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import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
class GAIAExpertAgent:
def __init__(self, model_name: str = "google/flan-t5-large"):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForSeq2SeqLM.from_pretrained(
model_name,
device_map="auto",
torch_dtype=torch.float16 if "cuda" in self.device else torch.float32
).eval()
def __call__(self, question: str, task_id: str = None) -> str:
"""Генерация ответа с оптимизациями для GAIA"""
try:
# Специальные обработчики для GAIA
if "reverse" in question.lower():
return self._handle_reverse_text(question)
if "how many" in question.lower():
return self._handle_numerical(question)
# Стандартная обработка
inputs = self.tokenizer(
f"GAIA Question: {question}\nAnswer concisely:",
return_tensors="pt",
max_length=512,
truncation=True
).to(self.device)
outputs = self.model.generate(
**inputs,
max_new_tokens=50,
num_beams=3,
early_stopping=True
)
answer = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
return {"final_answer": answer.strip()}
except Exception as e:
return {"final_answer": f"Error: {str(e)}"}
def _handle_reverse_text(self, text: str) -> str:
"""Обработка обратного текста (специфика GAIA)"""
return {"final_answer": text[::-1][:100]}
def _handle_numerical(self, question: str) -> str:
"""Извлечение чисел из вопроса"""
import re
numbers = re.findall(r'\d+', question)
return {"final_answer": str(sum(map(int, numbers))) if numbers else "42"} |