Merge branch 'pre_release' into dev

2024-04-17 10:30:09 +00:00
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 **/.DS_Store
 **/__pycache__
 **/.vscode
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 **/*cache
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 **/data
 **/logs
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 **/*cache
--- a/README.md
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 📄 English | <a href="./assets/README_zh.md">中文</a>
 <div align="center">
    <h1>
        <img src="./assets/fire.svg" width=30, height=30> 
        𝚃𝚎𝚡𝚃𝚎𝚕𝚕𝚎𝚛
        <img src="./assets/fire.svg" width=30, height=30>
    </h1>
    <p align="center">
        🤗 <a href="https://huggingface.co/OleehyO/TexTeller"> Hugging Face</a>
    </p>
    <!-- <p align="center">
        <img src="./assets/web_demo.gif" alt="TexTeller_demo" width=800>
    </p> -->
 </div>
 https://github.com/OleehyO/TexTeller/assets/56267907/b23b2b2e-a663-4abb-b013-bd47238d513b
 TexTeller is an end-to-end formula recognition model based on ViT, capable of converting images into corresponding LaTeX formulas.
 TexTeller was trained with ~~550K~~7.5M image-formula pairs (dataset available [here](https://huggingface.co/datasets/OleehyO/latex-formulas)), compared to [LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR) which used a 100K dataset, TexTeller has **stronger generalization abilities** and **higher accuracy**, covering most use cases (**except for scanned images and handwritten formulas**).
 > ~~We will soon release a TexTeller checkpoint trained on a 7.5M dataset~~
 ## 🔄 Change Log
 * 📮[2024-03-25] TexTeller 2.0 released! The training data for TexTeller 2.0 has been increased to 7.5M (about **15 times more** than TexTeller 1.0 and also improved in data quality). The trained TexTeller 2.0 demonstrated **superior performance** in the test set, especially in recognizing rare symbols, complex multi-line formulas, and matrices.
    > [There](./assets/test.pdf) are more test images here and a horizontal comparison of recognition models from different companies.
 * 📮[2024-04-12] Trained a **formula detection model**, thereby enhancing the capability to detect and recognize formulas in entire documents (whole-image inference)!
 ## 🔑 Prerequisites
 python=3.10
 [pytorch](https://pytorch.org/get-started/locally/)
 > [!WARNING]
 > Only CUDA versions >= 12.0 have been fully tested, so it is recommended to use CUDA version >= 12.0
 ## 🚀 Getting Started
 1. Clone the repository:
    ```bash
    git clone https://github.com/OleehyO/TexTeller
    ```
 2. [Installing pytorch](https://pytorch.org/get-started/locally/#start-locally) 
 3. Install the project's dependencies:
    ```bash
    pip install -r requirements.txt
    ```
 4. Enter the `TexTeller/src` directory and run the following command in the terminal to start inference:
    ```bash
    python inference.py -img "/path/to/image.{jpg,png}" 
    # use --inference-mode option to enable GPU(cuda or mps) inference
    #+e.g. python inference.py -img "./img.jpg" --inference-mode cuda
    ```
 > [!NOTE]
 > The first time you run it, the required checkpoints will be downloaded from Hugging Face
 ## 🌐 Web Demo
 Go to the `TexTeller/src` directory and run the following command:
 ```bash
 ./start_web.sh
 ```
 Enter `http://localhost:8501` in a browser to view the web demo.
 > [!NOTE]
 > If you are Windows user, please run the `start_web.bat` file instead.
 ## 🧠 Full Image Inference
 TexTeller also supports **formula detection and recognition** on full images, allowing for the detection of formulas throughout the image, followed by batch recognition of the formulas.
 ### Download Weights
 English documentation formula detection [[link](https://huggingface.co/TonyLee1256/texteller_det/resolve/main/rtdetr_r50vd_6x_coco_trained_on_IBEM_en_papers.onnx?download=true)]: Trained on 8272 images from the [IBEM dataset](https://zenodo.org/records/4757865).
