Update pages/2_Players_Comparison.py

pages/2_Players_Comparison.py

@@ -0,0 +1,160 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+import plotly.express as px
+from PIL import Image
+import requests
+import io
+
+# Page config
+st.set_page_config(page_title="HomePage", page_icon="🚀", layout="wide")
+
+# Background + CSS styling
+st.markdown("""
+    <style>
+    .stApp {
+        background-color: #E3F2FD;
+        font-family: 'Inter', sans-serif;
+    }
+    table {
+        width: 100%;
+        border-collapse: collapse;
+        font-size: 14px;
+        margin-top: 20px;
+    }
+    th, td {
+        border: 1px solid #ddd;
+        padding: 8px;
+        text-align: center;
+    }
+    thead {
+        background-color: #f2f2f2;
+    }
+    tbody tr:nth-child(even) {
+        background-color: #f9f9f9;
+    }
+    tbody tr:hover {
+        background-color: #f1f1f1;
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+# Title
+st.markdown("<h1 style='text-align: center; color: #c71585; font-size: 36px;'>Player Comparison</h1>", unsafe_allow_html=True)
+
+# Load model and data from Hugging Face
+@st.cache_resource
+def load_model_and_data():
+    model_url = ""
+    encoder_url = "https://huggingface.co/spaces/LakshmiHarika/Cricket_Dashboard/resolve/main/pages/label_encoder.pkl"
+    csv_url = ""
+
+    model = pickle.load(io.BytesIO(requests.get(model_url).content))
+    encoder = pickle.load(io.BytesIO(requests.get(encoder_url).content))
+    df = pd.read_csv(csv_url)
+
+    return model, encoder, df
+
+# Chart functions
+def plot_bar_comparison(x, p1_vals, p2_vals, p1, p2, title, ylabel):
+    fig = px.bar(
+        x=x * 2,
+        y=p1_vals + p2_vals,
+        color=[p1] * len(x) + [p2] * len(x),
+        barmode="group",
+        labels={"x": "Format", "y": ylabel, "color": "Player"},
+        title=title,
+        text=p1_vals + p2_vals
+    )
+    fig.update_traces(textposition='outside')
+    st.plotly_chart(fig, use_container_width=True)
+
+def plot_line_comparison(x, p1_vals, p2_vals, p1, p2, title, ylabel):
+    fig = px.line(
+        x=x * 2,
+        y=p1_vals + p2_vals,
+        color=[p1] * len(x) + [p2] * len(x),
+        markers=True,
+        labels={"x": "Format", "y": ylabel, "color": "Player"},
+        title=title
+    )
+    st.plotly_chart(fig, use_container_width=True)
+
+# Upload images
+col1, col2 = st.columns(2)
+with col1:
+    uploaded_file1 = st.file_uploader("Upload Player Image 1", type=["jpg", "jpeg", "png"])
+    if uploaded_file1:
+        st.image(uploaded_file1, caption="Player 1 Image")
+with col2:
+    uploaded_file2 = st.file_uploader("Upload Player Image 2", type=["jpg", "jpeg", "png"])
+    if uploaded_file2:
+        st.image(uploaded_file2, caption="Player 2 Image")
+
+# Load model and data
+model, encoder, stats_df = load_model_and_data()
+
+# When both images are uploaded
+if uploaded_file1 and uploaded_file2:
+    img1 = Image.open(uploaded_file1).convert("L").resize((50, 50))
+    img2 = Image.open(uploaded_file2).convert("L").resize((50, 50))
+
+    arr1 = np.array(img1).flatten().reshape(1, -1)
+    arr2 = np.array(img2).flatten().reshape(1, -1)
+
+    pred1 = model.predict(arr1)[0]
+    pred2 = model.predict(arr2)[0]
+
+    player1 = encoder.inverse_transform([pred1])[0]
+    player2 = encoder.inverse_transform([pred2])[0]
+
+    st.subheader(f"{player1} vs {player2}")
+
+    c1, c2 = st.columns(2)
+    show_bat = c1.button("Show Batting Comparison 🏏")
+    show_bowl = c2.button("Show Bowling Comparison 🎯")
+
+    p1_data = stats_df[stats_df["Player"] == player1].iloc[0]
+    p2_data = stats_df[stats_df["Player"] == player2].iloc[0]
+    formats = ["Test", "ODI", "T20", "IPL"]
+
+    # Batting section
+    if show_bat:
+        st.markdown(f"### Batting Comparison: {player1} vs {player2}")
+        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, player1, player2, 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, player1, player2, f"{metric} by Format", metric)
+
+    # Bowling section
+    if show_bowl:
+        st.markdown(f"### Bowling Comparison: {player1} vs {player2}")
+        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, player1, player2, 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, player1, player2, f"{metric} by Format", metric)