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app.py

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+import os
+os.system('pip install paddlepaddle')
+os.system('pip install paddleocr')
+from paddleocr import PaddleOCR, draw_ocr
+from PIL import Image
+import gradio as gr
+import torch
+
+torch.hub.download_url_to_file('https://i.imgur.com/aqMBT0i.jpg', 'example.jpg')
+
+def inference(img, lang):
+    ocr = PaddleOCR(use_angle_cls=True, lang=lang,use_gpu=False)
+    img_path = img.name
+    result = ocr.ocr(img_path, cls=True)
+    image = Image.open(img_path).convert('RGB')
+    boxes = [line[0] for line in result]
+    txts = [line[1][0] for line in result]
+    # scores = [line[1][1] for line in result]
+    im_show = draw_ocr(image, boxes, txts,
+                       font_path='simfang.ttf')
+    im_show = Image.fromarray(im_show)
+    im_show.save('result.jpg')
+    return 'result.jpg'
+
+title = 'A Framework for Data-Driven Document Evaluation and scoring - Image to Text Extraction '
+description = 'Demo for Optical character recognition(OCR) Using Tesseract and openCV for Mtech Project'
+article = ""
+examples = [['example.jpg','en']]
+css = ".output_image, .input_image {height: 40rem !important; width: 100% !important;}"
+gr.Interface(
+    inference,
+    [gr.inputs.Image(type='file', label='Input'),gr.inputs.Dropdown(choices=['ch', 'en', 'fr', 'german', 'korean', 'japan'], type="value", default='en', label='language')],
+    gr.outputs.Image(type='file', label='Output'),
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+    css=css,
+    enable_queue=True
+    ).launch(debug=True)