feat: integrate NNUE bot and add Python training pipeline with weight export functionality

modules/bot/src/main/scala/de/nowchess/bot/bots/NNUEBot.scala

@@ -1,4 +1,4 @@
-package de.nowchess.bot.bots.nnue
+package de.nowchess.bot.bots
 
 import de.nowchess.api.game.GameContext
 import de.nowchess.api.move.Move

modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/NNUE.scala

@@ -2,15 +2,58 @@ package de.nowchess.bot.bots.nnue
 
 import de.nowchess.api.board.{Board, Color, File, PieceType, Rank, Square}
 import de.nowchess.api.game.GameContext
+import java.nio.ByteBuffer
+import java.nio.ByteOrder
 
 class NNUE:
 
-  private val l1Weights = NNUEWeights.l1_weights
-  private val l1Bias = NNUEWeights.l1_bias
-  private val l2Weights = NNUEWeights.l2_weights
-  private val l2Bias = NNUEWeights.l2_bias
-  private val l3Weights = NNUEWeights.l3_weights
-  private val l3Bias = NNUEWeights.l3_bias
+  private val (l1Weights, l1Bias, l2Weights, l2Bias, l3Weights, l3Bias) = loadWeights()
+
+  private def loadWeights(): (Array[Float], Array[Float], Array[Float], Array[Float], Array[Float], Array[Float]) =
+    val stream = getClass.getResourceAsStream("/nnue_weights.bin")
+    if stream == null then
+      throw RuntimeException("NNUE weights file not found in resources")
+
+    try
+      val bytes = stream.readAllBytes()
+      val buffer = ByteBuffer.wrap(bytes).order(ByteOrder.LITTLE_ENDIAN)
+
+      // Read and verify magic number
+      val magic = buffer.getInt()
+      if magic != 0x4555_4e4e then // "NNUE" in little-endian
+        throw RuntimeException(s"Invalid magic number: 0x${magic.toHexString}")
+
+      // Read version
+      val version = buffer.getInt()
+      if version != 1 then
+        throw RuntimeException(s"Unsupported weight version: $version")
+
+      // Read all weight tensors in order
+      val l1w = readTensor(buffer)
+      val l1b = readTensor(buffer)
+      val l2w = readTensor(buffer)
+      val l2b = readTensor(buffer)
+      val l3w = readTensor(buffer)
+      val l3b = readTensor(buffer)
+
+      (l1w, l1b, l2w, l2b, l3w, l3b)
+    finally stream.close()
+
+  private def readTensor(buffer: ByteBuffer): Array[Float] =
+    // Read shape
+    val shapeLen = buffer.getInt()
+    val shape = Array.ofDim[Int](shapeLen)
+    for i <- 0 until shapeLen do
+      shape(i) = buffer.getInt()
+
+    // Calculate total elements
+    val totalElements = shape.product
+
+    // Read float data
+    val floats = Array.ofDim[Float](totalElements)
+    for i <- 0 until totalElements do
+      floats(i) = buffer.getFloat()
+    floats
 
   // Pre-allocated buffers for inference
   private val features = new Array[Float](768)
@@ -19,7 +62,7 @@ class NNUE:
 
   /** Convert a position to 768-dimensional binary feature vector.
    *  12 piece types (white pawn to black king) × 64 squares from white's perspective. */
-  def positionToFeatures(board: Board, sideToMove: Color): Array[Float] =
+  private def positionToFeatures(board: Board, sideToMove: Color): Array[Float] =
     // Zero out features array
     java.util.Arrays.fill(features, 0f)
 

modules/bot/src/main/scala/de/nowchess/bot/bots/nnue/NNUEWeights.scala

@@ -1,39 +0,0 @@
-package de.nowchess.bot.bots.nnue
-
-object NNUEWeights:
-
-  // PLACEHOLDER: This file is generated by export_weights.py
-  // Run: python3 modules/bot/python/run_pipeline.sh to generate actual weights
-
-  // Layer 1: Input(768) -> Hidden(256)
-  val l1_weights = Array(
-    0f
-  )
-  // Shape: [256, 768]
-
-  val l1_bias = Array(
-    0f
-  )
-  // Shape: [256]
-
-  // Layer 2: Hidden(256) -> Hidden(32)
-  val l2_weights = Array(
-    0f
-  )
-  // Shape: [32, 256]
-
-  val l2_bias = Array(
-    0f
-  )
-  // Shape: [32]
-
-  // Layer 3: Hidden(32) -> Output(1)
-  val l3_weights = Array(
-    0f
-  )
-  // Shape: [1, 32]
-
-  val l3_bias = Array(
-    0f
-  )
-  // Shape: [1]