Update app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 import numpy as np
 import logging
+import requests
 from tqdm import tqdm
 
 class SwarmAgent:
@@ -11,10 +12,11 @@ class SwarmAgent:
         self.v = np.zeros_like(position)
 
 class SwarmNeuralNetwork:
-    def __init__(self, num_agents, param_shape, target_response):
+    def __init__(self, num_agents, param_shape, target_response, api_endpoint):
         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.current_epoch = 0
         self.noise_schedule = np.linspace(0.1, 0.002, 1000)  # Noise schedule
 
@@ -25,11 +27,14 @@ class SwarmNeuralNetwork:
         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
+        # 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 update_agents(self, timestep):
         noise_level = self.noise_schedule[min(timestep, len(self.noise_schedule) - 1)]
@@ -85,16 +90,16 @@ class SwarmNeuralNetwork:
         return best_agent.position
 
 # Gradio Interface
-def train_snn(target_response, num_agents, epochs):
+def train_snn(api_endpoint, 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 = SwarmNeuralNetwork(num_agents=num_agents, param_shape=param_shape, target_response=target_response, api_endpoint=api_endpoint)
     snn.train(epochs=epochs)
     snn.save_model('snn_model.npy')
     return snn.generate_new_parameters()
 
-def generate_new_parameters():
+def generate_new_parameters(api_endpoint):
     param_shape = (10,)  # Example parameter shape
-    snn = SwarmNeuralNetwork(num_agents=2000, param_shape=param_shape, target_response=None)
+    snn = SwarmNeuralNetwork(num_agents=2000, param_shape=param_shape, target_response=None, api_endpoint=api_endpoint)
     snn.load_model('snn_model.npy')
     new_params = snn.generate_new_parameters()
     return new_params
@@ -102,13 +107,14 @@ def generate_new_parameters():
 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 target response and the number of agents and epochs to train the Swarm Neural Network to generate optimized API parameters."
+    description="Set the API endpoint, target response, number of agents, and epochs to train the Swarm Neural Network to generate optimized API parameters."
 )
 
 interface.launch()