main.py

import os
import sys
import numpy as np
import gym
from gym import spaces
from transformers import pipeline
import jax
import jax.numpy as jnp
from collections import deque
import random

# Add the project root directory to the Python path
project_root = os.path.dirname(os.path.abspath(__file__))
sys.path.append(project_root)

# Import project modules (to be implemented)
from modules.data_collection import DataCollector
from modules.image_recognition import ImageRecognizer
from modules.model_training import ModelTrainerWrapper
from modules.object_detection import ObjectDetector
from modules.threat_assessment import ThreatAssessor
from modules.response_generation import ResponseGenerator

# Define constants for the environment
N_ACTIONS = 4  # Example: move left, right, up, down
HEIGHT, WIDTH, CHANNELS = 84, 84, 3  # Example image dimensions

# Define hyperparameters
BATCH_SIZE = 32
REPLAY_BUFFER_SIZE = 10000
GAMMA = 0.99
EPSILON_START = 1.0
EPSILON_END = 0.01
EPSILON_DECAY = 0.995
TARGET_UPDATE_FREQUENCY = 100

class BacterialThreatEnv(gym.Env):
    def __init__(self, components):
        super(BacterialThreatEnv, self).__init__()
        self.action_space = spaces.Discrete(N_ACTIONS)
        self.observation_space = spaces.Box(low=0, high=255, shape=(HEIGHT, WIDTH, CHANNELS), dtype=np.uint8)
        self.components = components

    def step(self, action):
        # Execute one time step within the environment
        # This is a placeholder implementation
        observation = self.components['data_collector'].collect_data()
        processed_image = self.components['image_recognizer'].process_image(observation)
        detected_objects = self.components['object_detector'].detect_objects(processed_image)
        threat_level = self.components['threat_assessor'].assess_threat(detected_objects)
        response = self.components['response_generator'].generate_response(threat_level)

        reward = self._calculate_reward(threat_level, response)
        done = threat_level == 0  # Example: episode ends when no threat is detected

        return observation, reward, done, {}

    def reset(self):
        # Reset the state of the environment to an initial state
        return self.components['data_collector'].collect_data()

    def render(self, mode='human', close=False):
        # Render the environment to the screen (not implemented for this example)
        pass

    def _calculate_reward(self, threat_level, response):
        # Placeholder reward function
        return -threat_level + (10 if response == 'correct_response' else 0)

def dqn_learning(env, num_episodes):
    input_shape = env.observation_space.shape
    n_actions = env.action_space.n

    model = ModelTrainerWrapper(input_shape=input_shape, n_actions=n_actions)

    replay_buffer = deque(maxlen=REPLAY_BUFFER_SIZE)
    epsilon = EPSILON_START

    for episode in range(num_episodes):
        state = env.reset()
        done = False
        total_reward = 0

        while not done:
            if np.random.random() > epsilon:
                state = jnp.array(state, dtype=jnp.float32)
                q_values = model.predict(state)
                action = int(q_values[0])
            else:
                action = env.action_space.sample()

            next_state, reward, done, _ = env.step(action)
            replay_buffer.append((state, action, reward, next_state, done))
            state = next_state
            total_reward += reward

            if len(replay_buffer) > BATCH_SIZE:
                batch = random.sample(replay_buffer, BATCH_SIZE)
                loss = model.update(*zip(*batch))

            if episode % TARGET_UPDATE_FREQUENCY == 0:
                model.update_target()

        epsilon = max(EPSILON_END, epsilon * EPSILON_DECAY)
        print(f"Episode {episode}, Total Reward: {total_reward}, Epsilon: {epsilon:.2f}")

    return model

def main():
    print("PikasuBirdAi: AI-powered detection and targeting system")
    print("Initializing components...")

    components = {
        'data_collector': DataCollector(),
        'image_recognizer': ImageRecognizer(),
        'object_detector': ObjectDetector(),
        'threat_assessor': ThreatAssessor(),
        'response_generator': ResponseGenerator()
    }

    env = BacterialThreatEnv(components)

    # Add the model_trainer after creating the environment
    components['model_trainer'] = ModelTrainerWrapper(input_shape=env.observation_space.shape, n_actions=env.action_space.n)

    print("Training DQN agent...")
    trained_model = dqn_learning(env, num_episodes=1000)

    print("System initialized and trained. Ready for threat detection and response.")

    # Example of using the trained agent
    state = env.reset()
    done = False
    total_reward = 0
    while not done:
        state = jnp.array(state, dtype=jnp.float32)
        q_values = trained_model.predict(state)
        action = int(q_values[0].argmax())
        state, reward, done, _ = env.step(action)
        total_reward += reward
        print(f"Action taken: {action}, Reward: {reward}")

    print(f"Episode finished. Total reward: {total_reward}")

if __name__ == "__main__":
    main()