Update app.py

app.py

@@ -1,72 +1,83 @@
-import streamlit as st
-from analyzer import ChessAnalyzer
-from PIL import Image
-import io
-import pandas as pd
-from utils import get_eval_bar_image  # NEW import
-
-st.set_page_config(layout="wide")
-st.title("♟️ Chess Game Analyzer")
-
-uploaded_file = st.file_uploader("Upload a PGN file", type=["pgn"])
-
-if uploaded_file:
-    analyzer = ChessAnalyzer(uploaded_file)
-    analyzer.analyze_game()
-
-    scorecards = analyzer.get_scorecards()
-    moves = analyzer.moves_info
-    opening = analyzer.opening_name
-
-    st.subheader(f"📖 Opening: {opening}")
-    col1, col2 = st.columns(2)
-    col1.metric(f"{scorecards['White']['name']} (White)", f"{scorecards['White']['accuracy']}% Accuracy")
-    col2.metric(f"{scorecards['Black']['name']} (Black)", f"{scorecards['Black']['accuracy']}% Accuracy")
-
-    st.markdown("---")
-    st.subheader("🧠 Move-by-Move Analysis")
-
-    selected_index = st.slider("Select Move Number", 1, len(moves), 1)
-    selected_move = moves[selected_index - 1]
-
-    # Evaluation bar and board side-by-side
-    from PIL import Image as PILImage
-
-    img_col, bar_col = st.columns([5, 1], gap="small")
-
-    with img_col:
-        st.image(
-            PILImage.open(io.BytesIO(selected_move["image"])),
-            caption=f"{selected_move['color']} plays {selected_move['move']}"
-        )
-
-    with bar_col:
-        bar_image = get_eval_bar_image(selected_move["eval_before"])
-        st.image(
-            bar_image,
-            use_container_width=False,
-            width=40
-        )
-
-
-
-    st.write(f"**Move Type:** `{selected_move['type']}`")
-    st.write(f"**Centipawn Loss:** `{int(selected_move['cp_loss'])}`")
-    st.write(f"**Evaluation Before:** `{selected_move['eval_before']}`")
-    st.write(f"**Evaluation After:** `{selected_move['eval_after']}`")
-
-    st.markdown("---")
-    st.subheader("📋 All Moves Table")
-
-    table_data = [
-        {
-            "Move #": move["move_number"],
-            "Color": move["color"],
-            "Move": move["move"],
-            "Type": move["type"],
-            "CP Loss": int(move["cp_loss"])
-        }
-        for move in moves
-    ]
-    df = pd.DataFrame(table_data)
-    st.dataframe(df, use_container_width=True)
+import streamlit as st
+from analyzer import ChessAnalyzer
+from PIL import Image
+import io
+import pandas as pd
+from utils import get_eval_bar_image
+import platform
+import os
+import urllib.request
+
+# 🔁 Ensure stockfish is ready
+if platform.system() != "Windows":
+    if not os.path.exists("stockfish"):
+        print("⏬ Downloading Stockfish binary from Hugging Face...")
+        url = "https://huggingface.co/datasets/DheerajAlamuri/stockfish-binary/resolve/main/stockfish"
+        urllib.request.urlretrieve(url, "stockfish")
+        os.chmod("stockfish", 0o755)
+
+st.set_page_config(layout="wide")
+st.title("♟️ Chess Game Analyzer")
+
+uploaded_file = st.file_uploader("Upload a PGN file", type=["pgn"])
+
+if uploaded_file:
+    analyzer = ChessAnalyzer(uploaded_file)
+    analyzer.analyze_game()
+
+    scorecards = analyzer.get_scorecards()
+    moves = analyzer.moves_info
+    opening = analyzer.opening_name
+
+    st.subheader(f"📖 Opening: {opening}")
+    col1, col2 = st.columns(2)
+    col1.metric(f"{scorecards['White']['name']} (White)", f"{scorecards['White']['accuracy']}% Accuracy")
+    col2.metric(f"{scorecards['Black']['name']} (Black)", f"{scorecards['Black']['accuracy']}% Accuracy")
+
+    st.markdown("---")
+    st.subheader("🧠 Move-by-Move Analysis")
+
+    selected_index = st.slider("Select Move Number", 1, len(moves), 1)
+    selected_move = moves[selected_index - 1]
+
+    # Evaluation bar and board side-by-side
+    from PIL import Image as PILImage
+
+    img_col, bar_col = st.columns([5, 1], gap="small")
+
+    with img_col:
+        st.image(
+            PILImage.open(io.BytesIO(selected_move["image"])),
+            caption=f"{selected_move['color']} plays {selected_move['move']}"
+        )
+
+    with bar_col:
+        bar_image = get_eval_bar_image(selected_move["eval_before"])
+        st.image(
+            bar_image,
+            use_container_width=False,
+            width=40
+        )
+
+
+
+    st.write(f"**Move Type:** `{selected_move['type']}`")
+    st.write(f"**Centipawn Loss:** `{int(selected_move['cp_loss'])}`")
+    st.write(f"**Evaluation Before:** `{selected_move['eval_before']}`")
+    st.write(f"**Evaluation After:** `{selected_move['eval_after']}`")
+
+    st.markdown("---")
+    st.subheader("📋 All Moves Table")
+
+    table_data = [
+        {
+            "Move #": move["move_number"],
+            "Color": move["color"],
+            "Move": move["move"],
+            "Type": move["type"],
+            "CP Loss": int(move["cp_loss"])
+        }
+        for move in moves
+    ]
+    df = pd.DataFrame(table_data)
+    st.dataframe(df, use_container_width=True)