Delete agent.py

agent.py

@@ -1,63 +0,0 @@
-# Файл: agent_gaia.py
-import json
-import re
-import torch
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-from typing import Optional
-
-class GAIAExpertAgent:
-    """Специализированный агент для GAIA тестов"""
-    
-    def __init__(self):
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        print(f"⚡ Using device: {self.device.upper()}")
-        
-        # Оптимальная модель для GAIA вопросов
-        self.model_name = "google/flan-t5-large"
-        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
-        self.model = AutoModelForSeq2SeqLM.from_pretrained(
-            self.model_name,
-            device_map="auto",
-            torch_dtype=torch.float16 if "cuda" in self.device else torch.float32
-        ).eval()
-    
-    def solve_gaia_question(self, question: str) -> str:
-        """Специализированный решатель для GAIA вопросов"""
-        # Особые случаи
-        if "dnatsrednu uoy fI" in question:  # Обратный текст
-            return "right"
-        
-        if "how many" in question.lower():
-            return re.search(r'\d+', question) or "42"
-        
-        if "list" in question.lower():
-            return "A, B, C, D"
-        
-        # Общий промпт для GAIA
-        prompt = f"""
-        You are a GAIA test expert. Answer concisely and factually.
-        Question: {question}
-        Answer in 1-3 words ONLY:
-        """
-        
-        inputs = self.tokenizer(prompt, return_tensors="pt", max_length=512, truncation=True).to(self.device)
-        outputs = self.model.generate(
-            **inputs,
-            max_new_tokens=30,
-            num_beams=3,
-            temperature=0.3
-        )
-        
-        answer = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-        
-        # Постобработка
-        answer = answer.split(":")[-1].strip()
-        answer = re.sub(r'[^a-zA-Z0-9\s.,]', '', answer)
-        return answer[:100]  # Обрезка слишком длинных ответов
-
-    def __call__(self, question: str, task_id: Optional[str] = None) -> str:
-        try:
-            answer = self.solve_gaia_question(question)
-            return json.dumps({"final_answer": answer})
-        except Exception as e:
-            return json.dumps({"final_answer": "ERROR"})