Spaces:
Sleeping
Sleeping
File size: 3,185 Bytes
0fb58bf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
import gradio as gr
import logging
import os
import json
from PIL import Image, ImageDraw
import torch
from surya.ocr import run_ocr
from surya.detection import batch_text_detection
from surya.layout import batch_layout_detection
from surya.ordering import batch_ordering
from surya.model.detection.model import load_model as load_det_model, load_processor as load_det_processor
from surya.model.recognition.model import load_model as load_rec_model
from surya.model.recognition.processor import load_processor as load_rec_processor
from surya.settings import settings
# Configuração de logging
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
# Carregamento de modelos
det_processor, det_model = load_det_processor(), load_det_model()
rec_model, rec_processor = load_rec_model(), load_rec_processor()
class CustomJSONEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, Image.Image):
return "Image object (not serializable)"
return str(obj)
def serialize_result(result):
return json.dumps(result, cls=CustomJSONEncoder, indent=2)
def save_metadata(results):
output_file = "/mnt/data/ocr_metadata.json" # Caminho de armazenamento persistente
with open(output_file, "w") as f:
json.dump(results, f, cls=CustomJSONEncoder, indent=2)
return output_file
def ocr_workflow(images, langs):
logger.info(f"Iniciando workflow OCR para {len(images)} imagens com idiomas: {langs}")
results = []
for image_file in images:
try:
image = Image.open(image_file.name)
predictions = run_ocr([image], [langs.split(',')], det_model, det_processor, rec_model, rec_processor)
formatted_text = "\n".join([line.text for line in predictions[0].text_lines])
results.append({
"image": image_file.name,
"text": formatted_text,
"predictions": predictions[0].text_lines # Assuming text_lines is serializable
})
except Exception as e:
logger.error(f"Erro com {image_file.name}: {e}")
results.append({"image": image_file.name, "error": str(e)})
metadata_file = save_metadata(results)
return serialize_result(results), metadata_file
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("# Análise de Documentos com Surya")
with gr.Tab("OCR"):
gr.Markdown("## Reconhecimento Óptico de Caracteres para Múltiplas Imagens")
with gr.Row():
ocr_input = gr.Files(label="Carregar Imagens ou PDFs (até 100)")
ocr_langs = gr.Textbox(label="Idiomas (separados por vírgula)", value="en")
ocr_button = gr.Button("Executar OCR")
ocr_output = gr.JSON(label="Resultados OCR")
ocr_file = gr.File(label="Baixar Metadata OCR")
# Executa a função OCR e salva o arquivo de metadata para download
ocr_button.click(ocr_workflow, inputs=[ocr_input, ocr_langs], outputs=[ocr_output, ocr_file])
if __name__ == "__main__":
logger.info("Iniciando aplicativo Gradio...")
demo.launch()
|