Update app.py

app.py

@@ -4,7 +4,7 @@ import json
 import numpy as np
 import requests
 from openai import OpenAI
-import ast
+import time
 
 def call_gpt3_5(prompt, api_key):
     client = OpenAI(api_key=api_key)
@@ -12,7 +12,7 @@ def call_gpt3_5(prompt, api_key):
         response = client.chat.completions.create(
             model="gpt-3.5-turbo",
             messages=[
-                {"role": "system", "content": "You are a Python expert capable of constructing and executing a Swarm Neural Network (SNN). Return only the Python code for the SNN."},
+                {"role": "system", "content": "You are a Python expert capable of implementing specific functions for a Swarm Neural Network (SNN). Return only the Python code for the requested function."},
                 {"role": "user", "content": prompt}
             ]
         )
@@ -20,58 +20,82 @@ def call_gpt3_5(prompt, api_key):
     except Exception as e:
         return f"Error calling GPT-3.5: {str(e)}"
 
+class Agent:
+    def __init__(self, api_url):
+        self.api_url = api_url
+        self.data = None
+
+    def make_api_call(self):
+        try:
+            response = requests.get(self.api_url)
+            if response.status_code == 200:
+                self.data = response.json()
+            else:
+                self.data = {"error": f"API call failed with status code {response.status_code}"}
+        except Exception as e:
+            self.data = {"error": str(e)}
+
+class SwarmNeuralNetwork:
+    def __init__(self, api_url, num_agents, calls_per_agent, special_config):
+        self.api_url = api_url
+        self.num_agents = num_agents
+        self.calls_per_agent = calls_per_agent
+        self.special_config = special_config
+        self.agents = [Agent(api_url) for _ in range(num_agents)]
+
+    def run(self):
+        start_time = time.time()
+        for agent in self.agents:
+            for _ in range(self.calls_per_agent):
+                agent.make_api_call()
+        self.execution_time = time.time() - start_time
+
+    def process_data(self):
+        # This function will be implemented by GPT-3.5
+        pass
+
 def execute_snn(api_url, openai_api_key, num_agents, calls_per_agent, special_config):
     prompt = f"""
-    Construct a Swarm Neural Network (SNN) in Python with the following parameters:
+    Implement the process_data method for the SwarmNeuralNetwork class. The method should:
+    1. Analyze the data collected by all agents (accessible via self.agents[i].data)
+    2. Generate a summary of the collected data
+    3. Derive insights from the collective behavior
+    4. Calculate performance metrics
+    5. Return a dictionary with keys 'data_summary', 'insights', and 'performance'
+
+    Consider the following parameters:
     - API URL: {api_url}
     - Number of Agents: {num_agents}
     - Calls per Agent: {calls_per_agent}
     - Special Configuration: {special_config if special_config else 'None'}
 
-    The SNN should:
-    1. Initialize the specified number of agents
-    2. Have each agent make the specified number of API calls
-    3. Process the data retrieved from the API calls
-    4. Implement a simple collective behavior mechanism
-    5. Return a dictionary with the following keys:
-       - 'data_summary': A summary of the data retrieved
-       - 'insights': Any patterns or insights derived from the collective behavior
-       - 'performance': Performance metrics (e.g., execution time, success rate of API calls)
-
-    Provide only the Python code to implement this SNN. The code should be fully functional and ready to execute.
+    Provide only the Python code for the process_data method.
     """
     
-    snn_code = call_gpt3_5(prompt, openai_api_key)
+    process_data_code = call_gpt3_5(prompt, openai_api_key)
     
-    if not snn_code.startswith("Error"):
+    if not process_data_code.startswith("Error"):
         try:
-            # Add necessary imports to the generated code
-            full_code = f"""
-import requests
-import time
-import numpy as np
-
-{snn_code}
-
-# Execute the SNN
-snn = SwarmNeuralNetwork("{api_url}", {num_agents}, {calls_per_agent})
-result = snn.execute()
-print(result)
-            """
+            # Create the SNN instance
+            snn = SwarmNeuralNetwork(api_url, num_agents, calls_per_agent, special_config)
+            
+            # Add the process_data method to the SNN class
+            exec(process_data_code, globals())
+            SwarmNeuralNetwork.process_data = process_data
+            
+            # Run the SNN
+            snn.run()
             
-            # Execute the generated code
-            exec_globals = {}
-            exec(full_code, exec_globals)
+            # Process the data and get results
+            result = snn.process_data()
             
-            # Retrieve the result from the executed code
-            result = exec_globals.get('result', "No result returned from SNN execution.")
             return f"Results from the swarm neural network:\n\n{json.dumps(result, indent=2)}"
         except Exception as e:
-            return f"Error executing SNN code: {str(e)}\n\nGenerated code:\n{snn_code}"
+            return f"Error executing SNN: {str(e)}\n\nGenerated process_data code:\n{process_data_code}"
     else:
-        return snn_code
+        return process_data_code
 
-# Define the Gradio interface
+# Define the Gradio interface (same as before)
 iface = gr.Interface(
     fn=execute_snn,
     inputs=[