tools.py

from contextlib import redirect_stderr, redirect_stdout
import io
import json
import os
import re
import subprocess
import traceback
from typing import Dict, List, Literal, Optional

import google.generativeai as genai

import cv2
import pandas as pd
from pydantic import BaseModel
import requests
from audio_util import Audio_Util
from constantes import *
from file_util import File_Util
from image_util import Image_Util

from tavily import TavilyClient

from web_util import Web_Util
from wikipedia_util import Wikipedia_Historical_Page, Wikipedia_Util


class Video_Util:
    def download_video_from_url(url: str, output_path: str, video_file_name: str) -> str:
        """Baixa o vídeo do YouTube usando yt-dlp."""
        video_path = f'{output_path}/{video_file_name}.%(ext)s'
        print(f"Baixando vídeo de {url} para {video_path}...")
        try:
            # Comando yt-dlp para baixar o melhor formato mp4
            command = [
                'yt-dlp',
                "--cookies", YOUTUBE_COOKIE_PATH,
                '-f', 'bestvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best',
                '-o', video_path,
                url
            ]
            result = subprocess.run(command, check=True, capture_output=True, text=True)
            lista_arquivos = File_Util.retirar_sufixo_codec_arquivo(output_path)
            print("Download de áudio concluído com sucesso.")
            return f"{output_path}/{lista_arquivos[0]}"
        except subprocess.CalledProcessError as e:
            print(f"Erro ao baixar o vídeo: {e}")
            print(f"Saída do erro: {e.stderr}")
            return False
        except FileNotFoundError:
            print("Erro: O comando 'yt-dlp' não foi encontrado. Certifique-se de que ele está instalado e no PATH do sistema.")
            print("Você pode instalá-lo com: pip install yt-dlp")
            return False
        


def execute_python_code_tool(code_path: str) -> str:
    """
        Execute code python informed in code_path param

        Args:
            code_path: Path to the python file.

        Returns:
            Execution result.
    """

    saida = io.StringIO()
    erros = io.StringIO()

    final_code_path = File_Util.baixa_arquivo_task(code_path)
    print(f"Executando código em {final_code_path}...")

    try:
        with open(final_code_path, 'r', encoding='utf-8') as f:
            codigo = f.read()

        # Captura stdout e stderr usando contexto
        with redirect_stdout(saida), redirect_stderr(erros):
            exec(codigo, {'__name__': '__main__'})

        # Pega o conteúdo das saídas
        saida_valor = saida.getvalue()
        erro_valor = erros.getvalue()

        if erro_valor:
            return f"[ERRO DE EXECUÇÃO]:\n{erro_valor}"
        return saida_valor if saida_valor.strip() else "[SEM SAÍDA]"

    except Exception:
        return f"[EXCEÇÃO DURANTE EXECUÇÃO]:\n{traceback.format_exc()}"
    
    
    
def chess_image_to_fen_tool(image_path:str, current_player: Literal["black", "white"]) -> Dict[str,str]:
    """
        Convert chess image to FEN (Forsyth-Edwards Notation) notation.
        Args:
            image_path: Path to the image file.
            current_player: Whose turn it is to play. Must be either 'black' or 'white'.
        Returns:
            JSON with FEN (Forsyth-Edwards Notation) string representing the current board position.
    """
    print(f"Image to Fen invocada com os seguintes parametros:")
    print(f"image_path: {image_path}")
    print(f"current_player: {current_player}")


    if current_player not in ["black", "white"]:
        raise ValueError("current_player must be 'black' or 'white'")

    final_image_path= os.path.join(AGENTS_FILES_PATH, image_path)
    
    base64_image = Image_Util.encode_image_to_base64(final_image_path)
    if not base64_image:
        raise ValueError("Failed to encode image to base64.")
    base64_image_encoded =  f"data:image/jpeg;base64,{base64_image}"
    url = CHESSVISION_TO_FEN_URL
    payload = {
        "board_orientation": "predict",
        "cropped": False,
        "current_player": "black",
        "image": base64_image_encoded,
        "predict_turn": False
    }

