App push

README.md

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 ---
-title: Layoutlmv2_invoice
+title: Invoice Information Extractor
 emoji: ⚡
 colorFrom: blue
 colorTo: purple

app.py

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+import os
+
+os.system('pip install torch==1.8.0+cpu torchvision==0.9.0+cpu -f https://download.pytorch.org/whl/torch_stable.html')
+os.system('pip install -q detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/torch1.8/index.html')
+
+
+import gradio as gr
+import numpy as np
+from transformers import LayoutLMv2Processor, LayoutLMv2ForTokenClassification
+from datasets import load_dataset
+from PIL import Image, ImageDraw, ImageFont
+
+processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased")
+model = LayoutLMv2ForTokenClassification.from_pretrained("Theivaprakasham/layoutlmv2-finetuned-sroie_mod")
+
+# load image example
+dataset = load_dataset("darentang/generated", split="test")
+Image.open(dataset[50]["image_path"]).convert("RGB").save("example1.png")
+Image.open(dataset[14]["image_path"]).convert("RGB").save("example2.png")
+Image.open(dataset[20]["image_path"]).convert("RGB").save("example3.png")
+# define id2label, label2color
+labels = dataset.features['ner_tags'].feature.names
+id2label = {v: k for v, k in enumerate(labels)}
+label2color = {'b-abn': "blue",
+ 'b-biller': "blue",
+ 'b-biller_address': "black",
+ 'b-biller_post_code': "green",
+ 'b-due_date': "orange",
+ 'b-gst': 'red',
+ 'b-invoice_date': 'red',
+ 'b-invoice_number': 'violet',
+ 'b-subtotal': 'green',
+ 'b-total': 'green',
+ 'i-biller_address': 'blue',
+ 'o': '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):
+    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 = "Invoice Information extraction using LayoutLMv2 model"
+description = "Invoice Information Extraction - We use Microsoft's LayoutLMv2 trained on Invoice Dataset to predict the Biller Name, Biller Address, Biller post_code, Due_date, GST, Invoice_date, Invoice_number, Subtotal and Total. To use it, simply upload an image or use the example image below. Results will show up in a few seconds."
+
+article="<b>References</b><br>[1] Y. Xu et al., “LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding.” 2022. <a href='https://arxiv.org/abs/2012.14740'>Paper Link</a><br>[2]  <a href='https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LayoutLMv2/FUNSD'>LayoutLMv2 training and inference</a>" 
+
+examples =[['example1.png'],['example2.png'],['example3.png']]
+
+
+css = """.output_image, .input_image {height: 600px !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,
+                     analytics_enabled = True, enable_queue=True)
+iface.launch(inline=False, share=True, debug=False)

packages.txt

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+ffmpeg
+libsm6
+libxext6 -y
+libgl1
+-y libgl1-mesa-glx
+tesseract-ocr

requirements.txt

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+git+https://github.com/huggingface/transformers.git
+pyyaml==5.1
+pytesseract==0.3.9
+git+https://github.com/huggingface/datasets#egg=datasets