Update app.py

app.py

@@ -1,444 +1,10 @@
-import os
-
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-from PIL import Image, ImageDraw
-import traceback
-
-import pandas as pd
-
-import gradio as gr
-
-import torch
-from docquery import pipeline
-from docquery.document import load_document, ImageDocumenta
-from docquery.ocr_reader import get_ocr_reader
-
-
-def ensure_list(x):
-    if isinstance(x, list):
-        return x
-    else:
-        return [x]
-
-
-CHECKPOINTS = {
-    "LayoutLMv1 🦉": "impira/layoutlm-document-qa",
-    "LayoutLMv1 for Invoices 💸": "impira/layoutlm-invoices",
-    "Donut 🍩": "naver-clova-ix/donut-base-finetuned-docvqa",
-    "ggml-Vicuna": "eachadea/ggml-vicuna-13b-1.1",
-}
-
-PIPELINES = {}
-
-
-def construct_pipeline(task, model):
-    global PIPELINES
-    if model in PIPELINES:
-        return PIPELINES[model]
-
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    ret = pipeline(task=task, model=CHECKPOINTS[model], device=device)
-    PIPELINES[model] = ret
-    return ret
-
-
-def run_pipeline(model, question, document, top_k):
-    pipeline = construct_pipeline("document-question-answering", model)
-    return 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, page):
-    return document.context["image"][page][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])
-    min_x, min_y, max_x, max_y = [min(min_x), min(min_y), max(max_x), max(max_y)]
-    return [min_x, min_y, max_x, max_y]
-
-
-# LayoutLM boxes are normalized to 0, 1000
-def normalize_bbox(box, width, height, padding=0.005):
-    min_x, min_y, max_x, max_y = [c / 1000 for c in box]
-    if padding != 0:
-        min_x = max(0, min_x - padding)
-        min_y = max(0, min_y - padding)
-        max_x = min(max_x + padding, 1)
-        max_y = min(max_y + padding, 1)
-    return [min_x * width, min_y * height, max_x * width, max_y * height]
-
-
-examples = [
-    [
-        "invoice.png",
-        "What is the invoice number?",
-    ],
-    [
-        "contract.jpeg",
-        "What is the purchase amount?",
-    ],
-    [
-        "statement.png",
-        "What are net sales for 2020?",
-    ],
-    [
-        "SaleData.xlsx",
-        "What is the highest sale amount of televsion in east region?",
-
-    ]
-    #    [
-    #        "docquery.png",
-    #        "How many likes does the space have?",
-    #    ],
-    #    [
-    #        "hacker_news.png",
-    #        "What is the title of post number 5?",
-    #    ],
-]
-
-question_files = {
-    "What are net sales for 2020?": "statement.pdf",
-    "How many likes does the space have?": "https://huggingface.co/spaces/impira/docquery",
-    "What is the title of post number 5?": "https://news.ycombinator.com",
-}
-
-
-def process_path(path):
-    error = None
-    if path:
-        try:
-            document = load_document(path)
-            return (
-                document,
-                gr.update(visible=True, value=document.preview),
-                gr.update(visible=True),
-                gr.update(visible=False, value=None),
-                gr.update(visible=False, value=None),
-                None,
-            )
-        except Exception as e:
-            traceback.print_exc()
-            error = str(e)
-    return (
-        None,
-        gr.update(visible=False, value=None),
-        gr.update(visible=False),
-        gr.update(visible=False, value=None),
-        gr.update(visible=False, value=None),
-        gr.update(visible=True, value=error) if error is not None else None,
-        None,
-    )
-
-
-def process_upload(file, excel_file):
-    if file:
-        return process_path(file.name)
-    if excel_file:
-        excel_data = pd.read_excel(excel_file)
-        return process_path(excel_file.name)
-    else:
-        return (
-            None,
-            gr.update(visible=False, value=None),
-            gr.update(visible=False),
-            gr.update(visible=False, value=None),
-            gr.update(visible=False, value=None),
-            None,
-        )
-    
-
-    
-
-
-
-colors = ["#64A087", "green", "black"]
-
-
-def process_question(question, document, model=list(CHECKPOINTS.keys())[0]):
-    if not question or document is None:
-        return None, None, None
-
-    text_value = None
-    predictions = run_pipeline(model, question, document, 3)
