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import gradio as gr
def classify(input_img):
from transformers import (
AutoModelForSequenceClassification,
LayoutLMv2FeatureExtractor,
LayoutLMv2Tokenizer,
LayoutLMv2Processor,
)
model = AutoModelForSequenceClassification.from_pretrained(
"fedihch/InvoiceReceiptClassifier"
)
feature_extractor = LayoutLMv2FeatureExtractor()
tokenizer = LayoutLMv2Tokenizer.from_pretrained("microsoft/layoutlmv2-base-uncased")
processor = LayoutLMv2Processor(feature_extractor, tokenizer)
encoded_inputs = processor(input_img, return_tensors="pt")
for k, v in encoded_inputs.items():
encoded_inputs[k] = v.to(model.device)
outputs = model(**encoded_inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
id2label = {0: "invoice", 1: "receipt"}
return id2label[predicted_class_idx]
examples =[['https://upserve.com/media/sites/2/Bill-from-Mezcalero-in-Washington-D.C.-photo-by-Alfredo-Solis-1-e1507226752437.jpg',
'https://templates.invoicehome.com/invoice-template-us-neat-750px.png']]
demo = gr.Interface(
fn=classify,
inputs=gr.Image(shape=(200, 200)),
outputs="text",
allow_flagging="manual",
description="Upload an invoice or receipt image and the model will classify it!",
examples=examples
)
demo.launch(share=True)
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