train.py

import os
import glob
import json
import csv
import numpy as np
from tqdm.auto import tqdm
from sentence_transformers import SentenceTransformer
import zipfile
import xml.etree.ElementTree as ET

DATA_DIR = "/app/dados"
EXTRACT_DIR = "/app/dados_extraidos"

def setup_data():
    os.makedirs(EXTRACT_DIR, exist_ok=True)
    zip_files = glob.glob(DATA_DIR + "/**/*.zip", recursive=True)
    if not zip_files:
        print("Nenhum arquivo .zip encontrado, usando o diretório de dados principal.")
        return DATA_DIR
    for zip_path in zip_files:
        print(f"Descompactando {zip_path}...")
        with zipfile.ZipFile(zip_path, 'r') as zf:
            zf.extractall(EXTRACT_DIR)
    return EXTRACT_DIR

def xml_to_dict(element):
    d = {}
    for child in element:
        child_dict = xml_to_dict(child)
        if child.tag in d:
            if not isinstance(d[child.tag], list):
                d[child.tag] = [d[child.tag]]
            d[child.tag].append(child_dict)
        else:
            d[child.tag] = child_dict
    if not d:
        return element.text
    return d

def serialize_item_to_text(item_dict):
    parts = []
    if not isinstance(item_dict, dict):
        return str(item_dict)
    for key, value in item_dict.items():
        if isinstance(value, dict):
            nested_text = serialize_item_to_text(value)
            parts.append(f"{key} ({nested_text})")
        elif isinstance(value, list):
            list_str = ', '.join([serialize_item_to_text(i) for i in value])
            parts.append(f"{key}: [{list_str}]")
        else:
            parts.append(f"{key}: {value}")
    return ", ".join(parts)

def main():
    process_dir = setup_data()
    csv.field_size_limit(10_000_000)

    all_files = glob.glob(process_dir + "/**/*.json", recursive=True) + \
                glob.glob(process_dir + "/**/*.csv", recursive=True) + \
                glob.glob(process_dir + "/**/*.xml", recursive=True)
    print(f"\n🔎 Encontrados {len(all_files)} arquivos (JSON, CSV, XML) para processar.")

    if not all_files:
        return

    documents = []
    for filepath in tqdm(all_files, desc="Processando arquivos"):
        try:
            if filepath.endswith('.json'):
                with open(filepath, 'r', encoding='utf-8') as f:
                    data = json.load(f)
                    if isinstance(data, list):
                        for item in data: documents.append(serialize_item_to_text(item))
                    else:
                        documents.append(serialize_item_to_text(data))
            elif filepath.endswith('.csv'):
                with open(filepath, 'r', encoding='utf-8') as f:
                    reader = csv.DictReader(f)
                    for row in reader: documents.append(serialize_item_to_text(row))
            elif filepath.endswith('.xml'):
                tree = ET.parse(filepath)
                root = tree.getroot()
                xml_dict = {root.tag: xml_to_dict(root)}
                documents.append(serialize_item_to_text(xml_dict))
        except Exception as e:
            print(f"⚠️ Erro ao processar o arquivo {filepath}: {e}")

    print(f"\nProcessamento de arquivos concluído! {len(documents)} documentos foram criados.")
    if not documents:
        return

    cache_path = os.environ.get('SENTENCE_TRANSFORMERS_HOME', '/app/cache/torch')
    
    print("Carregando modelo de alta performance: intfloat/multilingual-e5-large")
    model = SentenceTransformer(
        'intfloat/multilingual-e5-large',
        cache_folder=cache_path
    )
    
    batch_size = 128
    output_filename = '/app/output/meus_embeddings_e5_large.npy'
    
    if os.path.exists(output_filename):
        os.remove(output_filename)

    print(f"🚀 Iniciando geração de embeddings (lotes de {batch_size}).")
    for i in tqdm(range(0, len(documents), batch_size), desc="Gerando Embeddings"):
        batch = documents[i:i + batch_size]
        batch_embeddings = model.encode(batch, show_progress_bar=False)
        with open(output_filename, 'ab') as f_out:
            np.save(f_out, batch_embeddings)

    print(f"✅ Processo finalizado! Embeddings salvos em '{output_filename}'.")

if __name__ == "__main__":
        main()