    response = requests.post(url, json=payload)
    if response.status_code == 200:
        dados = response.json()
        if dados.get("success"):
            print(f"Retorno Chessvision {dados}")
            fen = dados.get("result")
            fen = fen.replace("_", " ") #retorna _ no lugar de espaço em branco
            return json.dumps({"fen": fen})
        else:
            raise Exception("Requisição feita, mas falhou na predição.")
    else:
        raise Exception(f"Erro na requisição: {response.status_code}")

def chess_fen_get_best_next_move_tool(fen: str, current_player: Literal["black", "white"]) -> str:
    """
        Return the best move in algebric notation.
        Args:
            fen: FEN (Forsyth-Edwards Notation) notation.
        Returns:
            Best move in algebric notation.
    """
    if not fen:
        raise ValueError("fen must be provided.")
    if current_player not in ["black", "white"]:
        raise ValueError("current_player must be 'black' or 'white'")


    url = CHESS_MOVE_API
    payload = {
       "fen": fen
    }

    print(f"Buscando melhor jogada em {CHESS_MOVE_API} - {payload}")

    response = requests.post(url, json=payload)
    if response.status_code == 200:
        #print(f"Retorno melhor jogada --> {response.text}")
        dados = response.json()
        move_algebric_notation = dados.get("san")
        move = dados.get("text")
        print(f"Melhor jogada segundo chess-api.com -> {move}")

        return move_algebric_notation

    else:
        raise Exception(f"Erro na requisição: {response.status_code}")
    
    
    
def extract_frames_from_video_to_files(url: str) -> List[str]:
    """
        Extract frames from a video and store in temporaily files.
        Args:
            url: URL to the video.
        Returns:
            List of frame file paths.
    """
    frames_list: List[str] = []
    File_Util.create_or_clear_output_directory(OUTPUT_VIDEO_PATH)
    File_Util.create_or_clear_output_directory(OUTPUT_IMAGE_PATH)
    video_download_file_name = Video_Util.download_video_from_url(url, OUTPUT_VIDEO_PATH, VIDEO_FILE_NAME)
    if not video_download_file_name:
        raise ValueError("Failed to download video.")


    print(f"Extraindo frames de {video_download_file_name} a cada {FRAME_INTERVAL_SECONDS} segundos...")
    if not os.path.exists(video_download_file_name):
        print(f"Erro: Arquivo de vídeo não encontrado em {video_download_file_name}")
        return []

    cap = cv2.VideoCapture(video_download_file_name)
    # Verificar a resolução
    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    print(f"Resolução original do vídeo: {width}x{height}")

    if not cap.isOpened():
        print(f"Erro ao abrir o arquivo de vídeo: {video_download_file_name}")
        return []

    fps = cap.get(cv2.CAP_PROP_FPS)
    if fps == 0:
        print("Erro: Não foi possível obter o FPS do vídeo. Usando FPS padrão de 30.")
        fps = 30 # Valor padrão caso a leitura falhe

    # retirado para permitir fracionado frame_interval = int(fps * interval_sec)
    frame_interval = fps * FRAME_INTERVAL_SECONDS

    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    print(f"Vídeo FPS: {fps:.2f}, Intervalo de frames: {frame_interval}, Total de frames: {total_frames}")

    extracted_frames_paths = []
    frame_count = 0
    saved_frame_index = 5 # o importante nunca começa no inicio, é um deslocamento inicial para iniciar depois da introdução


    while True:
        # Define a posição do próximo frame a ser lido
        # Adiciona frame_interval para pegar o frame *após* o intervalo de tempo
        # ajustado para float target_frame_pos = saved_frame_index * frame_interval
        target_frame_pos = int(saved_frame_index * frame_interval)

        if target_frame_pos >= total_frames:
            break # Sai se o próximo frame alvo estiver além do final do vídeo

        if (saved_frame_index < INICIO_FRAME_IMPORTANTE or saved_frame_index > FIM_FRAME_IMPORTANTE):
            print(f"Pulando frame {saved_frame_index}")
            saved_frame_index += 1
            continue # evitar custo desnecessário para inferencia ao gpt
        cap.set(cv2.CAP_PROP_POS_FRAMES, target_frame_pos)
        ret, frame = cap.read()

        if not ret:
            print(f"Não foi possível ler o frame na posição {target_frame_pos}. Pode ser o fim do vídeo ou um erro.")
            break # Sai se não conseguir ler o frame

