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import os
import sys
if "APP_PATH" in os.environ:
app_path = os.path.abspath(os.environ["APP_PATH"])
if os.getcwd() != app_path:
# fix sys.path for import
os.chdir(app_path)
if app_path not in sys.path:
sys.path.append(app_path)
import io
import tempfile
from typing import List
import pypdfium2
import gradio as gr
from surya.models import load_predictors
from surya.debug.draw import draw_polys_on_image, draw_bboxes_on_image
from surya.debug.text import draw_text_on_image
from PIL import Image
from surya.recognition.languages import CODE_TO_LANGUAGE, replace_lang_with_code
from surya.table_rec import TableResult
from surya.detection import TextDetectionResult
from surya.recognition import OCRResult
from surya.layout import LayoutResult
from surya.settings import settings
from surya.common.util import rescale_bbox, expand_bbox
# just copy from streamlit_app.py
def run_ocr_errors(pdf_file, page_count, sample_len=512, max_samples=10, max_pages=15):
from pdftext.extraction import plain_text_output
with tempfile.NamedTemporaryFile(suffix=".pdf") as f:
f.write(pdf_file.getvalue())
f.seek(0)
# Sample the text from the middle of the PDF
page_middle = page_count // 2
page_range = range(max(page_middle - max_pages, 0), min(page_middle + max_pages, page_count))
text = plain_text_output(f.name, page_range=page_range)
sample_gap = len(text) // max_samples
if len(text) == 0 or sample_gap == 0:
return "This PDF has no text or very little text", ["no text"]
if sample_gap < sample_len:
sample_gap = sample_len
# Split the text into samples for the model
samples = []
for i in range(0, len(text), sample_gap):
samples.append(text[i:i + sample_len])
results = predictors["ocr_error"](samples)
label = "This PDF has good text."
if results.labels.count("bad") / len(results.labels) > .2:
label = "This PDF may have garbled or bad OCR text."
return label, results.labels
# just copy from streamlit_app.py
def inline_detection(img) -> (Image.Image, TextDetectionResult):
text_pred = predictors["detection"]([img])[0]
text_boxes = [p.bbox for p in text_pred.bboxes]
inline_pred = predictors["inline_detection"]([img], [text_boxes], include_maps=True)[0]
inline_polygons = [p.polygon for p in inline_pred.bboxes]
det_img = draw_polys_on_image(inline_polygons, img.copy(), color='blue')
return det_img, text_pred, inline_pred
# just copy from streamlit_app.py `name 'inline_pred' is not defined`
def text_detection(img) -> (Image.Image, TextDetectionResult):
text_pred = predictors["detection"]([img])[0]
text_polygons = [p.polygon for p in text_pred.bboxes]
det_img = draw_polys_on_image(text_polygons, img.copy())
return det_img, text_pred #, inline_pred
# just copy from streamlit_app.py
def layout_detection(img) -> (Image.Image, LayoutResult):
pred = predictors["layout"]([img])[0]
polygons = [p.polygon for p in pred.bboxes]
labels = [f"{p.label}-{p.position}" for p in pred.bboxes]
layout_img = draw_polys_on_image(polygons, img.copy(), labels=labels, label_font_size=18)
return layout_img, pred
# just copy from streamlit_app.py
def table_recognition(img, highres_img, skip_table_detection: bool) -> (Image.Image, List[TableResult]):
if skip_table_detection:
layout_tables = [(0, 0, highres_img.size[0], highres_img.size[1])]
table_imgs = [highres_img]
else:
_, layout_pred = layout_detection(img)
layout_tables_lowres = [l.bbox for l in layout_pred.bboxes if l.label in ["Table", "TableOfContents"]]
table_imgs = []
layout_tables = []
for tb in layout_tables_lowres:
highres_bbox = rescale_bbox(tb, img.size, highres_img.size)
# Slightly expand the box
highres_bbox = expand_bbox(highres_bbox)
table_imgs.append(
highres_img.crop(highres_bbox)
)
layout_tables.append(highres_bbox)
table_preds = predictors["table_rec"](table_imgs)
table_img = img.copy()
for results, table_bbox in zip(table_preds, layout_tables):
adjusted_bboxes = []
labels = []
colors = []
for item in results.cells:
adjusted_bboxes.append([
(item.bbox[0] + table_bbox[0]),
(item.bbox[1] + table_bbox[1]),
(item.bbox[2] + table_bbox[0]),
(item.bbox[3] + table_bbox[1])
])
labels.append(item.label)
if "Row" in item.label:
colors.append("blue")
else:
colors.append("red")
table_img = draw_bboxes_on_image(adjusted_bboxes, highres_img, labels=labels, label_font_size=18, color=colors)
return table_img, table_preds
# just copy from streamlit_app.py
def ocr(img, highres_img, langs: List[str]) -> (Image.Image, OCRResult):
replace_lang_with_code(langs)
img_pred = predictors["recognition"]([img], [langs], predictors["detection"], highres_images=[highres_img])[0]
bboxes = [l.bbox for l in img_pred.text_lines]
text = [l.text for l in img_pred.text_lines]
rec_img = draw_text_on_image(bboxes, text, img.size, langs)
return rec_img, img_pred
def open_pdf(pdf_file):
return pypdfium2.PdfDocument(pdf_file)
def page_counter(pdf_file):
doc = open_pdf(pdf_file)
doc_len = len(doc)
doc.close()
return doc_len
def get_page_image(pdf_file, page_num, dpi=settings.IMAGE_DPI):
doc = open_pdf(pdf_file)
renderer = doc.render(
pypdfium2.PdfBitmap.to_pil,
page_indices=[page_num - 1],
scale=dpi / 72,
)
png = list(renderer)[0]
png_image = png.convert("RGB")
doc.close()
return png_image
def get_uploaded_image(in_file):
return Image.open(in_file).convert("RGB")
# Load models if not already loaded in reload mode
predictors = load_predictors()
with gr.Blocks(title="Surya") as demo:
gr.Markdown("""
# Surya OCR Demo
This app will let you try surya, a multilingual OCR model. It supports text detection + layout analysis in any language, and text recognition in 90+ languages.
