276 lines
8.7 KiB
Python
276 lines
8.7 KiB
Python
import os
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import io
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import re
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import base64
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import tempfile
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import shutil
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import streamlit as st
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from PIL import Image
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from streamlit_paste_button import paste_image_button as pbutton
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from onnxruntime import InferenceSession
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from models.thrid_party.paddleocr.infer import predict_det, predict_rec
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from models.thrid_party.paddleocr.infer import utility
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from models.utils import mix_inference
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from models.det_model.inference import PredictConfig
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from models.ocr_model.model.TexTeller import TexTeller
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from models.ocr_model.utils.inference import inference as latex_recognition
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from models.ocr_model.utils.to_katex import to_katex
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st.set_page_config(page_title="TexTeller", page_icon="🧮")
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html_string = '''
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<h1 style="color: black; text-align: center;">
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<img src="https://raw.githubusercontent.com/OleehyO/TexTeller/main/assets/fire.svg" width="100">
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𝚃𝚎𝚡𝚃𝚎𝚕𝚕𝚎𝚛
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<img src="https://raw.githubusercontent.com/OleehyO/TexTeller/main/assets/fire.svg" width="100">
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</h1>
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'''
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suc_gif_html = '''
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<h1 style="color: black; text-align: center;">
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<img src="https://slackmojis.com/emojis/90621-clapclap-e/download" width="50">
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<img src="https://slackmojis.com/emojis/90621-clapclap-e/download" width="50">
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<img src="https://slackmojis.com/emojis/90621-clapclap-e/download" width="50">
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</h1>
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'''
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fail_gif_html = '''
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<h1 style="color: black; text-align: center;">
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<img src="https://slackmojis.com/emojis/51439-allthethings_intensifies/download" >
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<img src="https://slackmojis.com/emojis/51439-allthethings_intensifies/download" >
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<img src="https://slackmojis.com/emojis/51439-allthethings_intensifies/download" >
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</h1>
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'''
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@st.cache_resource
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def get_texteller(use_onnx, accelerator):
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return TexTeller.from_pretrained(
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os.environ['CHECKPOINT_DIR'], use_onnx=use_onnx, onnx_provider=accelerator
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)
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@st.cache_resource
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def get_tokenizer():
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return TexTeller.get_tokenizer(os.environ['TOKENIZER_DIR'])
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@st.cache_resource
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def get_det_models(accelerator):
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infer_config = PredictConfig("./models/det_model/model/infer_cfg.yml")
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latex_det_model = InferenceSession(
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"./models/det_model/model/rtdetr_r50vd_6x_coco.onnx",
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providers=['CUDAExecutionProvider'] if accelerator == 'cuda' else ['CPUExecutionProvider'],
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)
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return infer_config, latex_det_model
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@st.cache_resource()
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def get_ocr_models(accelerator):
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use_gpu = accelerator == 'cuda'
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SIZE_LIMIT = 20 * 1024 * 1024
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det_model_dir = "./models/thrid_party/paddleocr/checkpoints/det/default_model.onnx"
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rec_model_dir = "./models/thrid_party/paddleocr/checkpoints/rec/default_model.onnx"
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# The CPU inference of the detection model will be faster than the GPU inference (in onnxruntime)
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det_use_gpu = False
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rec_use_gpu = use_gpu and not (os.path.getsize(rec_model_dir) < SIZE_LIMIT)
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paddleocr_args = utility.parse_args()
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paddleocr_args.use_onnx = True
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paddleocr_args.det_model_dir = det_model_dir
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paddleocr_args.rec_model_dir = rec_model_dir
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paddleocr_args.use_gpu = det_use_gpu
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detector = predict_det.TextDetector(paddleocr_args)
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paddleocr_args.use_gpu = rec_use_gpu
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recognizer = predict_rec.TextRecognizer(paddleocr_args)
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return [detector, recognizer]
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def get_image_base64(img_file):
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buffered = io.BytesIO()
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img_file.seek(0)
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img = Image.open(img_file)
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img.save(buffered, format="PNG")
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return base64.b64encode(buffered.getvalue()).decode()
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def on_file_upload():
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st.session_state["UPLOADED_FILE_CHANGED"] = True
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def change_side_bar():
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st.session_state["CHANGE_SIDEBAR_FLAG"] = True
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if "start" not in st.session_state:
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st.session_state["start"] = 1
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st.toast('Hooray!', icon='🎉')
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if "UPLOADED_FILE_CHANGED" not in st.session_state:
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st.session_state["UPLOADED_FILE_CHANGED"] = False
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if "CHANGE_SIDEBAR_FLAG" not in st.session_state:
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st.session_state["CHANGE_SIDEBAR_FLAG"] = False
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if "INF_MODE" not in st.session_state:
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st.session_state["INF_MODE"] = "Formula recognition"
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############################## <sidebar> ##############################
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with st.sidebar:
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num_beams = 1
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st.markdown("# 🔨️ Config")
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st.markdown("")
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inf_mode = st.selectbox(
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"Inference mode",
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("Formula recognition", "Paragraph recognition"),
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on_change=change_side_bar,
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)