        # redimensiona o frame (custo chamada)
        # removido porque poderia afetar a nitidez e impactar o resultado
        # frame = cv2.resize(frame, (1280, 720))

        # Calcula o timestamp em segundos
        timestamp_sec = target_frame_pos / fps

        # Salva o frame
        frame_filename = f"frame_{saved_frame_index:04d}_time_{timestamp_sec:.2f}s.png"
        frame_path = os.path.join(OUTPUT_IMAGE_PATH, frame_filename)
        try:
            # modificado para salvar com qualidade máxima cv2.imwrite(frame_path, frame)
            cv2.imwrite(frame_path, frame, [cv2.IMWRITE_PNG_COMPRESSION, 0])

            extracted_frames_paths.append(frame_path)
            print(f"Frame salvo: {frame_path} (Timestamp: {timestamp_sec:.2f}s)")
            saved_frame_index += 1
        except Exception as e:
            print(f"Erro ao salvar o frame {frame_path}: {e}")
            # Continua para o próximo intervalo mesmo se um frame falhar

        # Segurança para evitar loop infinito caso algo dê errado com a lógica de posição
        if saved_frame_index > (total_frames / frame_interval) + 2:
             print("Aviso: Número de frames salvos parece exceder o esperado. Interrompendo extração.")
             break

    cap.release()
    print(f"Extração de frames concluída. Total de frames salvos: {len(extracted_frames_paths)}")
    return extracted_frames_paths


    return frames_list;


def count_birds_species(image_path: str) -> int:

    bird_count_prompt = """You are a world-class expert in avian species classification. Analyze the provided image and determine how many
                    **distinct bird species** are present. Consider size, shape, plumage, coloration, and beak structure. Focus only on
                    visible morphological differences. Return a **single integer** with no explanation. Do not count individuals of the same species. '
                    If unsure, assume that bird is a different specie."""

    if not OPENAI_API_KEY:
        raise ValueError("OPENAI API KEY must be defined.")

    base64_image = Image_Util.encode_image_to_base64(image_path)

    genai.configure(api_key=GEMINI_API_KEY)
    model = genai.GenerativeModel(GEMINI_MODEL)
    print(f"Enviando frame para análise no {GEMINI_MODEL}...")

    try:
        response = model.generate_content(
            contents=[
                {
                    "role": "user",
                    "parts": [
                        {f"text": f"{bird_count_prompt}"},
                        {"inline_data": {
                            "mime_type": "image/jpeg",
                            "data": base64_image
                        }}
                    ]
                }
            ],
            generation_config={
                "temperature": 0.0,
                "max_output_tokens": 500
            })

        # Extrai o conteúdo da resposta
        analysis_result = response.text.strip()
        print(f"Análise recebida: {analysis_result}")

        return int(analysis_result)
    except Exception as e:
        print(f"Erro ao chamar a API OpenAI: {e}")
        return {"error": str(e)}

def bird_video_count_tool(url: str) -> int:
    """
        Count different species of birds in a video.
        Args:
            url: URL to the video.
        Returns:
            Count of different species of birds.
    """



    frames_path_list = extract_frames_from_video_to_files(url)
    if not frames_path_list:
        raise ValueError("Failed to extract frames.")

    max_species: int = 0
    for frame_path in frames_path_list:
        species_count = count_birds_species(frame_path)
        if species_count > max_species:
            max_species = species_count

    return max_species


def extract_text_from_url_tool (audio_url:str) -> str:
    """
    Extracts text from an audio url using the OpenAI Whisper API.
    Args:
        audio_url: URL to the audio file.
    Returns:
        text extracted from the audio url.
    """

    if not audio_url:
        raise ValueError("'audio_url'must be provided.")
    if not OUTPUT_AUDIO_PATH:
        raise ValueError("OUTPUT_AUDIO_PATH must be defined.")

