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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))
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