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| import os | |
| os.environ["TOKENIZERS_PARALLELISM"] = "false" | |
| from PIL import ImageDraw | |
| import streamlit as st | |
| import torch | |
| from docquery.pipeline import get_pipeline | |
| from docquery.document import load_bytes, load_document | |
| def ensure_list(x): | |
| if isinstance(x, list): | |
| return x | |
| else: | |
| return [x] | |
| def construct_pipeline(): | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| ret = get_pipeline(device=device) | |
| return ret | |
| def run_pipeline(question, document, top_k): | |
| return construct_pipeline()(question=question, **document.context, top_k=top_k) | |
| # TODO: Move into docquery | |
| # TODO: Support words past the first page (or window?) | |
| def lift_word_boxes(document): | |
| return document.context["image"][0][1] | |
| def expand_bbox(word_boxes): | |
| if len(word_boxes) == 0: | |
| return None | |
| min_x, min_y, max_x, max_y = zip(*[x[1] for x in word_boxes]) | |
| return [min(min_x), min(min_y), max(max_x), max(max_y)] | |
| # LayoutLM boxes are normalized to 0, 1000 | |
| def normalize_bbox(box, width, height): | |
| pct = [c / 1000 for c in box] | |
| return [pct[0] * width, pct[1] * height, pct[2] * width, pct[3] * height] | |
| st.markdown("# DocQuery: Query Documents w/ NLP") | |
| if "document" not in st.session_state: | |
| st.session_state["document"] = None | |
| input_type = st.radio("Pick an input type", ["Upload", "URL"], horizontal=True) | |
| def load_file_cb(): | |
| if st.session_state.file_input is None: | |
| return | |
| file = st.session_state.file_input | |
| with loading_placeholder: | |
| with st.spinner("Processing..."): | |
| document = load_bytes(file, file.name) | |
| _ = document.context | |
| st.session_state.document = document | |
| def load_url_cb(): | |
| if st.session_state.url_input is None: | |
| return | |
| url = st.session_state.url_input | |
| with loading_placeholder: | |
| with st.spinner("Downloading..."): | |
| document = load_document(url) | |
| with st.spinner("Processing..."): | |
| _ = document.context | |
| st.session_state.document = document | |
| if input_type == "Upload": | |
| file = st.file_uploader( | |
| "Upload a PDF or Image document", key="file_input", on_change=load_file_cb | |
| ) | |
| elif input_type == "URL": | |
| # url = st.text_input("URL", "", on_change=load_url_callback, key="url_input") | |
| url = st.text_input("URL", "", key="url_input", on_change=load_url_cb) | |
| question = st.text_input("QUESTION", "") | |
| document = st.session_state.document | |
| loading_placeholder = st.empty() | |
| if document is not None: | |
| col1, col2 = st.columns([3, 1]) | |
| image = document.preview | |
| colors = ["blue", "red", "green"] | |
| if document is not None and question is not None and len(question) > 0: | |
| col2.header("Answers") | |
| predictions = run_pipeline(question=question, document=document, top_k=1) | |
| word_boxes = lift_word_boxes(document) | |
| image = image.copy() | |
| draw = ImageDraw.Draw(image) | |
| for i, p in enumerate(ensure_list(predictions)): | |
| col2.markdown(f"#### { p['answer'] }: ({round(p['score'] * 100, 1)}%)") | |
| x1, y1, x2, y2 = normalize_bbox( | |
| expand_bbox(word_boxes[p["start"] : p["end"] + 1]), | |
| image.width, | |
| image.height, | |
| ) | |
| draw.rectangle(((x1, y1), (x2, y2)), outline=colors[i]) | |
| if document is not None: | |
| col1.image(image, use_column_width=True) | |
| "DocQuery uses LayoutLMv1 fine-tuned on DocVQA, a document visual question answering dataset, as well as SQuAD, which boosts its English-language comprehension. To use it, simply upload an image or PDF, type a question, and click 'submit', or click one of the examples to load them." | |
| "[Github Repo](https://github.com/impira/docquery)" | |