Notes:
- This works best on documents with printed text.
- Preprocessing the image (e.g. increasing contrast) can improve results.
- If OCR doesn't work, try changing the resolution of your image (increase if below 2048px width, otherwise decrease).
- This supports 90+ languages, see [here](https://github.com/VikParuchuri/surya/tree/master/surya/languages.py) for a full list.
Find the project [here](https://github.com/VikParuchuri/surya).
""")
with gr.Row():
with gr.Column():
in_file = gr.File(label="PDF file or image:", file_types=[".pdf", ".png", ".jpg", ".jpeg", ".gif", ".webp"])
in_num = gr.Slider(label="Page number", minimum=1, maximum=100, value=1, step=1)
in_img = gr.Image(label="Select page of Image", type="pil", sources=None)
text_det_btn = gr.Button("Run Text Detection")
inline_det_btn = gr.Button("Run Inline Math Detection")
layout_det_btn = gr.Button("Run Layout Analysis")
lang_dd = gr.Dropdown(label="Languages", choices=sorted(list(CODE_TO_LANGUAGE.values())), multiselect=True, max_choices=4, info="Select the languages in the image (if known) to improve OCR accuracy. Optional.")
text_rec_btn = gr.Button("Run OCR")
use_pdf_boxes_ckb = gr.Checkbox(label="Use PDF table boxes", value=True, info="Table recognition only: Use the bounding boxes from the PDF file vs text detection model.")
skip_table_detection_ckb = gr.Checkbox(label="Skip table detection", value=False, info="Table recognition only: Skip table detection and treat the whole image/page as a table.")
table_rec_btn = gr.Button("Run Table Rec")
ocr_errors_btn = gr.Button("Run bad PDF text detection")
with gr.Column():
result_img = gr.Image(label="Result image")
result_json = gr.JSON(label="Result json")
def show_image(file, num=1):
if file.endswith('.pdf'):
count = page_counter(file)
img = get_page_image(file, num, settings.IMAGE_DPI)
return [
gr.update(visible=True, maximum=count),
gr.update(value=img)]
else:
img = get_uploaded_image(file)
return [
gr.update(visible=False),
gr.update(value=img)]
in_file.upload(
fn=show_image,
inputs=[in_file],
outputs=[in_num, in_img],
)
in_num.change(
fn=show_image,
inputs=[in_file, in_num],
outputs=[in_num, in_img],
)
# Run Text Detection
def text_det_img(pil_image):
det_img, text_pred = text_detection(pil_image)
return det_img, text_pred.model_dump(exclude=["heatmap", "affinity_map"])
text_det_btn.click(
fn=text_det_img,
inputs=[in_img],
outputs=[result_img, result_json]
)
def inline_det_img(pil_image):
det_img, text_pred, inline_pred = inline_detection(pil_image)
json = {
"text": text_pred.model_dump(exclude=["heatmap", "affinity_map"]),
"inline": inline_pred.model_dump(exclude=["heatmap", "affinity_map"])
}
return det_img, json
inline_det_btn.click(
fn=inline_det_img,
inputs=[in_img],
outputs=[result_img, result_json]
)
# Run layout
def layout_det_img(pil_image):
layout_img, pred = layout_detection(pil_image)
return layout_img, pred.model_dump(exclude=["segmentation_map"])
layout_det_btn.click(
fn=layout_det_img,
inputs=[in_img],
outputs=[result_img, result_json]
)
# Run OCR
def text_rec_img(pil_image, in_file, page_number, languages):
if in_file.endswith('.pdf'):
pil_image_highres = get_page_image(in_file, page_number, dpi=settings.IMAGE_DPI_HIGHRES)
else:
pil_image_highres = pil_image
rec_img, pred = ocr(pil_image, pil_image_highres, languages)
return rec_img, pred.model_dump()
text_rec_btn.click(
fn=text_rec_img,
inputs=[in_img, in_file, in_num, lang_dd],
outputs=[result_img, result_json]
)
# Run Table Recognition
def table_rec_img(pil_image, in_file, page_number, use_pdf_boxes, skip_table_detection):
if in_file.endswith('.pdf'):
pil_image_highres = get_page_image(in_file, page_number, dpi=settings.IMAGE_DPI_HIGHRES)
else:
pil_image_highres = pil_image
table_img, pred = table_recognition(pil_image, pil_image_highres, skip_table_detection)
return table_img, [p.model_dump() for p in pred]
table_rec_btn.click(
fn=table_rec_img,
inputs=[in_img, in_file, in_num, use_pdf_boxes_ckb, skip_table_detection_ckb],
outputs=[result_img, result_json]
)
# Run bad PDF text detection
def ocr_errors_pdf(file, page_count, sample_len=512, max_samples=10, max_pages=15):
if file.endswith('.pdf'):
count = page_counter(file)
else:
raise gr.Error("This feature only works with PDFs.", duration=5)
label, results = run_ocr_errors(io.BytesIO(open(file.name, "rb").read()), count)
return gr.update(label="Result json:" + label, value=results)
ocr_errors_btn.click(
fn=ocr_errors_pdf,
inputs=[in_file, in_num, use_pdf_boxes_ckb, skip_table_detection_ckb],
outputs=[result_json]
)
if __name__ == "__main__":
demo.launch()
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