app/llm.py

import threading
from .config import *
from .models import *
from .utils import *
import random

def generate_response(prompt, model_name):
    """
    Replace this with actual API calls to your LLM endpoints
    """
    # Placeholder implementation
    responses = {
        "gpt4": "This is a simulated GPT-4 response",
        "claude3": "This is a simulated Claude-3 response",
        "gemini": "This is a simulated Gemini-Pro response",
        "mixtral": "This is a simulated Mixtral response",
        "llama2": "This is a simulated Llama-2 response"
    }
    return responses.get(model_name, "Model not found")

def get_responses(prompt, model_a, model_b):
    results = {}
    
    def predict_and_store(prompt, model, result_storage):
        try:
            if model in AVAILABLE_MODELS:
                result = generate_response(prompt, AVAILABLE_MODELS[model])
            else:
                result = generate_response(prompt, model)
            result_storage[model] = result
        except Exception as e:
            raise gr.Error(f'Unable to generate response: {str(e)}')
    
    thread1 = threading.Thread(target=predict_and_store, args=(prompt, model_a, results))
    thread2 = threading.Thread(target=predict_and_store, args=(prompt, model_b, results))
    
    thread1.start()
    thread2.start()
    thread1.join()
    thread2.join()
    
    return results[model_a], results[model_b]

def random_models():
    return random.sample(list(AVAILABLE_MODELS.keys()), 2)