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Dockerfile

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+# Use Python 3.12 slim for smaller image
+FROM python:3.12-slim
+
+# Install compilation tools and dependencies
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    make \
+    g++ \
+    wget \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set working directory
+WORKDIR /app
+
+# Copy the whole repository (to build the engine)
+COPY . .
+
+# Build the C++ Engine
+WORKDIR /app/engine/src
+RUN make -j$(nproc) build ARCH=x86-64-sse41-popcnt
+RUN mv stockfish /app/engine/deepcastle
+
+# Copy NNUE (It should be in the engine/ folder in the host)
+# If it's missing, you can add a wget command here to download it from a URL.
+# COPY ../engine/output.nnue /app/engine/output.nnue
+
+# Setup Python environment
+WORKDIR /app/server
+RUN pip install --no-cache-dir fastapi uvicorn python-chess pydantic
+
+# Expose Hugging Face mandatory port
+EXPOSE 7860
+
+# Run FastAPI on 0.0.0.0:7860 (Hugging Face default)
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import os
+import chess
+import chess.engine
+import asyncio
+from typing import Optional
+
+app = FastAPI(title="Deepcastle v7 Engine API")
+
+# Allow ALL for easy testing (we can restrict this later if needed)
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Paths relative to the Docker container
+ENGINE_PATH = os.environ.get("ENGINE_PATH", "/app/engine/deepcastle")
+NNUE_PATH = os.environ.get("NNUE_PATH", "/app/engine/output.nnue")
+
+class MoveRequest(BaseModel):
+    fen: str
+    time: float = 1.0  # seconds
+    depth: Optional[int] = None
+
+class MoveResponse(BaseModel):
+    bestmove: str
+    score: float
+    depth: int
+    nodes: int
+    nps: int
+    pv: str
+
+@app.get("/")
+def home():
+    return {"status": "online", "engine": "Deepcastle v7 Hybrid Neural", "platform": "Hugging Face Spaces"}
+
+@app.get("/health")
+def health():
+    if not os.path.exists(ENGINE_PATH):
+        return {"status": "error", "message": "Engine binary not found"}
+    return {"status": "ok", "engine": "Deepcastle v7"}
+
+@app.post("/move", response_model=MoveResponse)
+async def get_move(request: MoveRequest):
+    if not os.path.exists(ENGINE_PATH):
+        raise HTTPException(status_code=500, detail="Engine binary not found")
+
+    try:
+        # Start Engine
+        transport, engine = await chess.engine.popen_uci(ENGINE_PATH)
+        
+        # Configure NNUE
+        if os.path.exists(NNUE_PATH):
+            await engine.configure({"EvalFile": NNUE_PATH})
+            # Optimization for server environment
+            await engine.configure({"Hash": 512, "Threads": 2})
+
+        board = chess.Board(request.fen)
+        
+        # ANALYSIS FOR STATS + BEST MOVE
+        limit = chess.engine.Limit(time=request.time, depth=request.depth)
+        
+        # Get result
+        result = await engine.play(board, limit)
+        
+        # Get info for stats (analysing briefly to get score/pv)
+        info_list = await engine.analyse(board, limit)
+        info = info_list # analyse returns a list in async context if using multipv, but simple by default
+        
+        # Extract stats
+        score = info["score"].relative.score(mate_score=10000) / 100.0 if "score" in info else 0.0
+        depth = info.get("depth", 0)
+        nodes = info.get("nodes", 0)
+        nps = info.get("nps", 0)
+        pv = " ".join([board.san(m) for m in info.get("pv", [])[:5]])
+        
+        await engine.quit()
+        
+        return MoveResponse(
+            bestmove=result.move.uci(),
+            score=score,
+            depth=depth,
+            nodes=nodes,
+            nps=nps,
+            pv=pv
+        )
+    except Exception as e:
+        print(f"Error: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    import uvicorn
+    # Hugging Face Spaces port is 7860
+    uvicorn.run(app, host="0.0.0.0", port=7860)

src.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2a67b4ccdc7a179506f33641de77ef2e70448c151ec439385075cce4b3dadb4c
+size 260583