-    pages = [x.copy().convert("RGB") for x in document.preview]
-    for i, p in enumerate(ensure_list(predictions)):
-        if i == 0:
-            text_value = p["answer"]
-        else:
-            # Keep the code around to produce multiple boxes, but only show the top
-            # prediction for now
-            break
-
-        if "word_ids" in p:
-            image = pages[p["page"]]
-            draw = ImageDraw.Draw(image, "RGBA")
-            word_boxes = lift_word_boxes(document, p["page"])
-            x1, y1, x2, y2 = normalize_bbox(
-                expand_bbox([word_boxes[i] for i in p["word_ids"]]),
-                image.width,
-                image.height,
-            )
-            draw.rectangle(((x1, y1), (x2, y2)), fill=(0, 255, 0, int(0.4 * 255)))
-
-    return (
-        gr.update(visible=True, value=pages),
-        gr.update(visible=True, value=predictions),
-        gr.update(
-            visible=True,
-            value=text_value,
-        ),
-    )
-
-
-def load_example_document(img, question, model):
-    if img is not None:
-        if question in question_files:
-            document = load_document(question_files[question])
-        else:
-            document = ImageDocument(Image.fromarray(img), get_ocr_reader())
-        preview, answer, answer_text = process_question(question, document, model)
-        return document, question, preview, gr.update(visible=True), answer, answer_text
-    else:
-        return None, None, None, gr.update(visible=False), None, None
-
-
-CSS = """
-#question input {
-    font-size: 16px;
-}
-#url-textbox {
-    padding: 0 !important;
-}
-#short-upload-box .w-full {
-    min-height: 10rem !important;
-}
-/* I think something like this can be used to re-shape
- * the table
- */
-/*
-.gr-samples-table tr {
-    display: inline;
-}
-.gr-samples-table .p-2 {
-    width: 100px;
-}
-*/
-#select-a-file {
-    width: 100%;
-}
-#file-clear {
-    padding-top: 2px !important;
-    padding-bottom: 2px !important;
-    padding-left: 8px !important;
-    padding-right: 8px !important;
-        margin-top: 10px;
-}
-.gradio-container .gr-button-primary {
-    background: linear-gradient(180deg, #CDF9BE 0%, #AFF497 100%);
-    border: 1px solid #B0DCCC;
-    border-radius: 8px;
-    color: #1B8700;
-}
-.gradio-container.dark button#submit-button {
-    background: linear-gradient(180deg, #CDF9BE 0%, #AFF497 100%);
-    border: 1px solid #B0DCCC;
-    border-radius: 8px;
-    color: #1B8700
-}
-
-table.gr-samples-table tr td {
-    border: none;
-    outline: none;
-}
-
-table.gr-samples-table tr td:first-of-type {
-    width: 0%;
-}
-
-div#short-upload-box div.absolute {
-    display: none !important;
-}
-
-gradio-app > div > div > div > div.w-full > div, .gradio-app > div > div > div > div.w-full > div {
-    gap: 0px 2%;
-}
-
-gradio-app div div div div.w-full, .gradio-app div div div div.w-full {
-    gap: 0px;
-}
-
-gradio-app h2, .gradio-app h2 {
-    padding-top: 10px;
-}
-
-#answer {
-    overflow-y: scroll;
-    color: white;
-    background: #666;
-    border-color: #666;
-    font-size: 20px;
-    font-weight: bold;
-}
-
-#answer span {
-    color: white;
-}
-
-#answer textarea {
-    color:white;
-    background: #777;
-    border-color: #777;
-    font-size: 18px;
-}
-
-#url-error input {
-    color: red;
-}
-"""
-
-with gr.Blocks(css=CSS) as demo:
-    gr.Markdown("# Document Query Engine")
-    
-
-    document = gr.Variable()
-    example_question = gr.Textbox(visible=False)
-    example_image = gr.Image(visible=False)
-    excel_upload = gr.File(label="Upload Excel", type="xlsx", elem_id="excel-upload-box")
-
-    excel_process_button = gr.Button("Process Excel", variant="primary", elem_id="excel-process-button")
-
-    with gr.Row(equal_height=True):
-        with gr.Column():
-            with gr.Row():
-                gr.Markdown("## 1. Select a file", elem_id="select-a-file")
-                img_clear_button = gr.Button(
-                    "Clear", variant="secondary", elem_id="file-clear", visible=False
-                )
-            image = gr.Gallery(visible=False)
-            with gr.Row(equal_height=True):
-                with gr.Column():
-                    with gr.Row():
-                        url = gr.Textbox(
-                            show_label=False,