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num_beams = st.number_input(
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'Number of beams', min_value=1, max_value=20, step=1, on_change=change_side_bar
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)
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device = st.radio("device", ("cpu", "cuda", "mps"), on_change=change_side_bar)
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st.markdown("## Seedup")
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use_onnx = st.toggle("ONNX Runtime ")
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############################## </sidebar> ##############################
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################################ <page> ################################
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texteller = get_texteller(use_onnx, device)
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tokenizer = get_tokenizer()
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latex_rec_models = [texteller, tokenizer]
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if inf_mode == "Paragraph recognition":
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infer_config, latex_det_model = get_det_models(device)
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lang_ocr_models = get_ocr_models(device)
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st.markdown(html_string, unsafe_allow_html=True)
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uploaded_file = st.file_uploader(" ", type=['jpg', 'png'], on_change=on_file_upload)
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paste_result = pbutton(
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label="📋 Paste an image",
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background_color="#5BBCFF",
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hover_background_color="#3498db",
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)
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st.write("")
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if st.session_state["CHANGE_SIDEBAR_FLAG"] is True:
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st.session_state["CHANGE_SIDEBAR_FLAG"] = False
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elif uploaded_file or paste_result.image_data is not None:
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if st.session_state["UPLOADED_FILE_CHANGED"] is False and paste_result.image_data is not None:
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uploaded_file = io.BytesIO()
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paste_result.image_data.save(uploaded_file, format='PNG')
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uploaded_file.seek(0)
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if st.session_state["UPLOADED_FILE_CHANGED"] is True:
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st.session_state["UPLOADED_FILE_CHANGED"] = False
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img = Image.open(uploaded_file)
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temp_dir = tempfile.mkdtemp()
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png_file_path = os.path.join(temp_dir, 'image.png')
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img.save(png_file_path, 'PNG')
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with st.container(height=300):
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img_base64 = get_image_base64(uploaded_file)
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st.markdown(
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f"""
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<style>
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.centered-container {{
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text-align: center;
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}}
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.centered-image {{
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display: block;
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margin-left: auto;
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margin-right: auto;
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max-height: 350px;
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max-width: 100%;
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}}
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</style>
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<div class="centered-container">
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<img src="data:image/png;base64,{img_base64}" class="centered-image" alt="Input image">
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</div>
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""",
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unsafe_allow_html=True,
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)
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st.markdown(
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f"""
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<style>
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.centered-container {{
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text-align: center;
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}}
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</style>
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<div class="centered-container">
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<p style="color:gray;">Input image ({img.height}✖️{img.width})</p>
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</div>
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""",
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unsafe_allow_html=True,
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)
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st.write("")
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with st.spinner("Predicting..."):
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if inf_mode == "Formula recognition":
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TexTeller_result = latex_recognition(
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texteller, tokenizer, [png_file_path], accelerator=device, num_beams=num_beams
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)[0]
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katex_res = to_katex(TexTeller_result)
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else:
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katex_res = mix_inference(
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png_file_path,
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infer_config,
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latex_det_model,
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lang_ocr_models,
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latex_rec_models,
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device,
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num_beams,
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)
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st.success('Completed!', icon="✅")
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st.markdown(suc_gif_html, unsafe_allow_html=True)
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st.text_area(":blue[*** 𝑃r𝑒d𝑖c𝑡e𝑑 𝑓o𝑟m𝑢l𝑎 ***]", katex_res, height=150)
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if inf_mode == "Formula recognition":
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st.latex(katex_res)
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elif inf_mode == "Paragraph recognition":
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mixed_res = re.split(r'(\$\$.*?\$\$)', katex_res)
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for text in mixed_res:
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if text.startswith('$$') and text.endswith('$$'):
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st.latex(text[2:-2])
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else:
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st.markdown(text)
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st.write("")
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st.write("")
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with st.expander(":star2: :gray[Tips for better results]"):
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st.markdown('''
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* :mag_right: Use a clear and high-resolution image.
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* :scissors: Crop images as accurately as possible.
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* :jigsaw: Split large multi line formulas into smaller ones.
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* :page_facing_up: Use images with **white background and black text** as much as possible.
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* :book: Use a font with good readability.
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''')
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shutil.rmtree(temp_dir)
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paste_result.image_data = None
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################################ </page> ################################
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