Update app.py

app.py

@@ -4,6 +4,7 @@ import json
 import numpy as np
 import requests
 from openai import OpenAI
+import ast
 
 def call_gpt3_5(prompt, api_key):
     client = OpenAI(api_key=api_key)
@@ -11,7 +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 helpful assistant capable of constructing and executing a Swarm Neural Network (SNN). Return only the formatted results of the SNN execution."},
+                {"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": "user", "content": prompt}
             ]
         )
@@ -21,26 +22,54 @@ def call_gpt3_5(prompt, api_key):
 
 def execute_snn(api_url, openai_api_key, num_agents, calls_per_agent, special_config):
     prompt = f"""
-    Construct and execute a Swarm Neural Network (SNN) with the following parameters:
+    Construct a Swarm Neural Network (SNN) in Python with 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'}
 
-    Simulate the execution of this SNN and provide only the formatted results. The results should include:
-    1. A summary of the data retrieved from the API calls
-    2. Any patterns or insights derived from the collective behavior of the agents
-    3. Performance metrics of the SNN (e.g., execution time, success rate of API calls)
+    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)
 
-    Present the results in a clear, structured format without any additional explanations or descriptions of the SNN process.
+    Provide only the Python code to implement this SNN. The code should be fully functional and ready to execute.
     """
     
-    gpt_response = call_gpt3_5(prompt, openai_api_key)
+    snn_code = call_gpt3_5(prompt, openai_api_key)
     
-    if gpt_response:
-        return f"Results from the swarm neural network:\n\n{gpt_response}"
+    if not snn_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)
+            """
+            
+            # Execute the generated code
+            exec_globals = {}
+            exec(full_code, exec_globals)
+            
+            # 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}"
     else:
-        return "Failed to execute SNN due to GPT-3.5 API call failure."
+        return snn_code
 
 # Define the Gradio interface
 iface = gr.Interface(
@@ -54,7 +83,7 @@ iface = gr.Interface(
     ],
     outputs="text",
     title="Swarm Neural Network Simulator",
-    description="Enter the parameters for your Swarm Neural Network (SNN) simulation.",
+    description="Enter the parameters for your Swarm Neural Network (SNN) simulation. The SNN will be constructed and executed based on your inputs.",
     examples=[
         ["https://meowfacts.herokuapp.com/", "your-api-key-here", 3, 1, ""],
         ["https://api.publicapis.org/entries", "your-api-key-here", 5, 2, "category=Animals"]