-                            placeholder="URL",
-                            lines=1,
-                            max_lines=1,
-                            elem_id="url-textbox",
-                        )
-                        submit = gr.Button("Get")
-                    url_error = gr.Textbox(
-                        visible=False,
-                        elem_id="url-error",
-                        max_lines=1,
-                        interactive=False,
-                        label="Error",
-                    )
-            gr.Markdown("— or —")
-            upload = gr.File(label=None, interactive=True, elem_id="short-upload-box")
-            gr.Examples(
-                examples=examples,
-                inputs=[example_image, example_question],
-            )
-
-        with gr.Column() as col:
-            gr.Markdown("## 2. Ask a question")
-            question = gr.Textbox(
-                label="Question",
-                placeholder="e.g. What is the invoice number?",
-                lines=1,
-                max_lines=1,
-            )
-            model = gr.Radio(
-                choices=list(CHECKPOINTS.keys()),
-                value=list(CHECKPOINTS.keys())[0],
-                label="Model",
-            )
-
-            with gr.Row():
-                clear_button = gr.Button("Clear", variant="secondary")
-                submit_button = gr.Button(
-                    "Submit", variant="primary", elem_id="submit-button"
-                )
-            with gr.Column():
-                output_text = gr.Textbox(
-                    label="Top Answer", visible=False, elem_id="answer"
-                )
-                output = gr.JSON(label="Output", visible=False)
-
-    for cb in [img_clear_button, clear_button]:
-        cb.click(
-            lambda _: (
-                gr.update(visible=False, value=None),
-                None,
-                gr.update(visible=False, value=None),
-                gr.update(visible=False, value=None),
-                gr.update(visible=False),
-                None,
-                None,
-                None,
-                gr.update(visible=False, value=None),
-                None,
-            ),
-            inputs=clear_button,
-            outputs=[
-                image,
-                document,
-                output,
-                output_text,
-                img_clear_button,
-                example_image,
-                upload,
-                url,
-                url_error,
-                question,
-            ],
-        )
-
-    upload.change(
-        fn=process_upload,
-        inputs=[upload],
-        outputs=[document, image, img_clear_button, output, output_text, url_error],
-    )
-    submit.click(
-        fn=process_path,
-        inputs=[url],
-        outputs=[document, image, img_clear_button, output, output_text, url_error],
-    )
-
-    question.submit(
-        fn=process_question,
-        inputs=[question, document, model],
-        outputs=[image, output, output_text],
-    )
-
-    submit_button.click(
-        process_question,
-        inputs=[question, document, model],
-        outputs=[image, output, output_text],
-    )
-
-    model.change(
-        process_question,
-        inputs=[question, document, model],
-        outputs=[image, output, output_text],
-    )
-
-    example_image.change(
-        fn=load_example_document,
-        inputs=[example_image, example_question, model],
-        outputs=[document, question, image, img_clear_button, output, output_text],
-    )
-    upload.change(
-    fn=process_upload,
-    inputs=[upload, excel_upload],
-    outputs=[document, image_preview, img_clear_button, output, output_text, url_error],
-    )
-
-if __name__ == "__main__":
-    demo.launch(enable_queue=False)
+The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.
+Moving 0 files to the new cache system
+
+0it [00:00, ?it/s]
+0it [00:00, ?it/s]
+image-classification is already registered. Overwriting pipeline for task image-classification...
+Traceback (most recent call last):
+  File "/home/user/app/app.py", line 14, in <module>
+    from docquery.document import load_document, ImageDocumenta
+ImportError: cannot import name 'ImageDocumenta' from 'docquery.document' (/home/user/.local/lib/python3.10/site-packages/docquery/document.py)