import os os.system("pip install git+https://github.com/ai-forever/ReadingPipeline.git") import cv2 import json import gradio as gr from huggingface_hub import hf_hub_download from ocrpipeline.predictor import PipelinePredictor from ocrpipeline.linefinder import get_structured_text def get_config_and_download_weights(repo_id, device='cpu'): # download weights and configs pipeline_config_path = hf_hub_download(repo_id, "pipeline_config.json") ocr_model_path = hf_hub_download(repo_id, "ocr/ocr_model.onnx") ocr_config_path = hf_hub_download(repo_id, "ocr/ocr_config.json") segm_model_path = hf_hub_download(repo_id, "segm/segm_model.onnx") segm_config_path = hf_hub_download(repo_id, "segm/segm_config.json") # change paths to downloaded weights and configs in main pipeline_config with open(pipeline_config_path, 'r') as f: pipeline_config = json.load(f) pipeline_config['main_process']['SegmPrediction']['model_path'] = segm_model_path pipeline_config['main_process']['SegmPrediction']['config_path'] = segm_config_path pipeline_config['main_process']['SegmPrediction']['num_threads'] = 2 pipeline_config['main_process']['SegmPrediction']['device'] = device pipeline_config['main_process']['SegmPrediction']['runtime'] = "OpenVino" pipeline_config['main_process']['OCRPrediction']['model_path'] = ocr_model_path pipeline_config['main_process']['OCRPrediction']['config_path'] = ocr_config_path pipeline_config['main_process']['OCRPrediction']['num_threads'] = 2 pipeline_config['main_process']['OCRPrediction']['device'] = device pipeline_config['main_process']['OCRPrediction']['runtime'] = "OpenVino" # save pipeline_config with open(pipeline_config_path, 'w') as f: json.dump(pipeline_config, f) return pipeline_config_path def predict(image_path): image = cv2.imread(image_path) rotated_image, pred_data = PREDICTOR(image) structured_text = get_structured_text(pred_data, ['shrinked_text']) result_text = [' '.join(line_text) for page_text in structured_text for line_text in page_text] return '\n'.join(result_text) PIPELINE_CONFIG_PATH = get_config_and_download_weights("sberbank-ai/ReadingPipeline-Peter") PREDICTOR = PipelinePredictor(pipeline_config_path=PIPELINE_CONFIG_PATH) gr.Interface( predict, inputs=gr.Image(label="Upload an image of Peter manuscript", type="filepath"), outputs=gr.Textbox(label="Text on the image"), title="Peter manuscript recognition", ).launch()