app.py

app.py

@@ -1,67 +0,0 @@
-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.")