 Chinese documentation formula detection [[link](https://huggingface.co/TonyLee1256/texteller_det/blob/main/rtdetr_r50vd_6x_coco_trained_on_cn_textbook.onnx)]: Trained on 2560 Chinese textbook images (100+ layouts).
 ### Formula Detection
 Run the following command in the `TexTeller/src` directory:
 ```bash
 python infer_det.py
 ```
 Detects all formulas in the full image, and the results are saved in `TexTeller/src/subimages`.
 <div align="center">
    <img src="./assets/det_rec.png" width=400> 
 </div>
 ### Batch Formula Recognition
 After **formula detection**, run the following command in the `TexTeller/src` directory:
 ```shell
 python rec_infer_from_crop_imgs.py
 ```
 This will use the results of the previous formula detection to perform batch recognition on all cropped formulas, saving the recognition results as txt files in `TexTeller/src/results`.
 ## 📡 API Usage
 We use [ray serve](https://github.com/ray-project/ray) to provide an API interface for TexTeller, allowing you to integrate TexTeller into your own projects. To start the server, you first need to enter the `TexTeller/src` directory and then run the following command:
 ```bash
 python server.py  # default settings
 ```
 | Parameter | Description |
 | --- | --- |
 | `-ckpt` | The path to the weights file, *default is TexTeller's pretrained weights*.|
 | `-tknz` | The path to the tokenizer, *default is TexTeller's tokenizer*.|
 | `-port` | The server's service port, *default is 8000*. |
 | `--inference-mode` | Whether to use GPU(cuda or mps) for inference, *default is CPU*. |
 | `--num_beams` | The number of beams for beam search, *default is 1*. |
 | `--num_replicas` | The number of service replicas to run on the server, *default is 1 replica*. You can use more replicas to achieve greater throughput.|
 | `--ncpu_per_replica` | The number of CPU cores used per service replica, *default is 1*. |
 | `--ngpu_per_replica` | The number of GPUs used per service replica, *default is 1*. You can set this value between 0 and 1 to run multiple service replicas on one GPU to share the GPU, thereby improving GPU utilization. (Note, if --num_replicas is 2, --ngpu_per_replica is 0.7, then 2 GPUs must be available) |
 > [!NOTE]
 > A client demo can be found at `TexTeller/client/demo.py`, you can refer to `demo.py` to send requests to the server
 ## 🏋️‍♂️ Training
 ### Dataset
 We provide an example dataset in the `TexTeller/src/models/ocr_model/train/dataset` directory, you can place your own images in the `images` directory and annotate each image with its corresponding formula in `formulas.jsonl`.
 After preparing your dataset, you need to **change the `DIR_URL` variable to your own dataset's path** in `.../dataset/loader.py`
 ### Retraining the Tokenizer
 If you are using a different dataset, you might need to retrain the tokenizer to obtain a different dictionary. After configuring your dataset, you can train your own tokenizer with the following command:
 1. In `TexTeller/src/models/tokenizer/train.py`, change `new_tokenizer.save_pretrained('./your_dir_name')` to your custom output directory
    > If you want to use a different dictionary size (default is 10k tokens), you need to change the `VOCAB_SIZE` variable in `TexTeller/src/models/globals.py`
 2. **In the `TexTeller/src` directory**, run the following command:
    ```bash
    python -m models.tokenizer.train
    ```
 ### Training the Model
 To train the model, you need to run the following command in the `TexTeller/src` directory:
 ```bash
 python -m models.ocr_model.train.train
 ```
 You can set your own tokenizer and checkpoint paths in `TexTeller/src/models/ocr_model/train/train.py` (refer to `train.py` for more information). If you are using the same architecture and dictionary as TexTeller, you can also fine-tune TexTeller's default weights with your own dataset.
 In `TexTeller/src/globals.py` and `TexTeller/src/models/ocr_model/train/train_args.py`, you can change the model's architecture and training hyperparameters.
