Create app.py

app.py

@@ -0,0 +1,114 @@
+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):
+        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.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 call_api(self, params):
+        # Placeholder for API call logic
+        # Simulate API call response based on parameters
+        # In real scenario, this would involve an actual API call
+        response = np.sum(params)  # Simulate response as sum of parameters
+        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.call_api(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.call_api(agent.position) - self.target_response)**2))
+        return best_agent.position
+
+# 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)
+    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)
+    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 API Parameter Optimization",
+    description="Set the target response and the number of agents and epochs to train the Swarm Neural Network to generate optimized API parameters."
+)
+
+interface.launch()