Create app.py

app.py

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+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")
+    kenlm_path = hf_hub_download(repo_id, "ocr/kenlm_corpus.arpa")
+    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']['lm_path'] = kenlm_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-notebooks")
+
+PREDICTOR = PipelinePredictor(pipeline_config_path=PIPELINE_CONFIG_PATH)
+
+gr.Interface(
+    predict,
+    inputs=gr.Image(label="Upload an image of handwritten school notebook", type="filepath"),
+    outputs=gr.Textbox(label="Text on the image"),
+    title="School notebook recognition",
+).launch()