import openai, sys, threading, time, json, logging, random, os, queue, traceback
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
openai.api_key = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY")
def ai_agent(prompt, temperature=0.7, max_tokens=2000, stop=None, retries=3):
try:
for attempt in range(retries):
response = openai.Completion.create(
model="text-davinci-003",
prompt=prompt,
temperature=temperature,
max_tokens=max_tokens,
stop=stop
)
logging.info(f"Agent Response: {response}")
return response["choices"][0]["text"].strip()
except Exception as e:
logging.error(f"Error occurred on attempt {attempt + 1}: {e}")
traceback.print_exc()
time.sleep(random.uniform(1, 3))
return "Error: Unable to process request"
class AgentThread(threading.Thread):
def __init__(self, prompt, temperature=0.7, max_tokens=1500, output_queue=None):
super().__init__()
self.prompt = prompt
self.temperature = temperature
self.max_tokens = max_tokens
self.output_queue = output_queue if output_queue else queue.Queue()
def run(self):
try:
result = ai_agent(self.prompt, self.temperature, self.max_tokens)
self.output_queue.put({"prompt": self.prompt, "response": result})
except Exception as e:
logging.error(f"Thread error for prompt '{self.prompt}': {e}")
self.output_queue.put({"prompt": self.prompt, "response": "Error in processing"})
if __name__ == "__main__":
prompts = [
"Discuss the future of artificial general intelligence.",
"What are the potential risks of autonomous weapons?",
"Explain the ethical implications of AI in surveillance systems.",
"How will AI affect global economies in the next 20 years?",
"What is the role of AI in combating climate change?"
]
threads = []
results = []
output_queue = queue.Queue()
start_time = time.time()
for idx, prompt in enumerate(prompts):
temperature = random.uniform(0.5, 1.0)
max_tokens = random.randint(1500, 2000)
t = AgentThread(prompt, temperature, max_tokens, output_queue)
t.start()
threads.append(t)
for t in threads:
t.join()
while not output_queue.empty():
result = output_queue.get()
results.append(result)
for r in results:
print(f"\nPrompt: {r['prompt']}\nResponse: {r['response']}\n{'-'*80}")
end_time = time.time()
total_time = round(end_time - start_time, 2)
logging.info(f"All tasks completed in {total_time} seconds.")
logging.info(f"Final Results: {json.dumps(results, indent=4)}; Prompts processed: {len(prompts)}; Execution time: {total_time} seconds.")
