app.py

import random
import numpy as np
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from sklearn.preprocessing import LabelEncoder

# Initialize the model and tokenizer
model_name = "xlm-roberta-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)

# Define action space: [0 = Incorrect, 1 = Correct]
action_space = [0, 1]

# Define environment (simplified): classify text
text_data = [
    ("Water freezes at 0°C.", 1),  # Correct
    ("The sun rises in the west.", 0),  # Incorrect
    ("Dogs can fly in the sky.", 0),  # Incorrect
    ("Birds lay eggs.", 1),  # Correct
    ("The Earth is flat.", 0),  # Incorrect
]

# Q-learning parameters
alpha = 0.1  # Learning rate
gamma = 0.9  # Discount factor
epsilon = 0.1  # Exploration rate
n_episodes = 1000  # Number of episodes for training
q_table = np.zeros((len(text_data), len(action_space)))  # Q-table (state x action)

# Reward system (simplified)
def get_reward(text, predicted_label):
    correct_label = next((label for t, label in text_data if t == text), None)
    if predicted_label == correct_label:
        return 1  # Reward for correct classification
    return -1  # Penalty for incorrect classification

# Q-learning training loop
for episode in range(n_episodes):
    state = random.choice(text_data)  # Random initial text input
    state_idx = text_data.index(state)

    # Exploration vs Exploitation
    if random.uniform(0, 1) < epsilon:
        action = random.choice(action_space)  # Exploration: random action
    else:
        action = np.argmax(q_table[state_idx])  # Exploitation: choose best action based on Q-table

    # Make a prediction based on action
    inputs = tokenizer(state[0], return_tensors="pt")
    with torch.no_grad():
        output = model(**inputs)
    predicted_label = torch.argmax(output.logits, dim=1).item()

    # Get reward from environment
    reward = get_reward(state[0], predicted_label)

    # Update Q-table using Q-learning update rule
    next_state_idx = random.choice(range(len(text_data)))  # Random next state (simplified)
    best_next_action = np.argmax(q_table[next_state_idx])
    q_table[state_idx, action] = (1 - alpha) * q_table[state_idx, action] + alpha * (reward + gamma * q_table[next_state_idx, best_next_action])

    if episode % 100 == 0:
        print(f"Episode {episode + 1}/{n_episodes}, Reward: {reward}")

# After training, the model should have learned which actions (labels) to take
print("Training complete.")