    File_Util.create_or_clear_output_directory(OUTPUT_AUDIO_PATH)
    audio_download_file_name = Audio_Util.download_audio_from_url(audio_url, OUTPUT_AUDIO_PATH, AUDIO_FILENAME)
    if not audio_download_file_name:
        raise ValueError("Failed to download audio.")

    transcript = Audio_Util.extract_text_from_audio_file(audio_download_file_name)

    return transcript


def extract_text_from_file_tool(audio_file_name:str) -> str:
    """
        Extracts text from an audio file using the OpenAI Whisper API.
    Args:
        audio_file_name: Name of the audio file.
    Returns:
        text extracted from the audio file.
    """

    if not audio_file_name and not audio_file_name:
        raise ValueError(" 'audio_file_name' must be provided.")
    if not OUTPUT_AUDIO_PATH:
        raise ValueError("OUTPUT_AUDIO_PATH must be defined.")

    treated_path = f"{AGENTS_FILES_PATH}/{audio_file_name}"
    transcript = Audio_Util.extract_text_from_audio_file(treated_path)

    return transcript



class Search_Web_Result(BaseModel):
    page_title: str
    page_url: str
    page_html_content: str
    page_markdown_content: str


def search_web_tool(query: str,
                    wikipedia_has_priority: bool,
                    wikipedia_historical_date: Optional[str]=None,
                    convert_to_markdown: bool=True
                    ) -> List[Search_Web_Result]:
    """
    Searches the web for pages with the most relevant information about the topic, returning a list of Search_Web_Result (title, url, html content and markdown content)

    Args:
        query: The main topic or question to search for.
        use_wikipedia_priority: If true, prioritize results from Wikipedia.
        wikipedia_date: Optional date to fetch historical Wikipedia data.

    Returns:
        A list of URLs or page titles sorted by relevance.
    """

    return_list: List[Search_Web_Result] = []


    try:
        tavily = TavilyClient(api_key=TAVILY_API_KEY)
    except Exception as e:
        print(f"Erro ao inicializar o cliente Tavily: {e}")
        raise

    print(f"\n--- Realizando busca por '{query}' usando Tavily ---")
    print(f"Prioridade para Wikipedia: {wikipedia_has_priority}")
    print(f"Data para Wikipedia: {wikipedia_historical_date}")
    print(f"Convertendo HTML para Markdown: {convert_to_markdown}")

    try:
        response = tavily.search(query=query, search_depth="basic", max_results=10)
        search_results = response.get('results', [])
    except Exception as e:
        print(f"Erro ao realizar busca com Tavily: {e}")
        raise

    if not search_results:
        print("Nenhum resultado encontrado pela busca Tavily.")
        return []

    if wikipedia_has_priority:
        print("Prioridade para Wikipedia habilitada. Filtrando resultados Tavily por Wikipedia...")
        return _processa_resultado_wikipedia(search_results, wikipedia_historical_date, convert_to_markdown)


    urls_to_process = []
    print("Usando os 5 primeiros resultados gerais.")
    urls_to_process = [res['url'] for res in search_results[:5]]

    print(f"\n--- Processando {len(urls_to_process)} URLs selecionadas ---")
    for url in urls_to_process:
        title, html_content = Web_Util.download_html(url)
        if not title or not html_content:
            raise AssertionError(f"Falha ao processar URL: {url}")

        md_content = ""
        if convert_to_markdown:
            md_content = Web_Util.convert_html_to_markdown(title, html_content)
            if not md_content:
                raise AssertionError(f"Falha ao converter URL: {url}, html:{html_content}")
        return_list.append(Search_Web_Result(
            page_title=title,
            page_url=url,
            page_html_content=html_content if not convert_to_markdown else "",
            page_markdown_content=md_content
        ))

