Refactor bot server setup to use Waitress for production; fallback to Flask dev server for local development. Added timeout to HTTP requests in Fun and Test cogs. Improved error handling for missing environment variables. Enhanced secret key management in Flask app. Added request timeout configuration. Introduced new experimental features including user profile and balance cards, and a Tic-Tac-Toe game with Minimax AI. Addressed various database and security issues, and improved code quality across multiple files.

Experimental/tic_tac_toe/Minimax/minimax.py

@@ -0,0 +1,93 @@
+import random
+
+win_states = [
+    (0, 1, 2), (3, 4, 5), (6, 7, 8),  # horizontal
+    (0, 3, 6), (1, 4, 7), (2, 5, 8),  # vertical
+    (0, 4, 8), (2, 4, 6)  # diagonal
+]
+
+
+def Terminal(state: list):
+    for win_state in win_states:
+        if all(state[i] == "O" for i in win_state) or all(state[i] == "X" for i in win_state):
+            return True
+    if state.count(" ") == 0:
+        return True
+    else:
+        return False
+
+
+def Value(state):
+    for win_state in win_states:
+        if all(state[i] == "O" for i in win_state):
+            return -1
+        elif all(state[i] == "X" for i in win_state):
+            return 1
+    if state.count(" ") == 0:
+        return 0
+
+
+def Player(state: list):
+    if state.count("X") == state.count("O"):
+        return "X"
+    else:
+        return "O"
+
+
+def Actions(state):
+    indexes = []
+    for i in range(9):
+        if state[i] == " ":
+            indexes.append(i)
+    return indexes
+
+
+def Result(state, index, player):
+    state[index] = player
+    return state
+
+
+def Minimax(state):
+    if Terminal(state):
+        return Value(state)
+    if Player(state) == "X":
+        value = -9999
+        for a in Actions(state):
+            value = max(value, Minimax(Result(state.copy(), a, "X")))
+        return value
+
+    elif Player(state) == "O":
+        value = 9999
+        for a in Actions(state):
+            value = min(value, Minimax(Result(state.copy(), a, "O")))
+        return value
+
+
+def aiO(state):
+    best_score = float('inf')
+    best_move = None
+    for move in Actions(state):
+        state[move] = 'O'
+        score = Minimax(state)
+        state[move] = ' '
+        if score < best_score:
+            best_score = score
+            best_move = move
+    return best_move
+
+
+def aiX(state):
+    best_score = float('-inf')
+    best_move = None
+    for move in Actions(state):
+        state[move] = 'X'
+        score = Minimax(state)
+        state[move] = ' '
+        if score > best_score:
+            best_score = score
+            best_move = move
+    return best_move
+
+
+def aiRandom(state):
+    return random.choice(Actions(state))