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app.py

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+import random
+import csv
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense, Embedding
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.preprocessing.text import Tokenizer
+
+# Mapping choices to numerical values
+choices = {'rock': 0, 'paper': 1, 'scissors': 2}
+rev_choices = {0: 'rock', 1: 'paper', 2: 'scissors'}
+
+def get_computer_choice(model, past_moves):
+    if len(past_moves) < 3:
+        return random.choice(['rock', 'paper', 'scissors'])
+    
+    # Prepare input data for prediction
+    sequence = [choices[move] for move in past_moves[-3:]]
+    sequence = pad_sequences([sequence], maxlen=3)
+    prediction = model.predict(sequence, verbose=0)
+    predicted_choice = rev_choices[np.argmax(prediction)]
+    
+    # Counteract the predicted choice
+    counter_choices = {'rock': 'paper', 'paper': 'scissors', 'scissors': 'rock'}
+    return counter_choices[predicted_choice]
+
+def get_winner(player, computer):
+    if player == computer:
+        return "It's a tie!"
+    elif (player == 'rock' and computer == 'scissors') or \
+         (player == 'scissors' and computer == 'paper') or \
+         (player == 'paper' and computer == 'rock'):
+        return "You win!"
+    else:
+        return "Computer wins!"
+
+def save_move(player, computer, result):
+    with open('game_moves.csv', mode='a', newline='') as file:
+        writer = csv.writer(file)
+        writer.writerow([player, computer, result])
+
+def load_data():
+    try:
+        with open('game_moves.csv', mode='r') as file:
+            reader = csv.reader(file)
+            next(reader)  # Skip header
+            data = [row[0] for row in reader]
+            return data
+    except FileNotFoundError:
+        return []
+
+def train_lstm_model(data):
+    tokenizer = Tokenizer(num_words=3)
+    tokenizer.fit_on_texts(data)
+    sequences = tokenizer.texts_to_sequences(data)
+    X = pad_sequences(sequences, maxlen=3)
+    y = np.array([choices[move] for move in data[1:]])
+    
+    model = Sequential([
+        Embedding(input_dim=3, output_dim=10, input_length=3),
+        LSTM(20, return_sequences=False),
+        Dense(3, activation='softmax')
+    ])
+    model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+    
+    if len(X) > 1:
+        model.fit(X[:-1], y, epochs=10, verbose=0)
+    return model
+
+def main():
+    print("Welcome to Rock, Paper, Scissors!")
+    with open('game_moves.csv', mode='w', newline='') as file:
+        writer = csv.writer(file)
+        writer.writerow(["Player Choice", "Computer Choice", "Result"])
+    
+    past_moves = load_data()
+    model = train_lstm_model(past_moves)
+    
+    while True:
+        player_choice = input("Enter rock, paper, or scissors (or 'quit' to exit): ").lower()
+        if player_choice == 'quit':
+            print("Thanks for playing!")
+            break
+        if player_choice not in ['rock', 'paper', 'scissors']:
+            print("Invalid choice. Please try again.")
+            continue
+        
+        computer_choice = get_computer_choice(model, past_moves)
+        result = get_winner(player_choice, computer_choice)
+        print(f"Computer chose: {computer_choice}")
+        print(result)
+        print()
+        
+        save_move(player_choice, computer_choice, result)
+        past_moves.append(player_choice)
+
+if __name__ == "__main__":
+    main()

game_moves.csv

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+Player Choice,Computer Choice,Result
+rock,paper,Computer wins!
+paper,scissors,Computer wins!
+scissors,rock,Computer wins!