    return return_list



def _processa_resultado_wikipedia(search_results: List[str], wikipedia_historical_date: str,
                                  convert_to_markdown:bool) -> List[Search_Web_Result]:
    """
    Trata do resultado de pesquisa quando existe prioridade para Wikipedia.
    Args:
        search_results: Lista com resultados da busca realizado pelo Tavily.
        wikipedia_historical_date: A data para buscar uma revisão histórica da Wikipedia.
        convert_to_markdown: Se true, converte o conteúdo HTML para Markdown.
    Returns:
        Lista com os resultados processados.
    """

    print("Prioridade para Wikipedia habilitada. Filtrando resultados Tavily por Wikipedia...")
    wiki_urls = [res['url'] for res in search_results if Web_Util.is_wikipedia_url(res['url'])]
    if not wiki_urls:
        print("Nenhuma URL da Wikipedia encontrada nos resultados.")
        return []
    # Pega o primeiro resultado da Wikipedia
    first_wiki_url = wiki_urls[0]
    page_title_guess = first_wiki_url.split('/')[-1].replace('_', ' ')
    page_check = Wikipedia_Util.wiki_executor.page(page_title_guess)
    if not page_check.exists():
        raise AssertionError(f"Página '{page_title_guess}' não encontrada na Wikipedia.")

    page_title = None
    page_url = None

    if not wikipedia_historical_date:
        page_title = page_title_guess
        page_url = first_wiki_url
    else:
        # Busca revisão histórica
        historical_wiki_info: Wikipedia_Historical_Page = Wikipedia_Util.get_wikipedia_page_historical_content(page_check.title, wikipedia_historical_date)
        print(f"Dados da versão histórica wikipedia - {historical_wiki_info}")
        page_title = historical_wiki_info.title
        page_url = historical_wiki_info.url

    title, html_content = Web_Util.download_html(page_url)
    print(f"title {title}")
    if not html_content:
        raise AssertionError(f"Conteúdo da página {page_url} não foi baixado, não será possível continuar.")

    md_content = ""
    if convert_to_markdown:
        md_content = Web_Util.convert_html_to_markdown(page_title, html_content)
        if md_content and wikipedia_historical_date:
            # Adiciona informação sobre a revisão no início do conteúdo (CORRIGIDO)
            header = f"# Wikipedia Content for '{historical_wiki_info.title}'\n"
            header += f"*Revision from {historical_wiki_info.timestamp} (ID: {historical_wiki_info.revision_id})*\n"
            header += f"*Regarding search date: {wikipedia_historical_date}*\n\n"
            header += "---\n\n"
            md_content = header + md_content

    return_list = [
         Search_Web_Result(
            page_title=page_title,
            page_url=page_url,
            page_html_content=html_content if not convert_to_markdown else "",
            page_markdown_content=md_content
        )
    ]
    return return_list



def text_inverter_tool(text: str ) -> str:
    """
    Invert the text.
    Args:
        text: Text to be inverted.
    Returns:
        Inverted text.
    """
    return text[::-1]



def parse_markdown_table_to_dict(markdown: str) -> dict:
    """
        Convert binary operation table in markdown format to a dictionary
        Args:
            markdown: table in markdown format

    """
    linhas = markdown.strip().split('\n')

    # Remove barras verticais nas extremidades e divide pelas internas
    cabecalho = [col.strip() for col in linhas[0].strip('|').split('|')]
    colunas = cabecalho[1:]  # ignora o '*'

    tabela = {}

    for linha in linhas[2:]:  # pula cabeçalho e separador
        partes = [p.strip() for p in linha.strip('|').split('|')]
        linha_elem = partes[0]
        valores = partes[1:]
        if len(valores) != len(colunas):
            raise ValueError(f"Erro ao processar linha '{linha_elem}': número de colunas incompatível.")
        tabela[linha_elem] = dict(zip(colunas, valores))

    return tabela

def check_table_commutativity_tool(markdown: str) -> dict:
    """
        Check if the table in markdown format is commutative
        Args:
            table: table in markdown format
    """
    contraexemplos = []
    elementos = set()

    table = parse_markdown_table_to_dict(markdown)

    for x in table:
        for y in table:
            if x != y and table[x][y] != table[y][x]:
                contraexemplos.append((x, y))
                elementos.update([x, y])
    return {
        "counter_example": contraexemplos,
        "elements_involved": sorted(elementos)
    }
    
    
    
def get_excel_columns_tool(file_path: str) -> list[str]:
    """
        Get the columns of an Excel file.

