Create app.py

app.py

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+import os
+
+os.system('pip install pip --upgrade')
+os.system('pip install -q git+https://github.com/huggingface/transformers.git')
+
+
+os.system("pip install pyyaml==5.1")
+# workaround: install old version of pytorch since detectron2 hasn't released packages for pytorch 1.9 (issue: https://github.com/facebookresearch/detectron2/issues/3158)
+os.system(
+    "pip install torch==1.8.0+cu101 torchvision==0.9.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html"
+)
+
+# install detectron2 that matches pytorch 1.8
+# See https://detectron2.readthedocs.io/tutorials/install.html for instructions
+os.system(
+    "pip install -q detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.8/index.html"
+)
+
+## install PyTesseract
+os.system("pip install -q pytesseract")
+
+import gradio as gr
+import numpy as np
+from transformers import LayoutLMv3Processor, LayoutLMv3ForTokenClassification
+from datasets import load_dataset
+from PIL import Image, ImageDraw, ImageFont
+
+processor = LayoutLMv3Processor.from_pretrained("jinhybr/LiLt-funsd-en")
+model = LayoutLMv3ForTokenClassification.from_pretrained(
+    "jinhybr/LiLt-funsd-en"
+)
+
+# load image example
+dataset = load_dataset("nielsr/funsd-layoutlmv3", split="test")
+image = Image.open(dataset[0]["image_path"]).convert("RGB")
+image = Image.open("./example_lm3.png")
+image.save("document.png")
+
+labels = dataset.features["ner_tags"].feature.names
+id2label = {v: k for v, k in enumerate(labels)}
+label2color = {
+    "question": "blue",
+    "answer": "green",
+    "header": "orange",
+    "other": "violet",
+}
+
+
+def unnormalize_box(bbox, width, height):
+    return [
+        width * (bbox[0] / 1000),
+        height * (bbox[1] / 1000),
+        width * (bbox[2] / 1000),
+        height * (bbox[3] / 1000),
+    ]
+
+
+def iob_to_label(label):
+    label = label[2:]
+    if not label:
+        return "other"
+    return label
+
+
+def process_image(image):
+    width, height = image.size
+
+    # encode
+    encoding = processor(
+        image, truncation=True, return_offsets_mapping=True, return_tensors="pt"
+    )
+    offset_mapping = encoding.pop("offset_mapping")
+
+    # forward pass
+    outputs = model(**encoding)
+
+    # get predictions
+    predictions = outputs.logits.argmax(-1).squeeze().tolist()
+    token_boxes = encoding.bbox.squeeze().tolist()
+
+    # only keep non-subword predictions
+    is_subword = np.array(offset_mapping.squeeze().tolist())[:, 0] != 0
+    true_predictions = [
+        id2label[pred] for idx, pred in enumerate(predictions) if not is_subword[idx]
+    ]
+    true_boxes = [
+        unnormalize_box(box, width, height)
+        for idx, box in enumerate(token_boxes)
+        if not is_subword[idx]
+    ]
+
+    # draw predictions over the image
+    draw = ImageDraw.Draw(image)
+    font = ImageFont.load_default()
+    for prediction, box in zip(true_predictions, true_boxes):
+        predicted_label = iob_to_label(prediction).lower()
+        draw.rectangle(box, outline=label2color[predicted_label])
+        draw.text(
+            (box[0] + 10, box[1] - 10),
+            text=predicted_label,
+            fill=label2color[predicted_label],
+            font=font,
+        )
+
+    return image
+
+
+title = "OCR Document Parser : Information Extraction - Fine Tuned LiLT Language-independent Layout Transformer Model"
+description = "Demo for  LiLT Language-independent Layout Transformer, a Transformer for state-of-the-art document image understanding tasks. This particular model is fine-tuned on FUNSD, a dataset of manually annotated forms. It annotates the words appearing in the image as QUESTION/ANSWER/HEADER/OTHER. To use it, simply upload an image or use the example image below and click 'Submit'. Results will show up in a few seconds. If you want to make the output bigger, right-click on it and select 'Open image in new tab'."
+article = "<p style='text-align: center'><a href=' https://arxiv.org/abs/2202.13669' target='_blank'> LiLT Language-independent Layout Transformer</a> | <a href='https://github.com/jpwang/lilt' target='_blank'>Github Repo</a></p>"
+examples = [["document.png"]]
+
+css = ".output-image, .input-image {height: 40rem !important; width: 100% !important;}"
+# css = "@media screen and (max-width: 600px) { .output_image, .input_image {height:20rem !important; width: 100% !important;} }"
+# css = ".output_image, .input_image {height: 600px !important}"
+
+css = ".image-preview {height: auto !important;}"
+
+iface = gr.Interface(
+    fn=process_image,
+    inputs=gr.inputs.Image(type="pil"),
+    outputs=gr.outputs.Image(type="pil", label="annotated image"),
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+    css=css,
+    enable_queue=True,
+)
+iface.launch(debug=True)