike Rate"], 2))
+
+# -------------------------------
+# 8. Visual Comparisons
+# -------------------------------
+st.markdown("## 📈 Visual Comparison")
+
+# Batting
+bat_df = pd.DataFrame({
+    "Format": formats,
+    p1_name: [player1.get(f'batting_Runs_{f}', 0) for f in formats],
+    p2_name: [player2.get(f'batting_Runs_{f}', 0) for f in formats]
+})
+st.plotly_chart(px.bar(bat_df, x="Format", y=[p1_name, p2_name], barmode="group", title="Batting Runs"))
+
+# Bowling
+bowl_df = pd.DataFrame({
+    "Format": formats,
+    p1_name: [player1.get(f'bowling_{f}_Wickets', 0) for f in formats],
+    p2_name: [player2.get(f'bowling_{f}_Wickets', 0) for f in formats]
+})
+st.plotly_chart(px.bar(bowl_df, x="Format", y=[p1_name, p2_name], barmode="group", title="Bowling Wickets"))
+
+# -------------------------------
+
+# Strike Rate Comparison
+sr_df = pd.DataFrame({
+    "Format": formats,
+    p1_name: [player1.get(f'batting_SR_{f}', 0) for f in formats],
+    p2_name: [player2.get(f'batting_SR_{f}', 0) for f in formats]
+})
+st.plotly_chart(px.bar(sr_df, x="Format", y=[p1_name, p2_name], barmode="group", title="Strike Rate Comparison"))
+
+#---------------------------------
+# 9. Match Distribution
+# -------------------------------
+for name, row, col, key_suffix in zip(
+    [p1_name, p2_name], 
+    [player1, player2], 
+    [col1, col2], 
+    ["player1", "player2"]
+):
+    values = [row.get(f"Matches_{f}", 0) for f in formats]
+    fig = px.pie(
+        values=values, 
+        names=formats, 
+        title=f"{name}'s Match Distribution"
+    )
+    col.plotly_chart(fig, key=f"pie_chart_{key_suffix}")  # 👈 Unique key
+
+# 10. Milestones
+# -------------------------------
+st.subheader("🏆 Milestones")
+for fmt in formats:
+    st.markdown(f"### {fmt}")
+    col1, col2 = st.columns(2)
+    for m in ["50s", "100s", "200s"]:
+        col1.metric(f"{p1_name} {m}", player1.get(f"batting_{m}_{fmt}", 0))
+        col2.metric(f"{p2_name} {m}", player2.get(f"batting_{m}_{fmt}", 0))