Create agent.py
Browse files
agent.py
ADDED
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import os
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import json
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import re
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import torch
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from typing import Dict, Optional
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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CACHE_FILE = "gaia_answers_cache.json"
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DEFAULT_MODEL = "google/flan-t5-base"
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class EnhancedGAIAAgent:
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"""Агент для Hugging Face GAIA с улучшенной обработкой вопросов"""
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def __init__(self, model_name=DEFAULT_MODEL, use_cache=False):
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print(f"Initializing EnhancedGAIAAgent with model: {model_name}")
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self.model_name = model_name
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self.use_cache = use_cache
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self.cache = self._load_cache() if use_cache else {}
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self.tokenizer = AutoTokenizer.from_pretrained(model_name)
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self.model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
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def _load_cache(self) -> Dict[str, str]:
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if os.path.exists(CACHE_FILE):
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try:
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with open(CACHE_FILE, 'r', encoding='utf-8') as f:
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return json.load(f)
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except:
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return {}
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return {}
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def _save_cache(self) -> None:
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try:
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with open(CACHE_FILE, 'w', encoding='utf-8') as f:
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json.dump(self.cache, f, ensure_ascii=False, indent=2)
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except:
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pass
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def _classify_question(self, question: str) -> str:
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question_lower = question.lower()
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if any(word in question_lower for word in ["calculate", "sum", "how many"]):
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return "calculation"
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elif any(word in question_lower for word in ["list", "enumerate"]):
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return "list"
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elif any(word in question_lower for word in ["date", "time", "when"]):
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return "date_time"
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return "factual"
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def _format_answer(self, raw_answer: str, question_type: str) -> str:
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answer = raw_answer.strip()
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# Удаление префиксов
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prefixes = ["Answer:", "The answer is:", "I think", "I believe"]
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for prefix in prefixes:
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if answer.lower().startswith(prefix.lower()):
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answer = answer[len(prefix):].strip()
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# Специфическое форматирование
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if question_type == "calculation":
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numbers = re.findall(r'-?\d+\.?\d*', answer)
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if numbers:
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answer = numbers[0]
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elif question_type == "list":
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if "," not in answer and " " in answer:
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items = [item.strip() for item in answer.split() if item.strip()]
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answer = ", ".join(items)
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# Финальная очистка
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answer = answer.strip('"\'')
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if answer.endswith('.') and not re.match(r'.*\d\.$', answer):
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answer = answer[:-1]
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return re.sub(r'\s+', ' ', answer).strip()
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def __call__(self, question: str, task_id: Optional[str] = None) -> str:
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cache_key = task_id if task_id else question
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if self.use_cache and cache_key in self.cache:
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return self.cache[cache_key]
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question_type = self._classify_question(question)
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try:
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# Генерация ответа
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inputs = self.tokenizer(question, return_tensors="pt")
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outputs = self.model.generate(**inputs, max_length=100)
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raw_answer = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
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# Форматирование
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formatted_answer = self._format_answer(raw_answer, question_type)
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# Формирование JSON
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result = {"final_answer": formatted_answer}
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json_response = json.dumps(result)
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if self.use_cache:
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self.cache[cache_key] = json_response
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self._save_cache()
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return json_response
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except Exception as e:
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return json.dumps({"final_answer": f"AGENT ERROR: {e}"})
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