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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from llama_cpp import Llama
from concurrent.futures import ThreadPoolExecutor, as_completed
import uvicorn
from dotenv import load_dotenv
from difflib import SequenceMatcher
from tqdm import tqdm
load_dotenv()
app = FastAPI()
# Configuración de los modelos
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"},
]
# Cargar modelos en RAM solo una vez
llms = [Llama.from_pretrained(repo_id=model['repo_id'], filename=model['filename']) for model in models]
print(f"Modelos cargados en RAM: {[model['repo_id'] for model in models]}")
class ChatRequest(BaseModel):
message: str
top_k: int = 50
top_p: float = 0.95
temperature: float = 0.7
def generate_chat_response(request, llm):
try:
user_input = normalize_input(request.message)
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']
return {"response": reply, "literal": user_input}
except Exception as e:
return {"response": f"Error: {str(e)}", "literal": user_input}
def normalize_input(input_text):
return input_text.strip()
def select_best_response(responses):
# Deduplicar respuestas
unique_responses = list(set(responses))
# Filtrar respuestas coherentes
coherent_responses = filter_by_coherence(unique_responses)
# Seleccionar la mejor respuesta
best_response = filter_by_similarity(coherent_responses)
return best_response
def filter_by_coherence(responses):
# Implementa aquí un filtro de coherencia si es necesario
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):
if not request.message.strip():
raise HTTPException(status_code=400, detail="The message cannot be empty.")
print(f"Procesando solicitud: {request.message}")
# Utilizar un ThreadPoolExecutor para procesar los modelos en paralelo
with ThreadPoolExecutor() as executor:
futures = [executor.submit(generate_chat_response, request, llm) for llm in llms]
responses = []
for future in tqdm(as_completed(futures), total=len(futures), desc="Generando respuestas"):
response = future.result()
responses.append(response)
print(f"Modelo procesado: {response['literal'][:30]}...")
# Extraer respuestas de los diccionarios
response_texts = [resp['response'] for resp in responses]
# Verificar si hay errores en las respuestas
error_responses = [resp for resp in responses if "Error" in resp['response']]
if error_responses:
error_response = error_responses[0]
raise HTTPException(status_code=500, detail=error_response['response'])
# Seleccionar la mejor respuesta
best_response = select_best_response(response_texts)
print(f"Mejor respuesta seleccionada: {best_response}")
return {
"best_response": best_response,
"all_responses": response_texts
}
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860)
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