        Args:
            file_path: Path to the Excel file.

        Returns:
            List of column names.

    """
    final_excel_path = File_Util.baixa_arquivo_task(file_path)
    print(f"Extraindo as colunas do arquivo {file_path}")

    df = pd.read_excel(final_excel_path, nrows=0)
    return df.columns.tolist()

def calculate_excel_sum_by_columns_tool(
    file_path: str,
    include_columns: list[str]
) -> str:
    """
        Calculate the sum of values in specified columns of an Excel file.

    Args:
    - file_path: Path to the Excel file.
    - include_columns: Columns included in the sum
    """
    final_excel_path = File_Util.baixa_arquivo_task(file_path)
    print(f"Calculando soma de {include_columns} em {final_excel_path}")

    df = pd.read_excel(final_excel_path)
    total = df[include_columns].sum().sum()  # soma todas as colunas e depois soma os totais
    return total

# Lista curada de vegetais culinários
VEGETABLES = {
    "lettuce", "carrot", "broccoli", "spinach", "kale", "celery", "cabbage",
    "sweet potato", "radish", "turnip", "cauliflower", "beet", "onion", "garlic",
    "pea", "chard", "arugula", "basil", "parsley", "dill", "leek",
    "asparagus", "eggplant", "okra", "pumpkin", "squash", "yam", "collard green",
    "mustard green", "brussels sprout", "scallion", "fennel", "rhubarb", "artichoke",
    "endive", "escarole", "bok choy", "watercress", "turnip green"
}

COMMON_ADJECTIVES = {"fresh", "raw", "organic", "chopped", "sliced", "whole"}

def normalize_item(text: str) -> str:
    # Lowercase and remove common adjectives
    words = [w for w in re.findall(r"\w+", text.lower()) if w not in COMMON_ADJECTIVES]
    # Singularização básica
    singular = []
    for word in words:
        if word.endswith("ies"):
            singular.append(word[:-3] + "y")
        elif word.endswith("oes"):
            singular.append(word[:-2])
        elif word.endswith("s") and not word.endswith("ss"):
            singular.append(word[:-1])
        else:
            singular.append(word)
    return " ".join(singular)

def filter_vegetables_from_list_tool(items: list[str]) -> list[str]: 
    """
        Return a set of vegetables from items
        
        Args:
            items:
                Listo of items
            
        Returns:
            List of vegetable items
    """ 
    result =[]
    for i in items:
        
        if normalize_item(i) in VEGETABLES:
            result.append(i)
            
    return result


def clean_ingredient_measure_tool(ingredients: list[str]) -> list[str]:
    """
        Strips words that indicate measurements or quantities from a list of ingredients
        and returns only the cleaned ingredient names, without duplicates and in alphabetical order.

        The function should be used when extracting ingredients from audio or text
        contains units such as "dash", "pinch", "cup", etc., and when it is necessary to
        keep only the descriptive names of the ingredients for a shopping list or display.

        Parameters:
        - ingredients: list of strings, where each string is an ingredient extracted from the audio or transcript.

        Returns:
        - List of strings with the names of the ingredients cleaned, without units of measurement and sorted alphabetically.
    """
    
    cleaned = []
    for ingredient in ingredients:
        words = ingredient.split()
        filtered_words = [word for word in words if word.lower() not in MEASURE_WORDS]
        cleaned_ingredient = ' '.join(filtered_words).strip()
        if cleaned_ingredient:
            cleaned.append(cleaned_ingredient)

    # Remove duplicatas e ordena
    return sorted(set(cleaned))