Deployment Fixes

.gitignore

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+/__pycache__
+/venv

Dockerfile

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+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["gunicorn", "-b", "0.0.0.0:7860", "app:app"]

Model_Handler.py

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+import numpy as np
+import chess
+import tensorflow as tf
+from keras.models import load_model
+
+class Model_handler :
+  def __init__(self) :
+    self.model = load_model("model.h5")
+    print("Model loaded and is ready to use !!!")
+
+  def predict(self, state, color) :
+    prediction = self.model(self.convert_state_to_input(state, color))
+    return self.convert_output_to_probs(state, color, prediction[1][0])
+
+  def convert_state_to_input(self, state, color) :
+    if type(state) == str :
+      temp = state.split("_")
+      temp_arr = np.zeros((8,8,12), dtype = np.float16)
+      arr2 = np.zeros((8,8,10), dtype = np.float16)
+      temp_arr, arr2 = self.convert_board_to_input(temp[-1], color)
+      for i in range(1,5) :
+        temp_arr = np.concatenate([self.convert_board_to_input(temp[-1-i], color, False), temp_arr], axis = 2)
+      return (np.expand_dims(temp_arr, axis = 0), np.expand_dims(arr2, axis = 0))
+
+  def convert_board_to_input(self, state, color, current = True) :
+    if current :
+      board = chess.Board(state)
+      board.turn = color
+      arr = np.zeros((8,8,12), dtype = np.float16)
+      arr2 = np.zeros((8,8,10), dtype = np.float16)
+      piece_to_value = self.get_piece_to_value(color)
+      piece_to_value2 = self.get_piece_to_value(color, False)
+      for i in range(64) :
+        if(board.piece_at(i) is not None) :
+          arr[i//8,i%8,piece_to_value[board.piece_at(i).symbol()]] = 1
+      for move in board.legal_moves :
+        square = move.to_square
+        arr[square//8, square%8, piece_to_value[board.piece_at(move.from_square).symbol()]] = 0.5
+        symbol = board.piece_at(move.from_square).symbol()
+        if move.promotion is not None :
+          arr2[move.promotion-2, move.from_square%8, 9] = 1
+        else :
+          arr2[square//8, square%8, piece_to_value2[symbol]] = 1
+          if(piece_to_value2[symbol] == 1 or piece_to_value2[symbol] == 3 or piece_to_value2[symbol] == 5) :
+            piece_to_value2[symbol] += 1
+      return (arr, arr2)
+    else :
+      arr = np.zeros((8,8,12), dtype = np.float16)
+      if len(state) == 0 :
+        return arr
+      board = chess.Board(state)
+      board.turn = color
+      piece_to_value = self.get_piece_to_value(color)
+      for i in range(64) :
+        if(board.piece_at(i) is not None) :
+          arr[i//8,i%8,piece_to_value[board.piece_at(i).symbol()]] = 1
+      return arr
+    
+  def convert_output_to_probs(self, state, color, policy_output) :
+    policy = tf.reshape(policy_output, [8,8,10])
+    board = chess.Board(state.split("_")[-1])
+    board.turn = color
+    piece_to_value = self.get_piece_to_value(color,False)
+    move_dict = {}
+    for move in list(board.legal_moves) :
+      to_square = move.to_square
+      from_square = move.from_square
+      piece_type = piece_to_value[board.piece_at(from_square).symbol()]
+      if move.promotion is not None :
+        move_dict[move.uci()] = policy[move.promotion-2, from_square%8, 9]
+      else :
+        move_dict[move.uci()] = policy[to_square//8, to_square%8, piece_type]
+        if(piece_type == 1 or piece_type == 3 or piece_type == 5) :
+          piece_to_value[board.piece_at(from_square).symbol()] += 1
+   # print(list(board.legal_moves))
+    move = [item[0] for item in sorted(move_dict.items(), key = lambda x: x[1], reverse = True)][0]
+    print(move_dict[move])
+    return move
+  
+  def get_piece_to_value(self, color, inp = True) :
+    if inp :
+      if(color == 1) :
+        return {
+        'P': 0, 'N': 1, 'B': 2, 'R': 3, 'Q': 4, 'K': 5,
+        'p': 6, 'n':7, 'b': 8, 'r': 9, 'q': 10, 'k': 11
+        }
+      return {
+        'p': 0, 'n': 1, 'b': 2, 'r': 3, 'q': 4, 'k': 5,
+        'P': 6, 'N':7, 'B': 8, 'R': 9, 'Q': 10, 'K': 11
+      }
+    else :
+      if(color == 1) :
+        return {
+        'P': 0, 'N': 1, 'B': 3, 'R': 5, 'Q': 7, 'K': 8
+        }
+      return {
+        'p': 0, 'n': 1, 'b': 3, 'r': 5, 'q': 7, 'k': 8
+      }

app.py

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+from Model_Handler import Model_handler
+from flask import Flask, request
+from flask_cors import CORS
+
+
+app = Flask(__name__)
+CORS(app)
+model = Model_handler()
+
+@app.route('/')
+def hello_world():
+   return 'Hello World'
+
+@app.route('/generateMove', methods = ["POST"])
+def generate_move() :
+   data = request.get_json()
+   return model.predict(data['state'], data['turn'])
+
+if __name__ == '__main__':
+   app.run()

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1d6b8f63730ca5d909416180bb22134db39575afc2a1b986b7da0c74fd43589d
+size 20462528

requirements.txt

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+tensorflow==2.13.0
+numpy
+python-chess
+Flask
+Flask-Cors
+requests
+gunicorn