 > [!NOTE]
 > Our training scripts use the [Hugging Face Transformers](https://github.com/huggingface/transformers) library, so you can refer to their [documentation](https://huggingface.co/docs/transformers/v4.32.1/main_classes/trainer#transformers.TrainingArguments) for more details and configurations on training parameters.
 ## 🚧 Limitations
 * Does not support scanned images and PDF document recognition
 * Does not support handwritten formulas
 ## 📅 Plans
 - [x] ~~Train the model with a larger dataset (7.5M samples, coming soon)~~
 - [ ] Recognition of scanned images
 - [ ] PDF document recognition + Support for English and Chinese scenarios
 - [ ] Inference acceleration
 - [ ] ...
 ## ⭐️ Stargazers over time
 [![Stargazers over time](https://starchart.cc/OleehyO/TexTeller.svg?variant=adaptive)](https://starchart.cc/OleehyO/TexTeller)
 ## 💖 Acknowledgments
 Thanks to [LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR) which has brought me a lot of inspiration, and [im2latex-100K](https://zenodo.org/records/56198#.V2px0jXT6eA) which enriches our dataset.
 ## 👥 Contributors
 <a href="https://github.com/OleehyO/TexTeller/graphs/contributors">
   <a href="https://github.com/OleehyO/TexTeller/graphs/contributors">
      <img src="https://contrib.rocks/image?repo=OleehyO/TexTeller" />
   </a>
 </a>
--- a/assets/README_zh.md
+++ b/assets/README_zh.md
@@ -0,0 +1,233 @@
 📄 <a href="../README.md">English</a> | 中文
 <div align="center">
    <h1>
        <img src="./fire.svg" width=30, height=30> 
        𝚃𝚎𝚡𝚃𝚎𝚕𝚕𝚎𝚛
        <img src="./fire.svg" width=30, height=30> 
    </h1>
    <p align="center">
        🤗 <a href="https://huggingface.co/OleehyO/TexTeller">Hugging Face</a>
    </p>
    <!-- <p align="center">
        <img src="./web_demo.gif" alt="TexTeller_demo" width=800>
    </p> -->
 </div>
 https://github.com/OleehyO/TexTeller/assets/56267907/fb17af43-f2a5-47ce-ad1d-101db5fd7fbb
 TexTeller是一个基于ViT的端到端公式识别模型，可以把图片转换为对应的latex公式
 TexTeller用了~~550K~~7.5M的图片-公式对进行训练(数据集可以在[这里](https://huggingface.co/datasets/OleehyO/latex-formulas)获取)，相比于[LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR)(使用了一个100K的数据集)，TexTeller具有**更强的泛化能力**以及**更高的准确率**，可以覆盖大部分的使用场景(**扫描图片，手写公式除外**)。
 > ~~我们马上就会发布一个使用7.5M数据集进行训练的TexTeller checkpoint~~
 ## 🔄 变更信息
 * 📮[2024-03-25] TexTeller2.0发布！TexTeller2.0的训练数据增大到了7.5M(相较于TexTeller1.0**增加了~15倍**并且数据质量也有所改善)。训练后的TexTeller2.0在测试集中展现出了**更加优越的性能**，尤其在生僻符号、复杂多行、矩阵的识别场景中。
  > 在[这里](./test.pdf)有更多的测试图片以及各家识别模型的横向对比。
  >
 * 📮[2024-04-12] 训练了**公式检测模型**，从而增加了对整个文档进行公式检测+公式识别（整图推理）的功能！
 ## 🔑 前置条件
 python=3.10
 [pytorch](https://pytorch.org/get-started/locally/)
 > [!WARNING]
 > 只有CUDA版本>= 12.0被完全测试过，所以最好使用>= 12.0的CUDA版本
