Spaces:
Runtime error
Runtime error
File size: 4,169 Bytes
b212c94 53eee33 b212c94 18000a9 b212c94 53eee33 b212c94 36c9f0a 71df925 18000a9 b212c94 18000a9 36c9f0a b212c94 e038371 b212c94 e038371 b212c94 36c9f0a 18000a9 36c9f0a b212c94 18000a9 b212c94 53eee33 b212c94 36c9f0a 18000a9 53eee33 18000a9 b212c94 53eee33 18000a9 53eee33 18000a9 b212c94 18000a9 e038371 b212c94 18000a9 e038371 18000a9 e038371 18000a9 e038371 b212c94 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 |
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 # Importa tqdm para la barra de progreso
load_dotenv()
app = FastAPI()
# Configuraci贸n 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 memoria solo una vez
llms = [Llama.from_pretrained(repo_id=model['repo_id'], filename=model['filename']) for model in models]
print(f"Modelos cargados: {[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:
# Normalizaci贸n del mensaje para manejo robusto
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):
# Implementar aqu铆 cualquier l贸gica de normalizaci贸n que sea necesaria
return input_text.strip()
def select_best_response(responses, request):
coherent_responses = filter_by_coherence([resp['response'] for resp in responses], request)
best_response = filter_by_similarity(coherent_responses)
return best_response
def filter_by_coherence(responses, request):
# Implementa aqu铆 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}")
# Crear un ThreadPoolExecutor para ejecutar las tareas en paralelo
with ThreadPoolExecutor(max_workers=None) as executor:
# Usar tqdm para mostrar la barra de progreso
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]}...") # Muestra los primeros 30 caracteres de la respuesta
# Verificar si hay errores en las respuestas
if any("Error" in response['response'] for response in responses):
error_response = next(response for response in responses if "Error" in response['response'])
raise HTTPException(status_code=500, detail=error_response['response'])
best_response = select_best_response([resp['response'] for resp in responses], request)
print(f"Mejor respuesta seleccionada: {best_response}")
return {
"best_response": best_response,
"all_responses": [resp['response'] for resp in responses],
"literal_inputs": [resp['literal'] for resp in responses]
}
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860)
|