from fastapi import FastAPI, HTTPException from pydantic import BaseModel from llama_cpp import Llama from multiprocessing import Process, Queue import uvicorn from dotenv import load_dotenv from difflib import SequenceMatcher load_dotenv() app = FastAPI() models = [ {"repo_id": "Ffftdtd5dtft/gpt2-xl-Q2_K-GGUF", "filename": "gpt2-xl-q2_k.gguf"}, {"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-8B-Instruct-Q2_K-GGUF", "filename": "meta-llama-3.1-8b-instruct-q2_k.gguf"}, {"repo_id": "Ffftdtd5dtft/gemma-2-9b-it-Q2_K-GGUF", "filename": "gemma-2-9b-it-q2_k.gguf"}, {"repo_id": "Ffftdtd5dtft/gemma-2-27b-Q2_K-GGUF", "filename": "gemma-2-27b-q2_k.gguf"}, ] llms = [] for model in models: llm = Llama.from_pretrained(repo_id=model['repo_id'], filename=model['filename']) llms.append(llm) class ChatRequest(BaseModel): message: str top_k: int = 50 top_p: float = 0.95 temperature: float = 0.7 def generate_chat_response(request, queue): try: user_input = request.message responses = [] for llm in llms: response = llm.create_chat_completion( messages=[{"role": "user", "content": user_input}], top_k=request.top_k, top_p=request.top_p, temperature=request.temperature ) reply = response['choices'][0]['message']['content'] responses.append(reply) best_response = select_best_response(responses, request) queue.put(best_response) except Exception as e: queue.put(f"Error: {str(e)}") def select_best_response(responses, request): coherent_responses = filter_by_coherence(responses, request) best_response = filter_by_similarity(coherent_responses) return best_response def filter_by_coherence(responses, request): return responses def filter_by_similarity(responses): responses.sort(key=len, reverse=True) best_response = responses[0] for i in range(1, len(responses)): ratio = SequenceMatcher(None, best_response, responses[i]).ratio() if ratio < 0.9: best_response = responses[i] break return best_response @app.post("/generate_chat") async def generate_chat(request: ChatRequest): queue = Queue() p = Process(target=generate_chat_response, args=(request, queue)) p.start() p.join() response = queue.get() if "Error" in response: raise HTTPException(status_code=500, detail=response) return {"response": response} if __name__ == "__main__": uvicorn.run(app, host="0.0.0.0", port=8001)