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import numpy as np |
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import torch |
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from torch import nn |
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import streamlit as st |
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import os |
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from PIL import Image |
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from io import BytesIO |
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from transformers import VisionEncoderDecoderModel, VisionEncoderDecoderConfig, DonutProcessor, DonutImageProcessor, AutoTokenizer |
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def run_prediction(sample, model, processor): |
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pixel_values = processor(np.array( |
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sample, |
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np.float32, |
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), return_tensors="pt").pixel_values |
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with torch.no_grad(): |
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outputs = model.generate( |
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pixel_values.to(device), |
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decoder_input_ids=processor.tokenizer("<s><s_plain>", add_special_tokens=False, return_tensors="pt").input_ids.to(device), |
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do_sample=True, |
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top_p=0.92, |
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top_k=5, |
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no_repeat_ngram_size=10, |
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num_beams=3 |
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) |
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prediction = processor.batch_decode(outputs)[0] |
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print(prediction) |
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return prediction |
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logo = Image.open("./rsz_unstructured_logo.png") |
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st.image(logo) |
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st.markdown(''' |
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### Chipper |
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Chipper is an OCR-free Document Understanding Transformer. It was pre-trained with over 1M documents from public sources and fine-tuned on a large range of documents. |
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At [Unstructured.io](https://github.com/Unstructured-IO/unstructured) we are on a mission to build custom preprocessing pipelines for labeling, training, or production ML-ready pipelines. |
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Come and join us in our public repos and contribute! Each of your contributions and feedback holds great value and is very significant to the community. |
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''') |
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image_upload = None |
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photo = None |
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with st.sidebar: |
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uploaded_file = st.file_uploader("Upload a document") |
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if uploaded_file is not None: |
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image_bytes_data = uploaded_file.getvalue() |
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image_upload = Image.open(BytesIO(image_bytes_data)) |
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if image_upload: |
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image = image_upload |
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else: |
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image = Image.open(f"./document.png") |
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st.image(image, caption='Your target document') |
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with st.spinner(f'Processing the document ...'): |
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pre_trained_model = "unstructuredio/chipper-fast-fine-tuning" |
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processor = DonutProcessor.from_pretrained(pre_trained_model) |
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model = VisionEncoderDecoderModel.from_pretrained(pre_trained_model) |
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from huggingface_hub import hf_hub_download |
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lm_head_file = hf_hub_download( |
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repo_id=pre_trained_model, filename="lm_head.pth" |
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) |
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rank = 128 |
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model.decoder.lm_head = nn.Sequential( |
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nn.Linear(model.decoder.lm_head.weight.shape[1], rank, bias=False), |
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nn.Linear(rank, rank, bias=False), |
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nn.Linear(rank, model.decoder.lm_head.weight.shape[0], bias=True), |
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) |
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model.decoder.lm_head.load_state_dict(torch.load(lm_head_file)) |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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model.eval() |
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model.to(device) |
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st.info(f'Parsing document') |
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parsed_info = run_prediction(image.convert("RGB"), model, processor) |
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st.text(f'\nDocument:') |
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st.text_area('Output text', value=parsed_info, height=800) |
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