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| import gradio as gr | |
| import os | |
| import json | |
| import numpy as np | |
| import requests | |
| from openai import OpenAI | |
| import time | |
| def call_gpt3_5(prompt, api_key): | |
| client = OpenAI(api_key=api_key) | |
| try: | |
| response = client.chat.completions.create( | |
| model="gpt-3.5-turbo", | |
| messages=[ | |
| {"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, without any additional text."}, | |
| {"role": "user", "content": prompt} | |
| ] | |
| ) | |
| code = response.choices[0].message.content | |
| # Clean up the code: remove leading/trailing whitespace and any markdown code blocks | |
| code = code.strip() | |
| if code.startswith("```python"): | |
| code = code[10:] | |
| if code.endswith("```"): | |
| code = code[:-3] | |
| return code.strip() | |
| 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 | |
| self.processing_time = 0 | |
| def make_api_call(self): | |
| try: | |
| start_time = time.time() | |
| 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}"} | |
| self.processing_time = time.time() - start_time | |
| except Exception as e: | |
| self.data = {"error": str(e)} | |
| self.processing_time = time.time() - start_time | |
| 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)] | |
| self.execution_time = 0 | |
| 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""" | |
| 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'} | |
| Provide only the Python code for the process_data method, without any additional text or markdown formatting. | |
| """ | |
| process_data_code = call_gpt3_5(prompt, openai_api_key) | |
| if not process_data_code.startswith("Error"): | |
| try: | |
| # 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() | |
| # Process the data and get results | |
| result = snn.process_data() | |
| return f"Results from the swarm neural network:\n\n{json.dumps(result, indent=2)}" | |
| except Exception as e: | |
| return f"Error executing SNN: {str(e)}\n\nGenerated process_data code:\n{process_data_code}" | |
| else: | |
| return process_data_code | |
| # Define the Gradio interface | |
| iface = gr.Interface( | |
| fn=execute_snn, | |
| inputs=[ | |
| gr.Textbox(label="API URL for your task"), | |
| gr.Textbox(label="OpenAI API Key", type="password"), | |
| gr.Number(label="Number of Agents", minimum=1, maximum=100, step=1), | |
| gr.Number(label="Calls per Agent", minimum=1, maximum=100, step=1), | |
| gr.Textbox(label="Special Configuration (optional)") | |
| ], | |
| outputs="text", | |
| title="Swarm Neural Network Simulator", | |
| 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"] | |
| ] | |
| ) | |
| # Launch the interface | |
| iface.launch() |