# Configuration and constants for GAIA Agent Evaluator
import os

# --- API Configuration ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
LLAMA_API_URL = "https://api-inference.huggingface.co/models/mistralai/Mixtral-8x7B-Instruct-v0.1"
HF_API_TOKEN = os.getenv("HF_API_TOKEN")
HEADERS = {"Authorization": f"Bearer {HF_API_TOKEN}"} if HF_API_TOKEN else {}

# --- Request Configuration ---
MAX_RETRIES = 3
RETRY_DELAY = 2  # seconds

# --- Knowledge Base Content ---
GAIA_KNOWLEDGE = """
### AI and Agent Concepts
- An agent is an autonomous entity that observes and acts upon an environment using sensors and actuators, usually to achieve specific goals.
- GAIA (General AI Assistant) is a framework for creating and evaluating AI assistants that can perform a wide range of tasks.
- The agent loop consists of perception, reasoning, and action.
- RAG (Retrieval-Augmented Generation) combines retrieval of relevant information with generation capabilities of language models.
- An LLM (Large Language Model) is a neural network trained on vast amounts of text data to understand and generate human language.

### Agent Capabilities
- Tool use refers to an agent's ability to employ external tools like search engines, APIs, or specialized algorithms.
- An effective agent should be able to decompose complex problems into manageable parts.
- Chain-of-thought reasoning allows agents to break down problem-solving steps to improve accuracy.
- Agents should apply appropriate reasoning strategies based on the type of question (factual, analytical, etc.)
- Self-reflection helps agents identify and correct errors in their reasoning.

### Evaluation Criteria
- Agent responses should be accurate, relevant, and factually correct.
- Effective agents provide concise yet comprehensive answers.
- Agents should acknowledge limitations and uncertainties when appropriate.
- Good agents can follow multi-step instructions and fulfill all requirements.
- Reasoning transparency helps users understand how the agent arrived at its conclusions.
"""

# --- Tool Pattern Matching ---
YOUTUBE_PATTERNS = ["youtube.com", "youtu.be", "video", "watch?v=", "channel"]

REVERSE_TEXT_PATTERNS = ["rewsna", "ecnetnes", "sdrow"]

WIKIPEDIA_PATTERNS = [
    "wikipedia", "article", "published", "featured article", 
    "promoted", "nominated", "discography", "studio albums",
    "encyclopedia", "wiki", "featured content"
]

WEB_SEARCH_PATTERNS = [
    # Time indicators
    "current", "latest", "recent", "2023", "2022", "2021", "2020", "today",
    # Question words
    "how many", "where", "when", "who", "which", "what", "whose",
    # Sports and competitions
    "yankee", "walks", "athletes", "olympics", "competition", "pitcher", "baseball",
    # Specific entities that need web lookup
    "malko", "taishō tamai", "universe today", "nedoshivina",
    "specimens", "polish-language", "actor", "played",
    # Geographic and demographic
    "country", "nationality", "first name", "award number", "city",
    # Publications and research
    "published", "paper", "study", "research", "journal", "author",
    # Statistics and data
    "statistics", "data", "facts", "information about", "number of"
]

AI_PATTERNS = ["agent", "gaia", "llm", "ai", "evaluation", "tool", "artificial intelligence"]

FILE_PATTERNS = ["excel", "xlsx", "csv", "python", ".py", "mp3", "wav", "audio", "voice memo"]

# --- Answer Cleaning Patterns ---
ANSWER_PREFIXES_TO_REMOVE = [
    "final answer:", "answer:", "the answer is:", "result:", 
    "solution:", "conclusion:", "final answer is:", "direct answer:",
    "based on the context:", "according to:", "the result is:"
]

LLM_RESPONSE_MARKERS = ["<answer>", "<response>", "Answer:", "Response:", "Assistant:"]
LLM_END_MARKERS = ["</answer>", "</response>", "Human:", "User:"]