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Create app.py

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  1. app.py +64 -0
app.py ADDED
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+ import os
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+ os.system("pip install git+https://github.com/ai-forever/ReadingPipeline.git")
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+
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+ import cv2
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+ import json
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+
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+ import gradio as gr
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+ from huggingface_hub import hf_hub_download
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+
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+ from ocrpipeline.predictor import PipelinePredictor
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+ from ocrpipeline.linefinder import get_structured_text
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+
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+
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+ def get_config_and_download_weights(repo_id, device='cpu'):
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+ # download weights and configs
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+ pipeline_config_path = hf_hub_download(repo_id, "pipeline_config.json")
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+ ocr_model_path = hf_hub_download(repo_id, "ocr/ocr_model.onnx")
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+ kenlm_path = hf_hub_download(repo_id, "ocr/kenlm_corpus.arpa")
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+ ocr_config_path = hf_hub_download(repo_id, "ocr/ocr_config.json")
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+ segm_model_path = hf_hub_download(repo_id, "segm/segm_model.onnx")
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+ segm_config_path = hf_hub_download(repo_id, "segm/segm_config.json")
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+
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+ # change paths to downloaded weights and configs in main pipeline_config
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+ with open(pipeline_config_path, 'r') as f:
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+ pipeline_config = json.load(f)
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+ pipeline_config['main_process']['SegmPrediction']['model_path'] = segm_model_path
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+ pipeline_config['main_process']['SegmPrediction']['config_path'] = segm_config_path
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+ pipeline_config['main_process']['SegmPrediction']['num_threads'] = 2
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+ pipeline_config['main_process']['SegmPrediction']['device'] = device
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+ pipeline_config['main_process']['SegmPrediction']['runtime'] = "OpenVino"
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+
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+ pipeline_config['main_process']['OCRPrediction']['model_path'] = ocr_model_path
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+ pipeline_config['main_process']['OCRPrediction']['lm_path'] = kenlm_path
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+ pipeline_config['main_process']['OCRPrediction']['config_path'] = ocr_config_path
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+ pipeline_config['main_process']['OCRPrediction']['num_threads'] = 2
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+ pipeline_config['main_process']['OCRPrediction']['device'] = device
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+ pipeline_config['main_process']['OCRPrediction']['runtime'] = "OpenVino"
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+
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+ # save pipeline_config
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+ with open(pipeline_config_path, 'w') as f:
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+ json.dump(pipeline_config, f)
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+
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+ return pipeline_config_path
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+
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+
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+ def predict(image_path):
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+ image = cv2.imread(image_path)
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+ rotated_image, pred_data = PREDICTOR(image)
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+ structured_text = get_structured_text(pred_data, ['shrinked_text'])
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+ result_text = [' '.join(line_text) for page_text in structured_text
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+ for line_text in page_text]
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+ return '\n'.join(result_text)
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+
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+
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+ PIPELINE_CONFIG_PATH = get_config_and_download_weights("sberbank-ai/ReadingPipeline-notebooks")
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+
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+ PREDICTOR = PipelinePredictor(pipeline_config_path=PIPELINE_CONFIG_PATH)
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+
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+ gr.Interface(
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+ predict,
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+ inputs=gr.Image(label="Upload an image of handwritten school notebook", type="filepath"),
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+ outputs=gr.Textbox(label="Text on the image"),
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+ title="School notebook recognition",
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+ ).launch()