app.py

import gradio as gr
import numpy as np
import logging
from tqdm import tqdm

class SwarmAgent:
    def __init__(self, position, velocity):
        self.position = position
        self.velocity = velocity
        self.m = np.zeros_like(position)
        self.v = np.zeros_like(position)

class SwarmNeuralNetwork:
    def __init__(self, num_agents, param_shape, target_response, optimization_function):
        self.param_shape = param_shape
        self.agents = [SwarmAgent(self.random_position(), self.random_velocity()) for _ in range(num_agents)]
        self.target_response = target_response
        self.optimization_function = optimization_function
        self.current_epoch = 0
        self.noise_schedule = np.linspace(0.1, 0.002, 1000)  # Noise schedule

    def random_position(self):
        return np.random.randn(*self.param_shape)  # Use Gaussian noise

    def random_velocity(self):
        return np.random.randn(*self.param_shape) * 0.01

    def evaluate_function(self, params):
        # Evaluate the optimization function
        response = self.optimization_function(params)
        return response

    def update_agents(self, timestep):
        noise_level = self.noise_schedule[min(timestep, len(self.noise_schedule) - 1)]

        for agent in self.agents:
            # Predict noise
            predicted_noise = agent.position - self.target_response
            
            # Denoise
            denoised = (agent.position - noise_level * predicted_noise) / (1 - noise_level)
            
            # Add scaled noise for next step
            agent.position = denoised + np.random.randn(*self.param_shape) * np.sqrt(noise_level)
            
            # Clip values
            agent.position = np.clip(agent.position, -1, 1)

    def train(self, epochs):
        logging.basicConfig(filename='training.log', level=logging.INFO)

        for epoch in tqdm(range(epochs), desc="Training Epochs"):
            self.update_agents(epoch)

            # Evaluate performance
            responses = [self.evaluate_function(agent.position) for agent in self.agents]
            mse = np.mean((np.array(responses) - self.target_response)**2)
            logging.info(f"Epoch {epoch}, MSE: {mse}")

            if epoch % 10 == 0:
                print(f"Epoch {epoch}, MSE: {mse}")
            self.current_epoch += 1

    def save_model(self, filename):
        model_state = {
            'agents': self.agents,
            'current_epoch': self.current_epoch
        }
        np.save(filename, model_state)

    def load_model(self, filename):
        model_state = np.load(filename, allow_pickle=True).item()
        self.agents = model_state['agents']
        self.current_epoch = model_state['current_epoch']

    def generate_new_parameters(self, num_steps=1000):
        for agent in self.agents:
            agent.position = np.random.randn(*self.param_shape)
        
        for step in tqdm(range(num_steps), desc="Generating Parameters"):
            self.update_agents(num_steps - step - 1)  # Reverse order
        
        best_agent = min(self.agents, key=lambda agent: np.mean((self.evaluate_function(agent.position) - self.target_response)**2))
        return best_agent.position

# Optimization function
def optimization_function(params):
    return np.sum((params - 3)**2)  # Example: simple quadratic function

# Gradio Interface
def train_snn(target_response, num_agents, epochs):
    param_shape = (10,)  # Example parameter shape
    snn = SwarmNeuralNetwork(num_agents=num_agents, param_shape=param_shape, target_response=target_response, optimization_function=optimization_function)
    snn.train(epochs=epochs)
    snn.save_model('snn_model.npy')
    return snn.generate_new_parameters()

def generate_new_parameters():
    param_shape = (10,)  # Example parameter shape
    snn = SwarmNeuralNetwork(num_agents=2000, param_shape=param_shape, target_response=None, optimization_function=optimization_function)
    snn.load_model('snn_model.npy')
    new_params = snn.generate_new_parameters()
    return new_params

interface = gr.Interface(
    fn=train_snn,
    inputs=[
        gr.Number(label="Target Response"),
        gr.Slider(minimum=500, maximum=3000, value=2000, label="Number of Agents"),
        gr.Slider(minimum=10, maximum=200, value=100, label="Number of Epochs")
    ],
    outputs=gr.Textbox(label="Generated Parameters"),
    title="Swarm Neural Network Function Optimization",
    description="Set the target response, number of agents, and epochs to train the Swarm Neural Network to generate optimized function parameters."
)

interface.launch()