Update app.py

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 import numpy as np
 import logging
-import requests
 from tqdm import tqdm
 
 class SwarmAgent:
@@ -12,11 +11,11 @@ class SwarmAgent:
         self.v = np.zeros_like(position)
 
 class SwarmNeuralNetwork:
-    def __init__(self, num_agents, param_shape, target_response, api_endpoint):
+    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.api_endpoint = api_endpoint
+        self.optimization_function = optimization_function
         self.current_epoch = 0
         self.noise_schedule = np.linspace(0.1, 0.002, 1000)  # Noise schedule
 
@@ -26,15 +25,10 @@ class SwarmNeuralNetwork:
     def random_velocity(self):
         return np.random.randn(*self.param_shape) * 0.01
 
-    def call_api(self, params):
-        # Make an API call with parameters
-        # In a real-world scenario, params would be converted into a proper API request format
-        try:
-            response = requests.get(self.api_endpoint)
-            response_value = len(response.text)  # Example: use length of response text as a metric
-        except Exception as e:
-            response_value = 0  # Fallback if API call fails
-        return response_value
+    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)]
@@ -59,7 +53,7 @@ class SwarmNeuralNetwork:
             self.update_agents(epoch)
 
             # Evaluate performance
-            responses = [self.call_api(agent.position) for agent in self.agents]
+            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}")
 
@@ -86,20 +80,24 @@ class SwarmNeuralNetwork:
         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))
+        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(api_endpoint, target_response, num_agents, epochs):
+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, api_endpoint=api_endpoint)
+    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(api_endpoint):
+def generate_new_parameters():
     param_shape = (10,)  # Example parameter shape
-    snn = SwarmNeuralNetwork(num_agents=2000, param_shape=param_shape, target_response=None, api_endpoint=api_endpoint)
+    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
@@ -107,14 +105,13 @@ def generate_new_parameters(api_endpoint):
 interface = gr.Interface(
     fn=train_snn,
     inputs=[
-        gr.Textbox(label="API Endpoint", value="https://meowfacts.herokuapp.com/"),
         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 API endpoint, target response, number of agents, and epochs to train the Swarm Neural Network to generate optimized API 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()