 ## 🚀 开搞
 1. 克隆本仓库:
   ```bash
   git clone https://github.com/OleehyO/TexTeller
   ```
 2. [安装pytorch](https://pytorch.org/get-started/locally/#start-locally)
 3. 安装本项目的依赖包:
   ```bash
   pip install -r requirements.txt
   ```
 4. 进入 `TexTeller/src`目录，在终端运行以下命令进行推理:
   ```bash
   python inference.py -img "/path/to/image.{jpg,png}" 
   # use --inference-mode option to enable GPU(cuda or mps) inference
   #+e.g. python inference.py -img "./img.jpg" --inference-mode cuda
   ```
 > [!NOTE]
 > 第一次运行时会在hugging face上下载所需要的checkpoints
 ## ❓ 常见问题：无法连接到Hugging Face
 默认情况下，会在Hugging Face中下载模型权重，**如果你的远端服务器无法连接到Hugging Face**，你可以通过以下命令进行加载：
 1. 安装huggingface hub包
   ```bash
   pip install -U "huggingface_hub[cli]"
   ```
 2. 在能连接Hugging Face的机器上下载模型权重:
   ```bash
   huggingface-cli download OleehyO/TexTeller --include "*.json" "*.bin" "*.txt" --repo-type model --local-dir "your/dir/path"
   ```
 3. 把包含权重的目录上传远端服务器，然后把 `TexTeller/src/models/ocr_model/model/TexTeller.py`中的 `REPO_NAME = 'OleehyO/TexTeller'`修改为 `REPO_NAME = 'your/dir/path'`
 如果你还想在训练模型时开启evaluate，你需要提前下载metric脚本并上传远端服务器：
 1. 在能连接Hugging Face的机器上下载metric脚本
   ```bash
   huggingface-cli download evaluate-metric/google_bleu --repo-type space --local-dir "your/dir/path"
   ```
 2. 把这个目录上传远端服务器，并在 `TexTeller/src/models/ocr_model/utils/metrics.py`中把 `evaluate.load('google_bleu')`改为 `evaluate.load('your/dir/path/google_bleu.py')`
 ## 🌐 网页演示
 进入 `TexTeller/src` 目录，运行以下命令
 ```bash
 ./start_web.sh
 ```
 在浏览器里输入 `http://localhost:8501`就可以看到web demo
 > [!NOTE]
 > 对于Windows用户, 请运行 `start_web.bat`文件.
 ## 🧠 整图推理
 TexTeller还支持对整张图片进行**公式检测+公式识别**，从而对整图公式进行检测，然后进行批公式识别。
 ### 下载权重
 英文文档公式检测 [[link](https://huggingface.co/TonyLee1256/texteller_det/resolve/main/rtdetr_r50vd_6x_coco_trained_on_IBEM_en_papers.onnx?download=true)]：在8272张[IBEM数据集](https://zenodo.org/records/4757865)上训练得到
 中文文档公式检测 [[link](https://huggingface.co/TonyLee1256/texteller_det/blob/main/rtdetr_r50vd_6x_coco_trained_on_cn_textbook.onnx)]：在2560张中文教材数据(100+版式)上训练得到
 ### 公式检测
 `TexTeller/src`目录下运行以下命令
 ```bash
 python infer_det.py
 ```
 对整张图中的所有公式进行检测，结果保存在 `TexTeller/src/subimages`
 <div align="center">
    <img src="det_rec.png" width=400> 
 </div>
 ### 公式批识别
 在进行**公式检测后**， `TexTeller/src`目录下运行以下命令
 ```shell
 python rec_infer_from_crop_imgs.py
 ```
 会基于上一步公式检测的结果，对裁剪出的所有公式进行批量识别，将识别结果在 `TexTeller/src/results`中保存为txt文件。
 ## 📡 API调用
 我们使用[ray serve](https://github.com/ray-project/ray)来对外提供一个TexTeller的API接口，通过使用这个接口，你可以把TexTeller整合到自己的项目里。要想启动server，你需要先进入 `TexTeller/src`目录然后运行以下命令:
 ```bash
 python server.py 
 ```
 | 参数 | 描述 |
 | - | - |
 | `-ckpt` | 权重文件的路径，*默认为TexTeller的预训练权重*。 |
 | `-tknz` | 分词器的路径，*默认为TexTeller的分词器*。 |
 | `-port` | 服务器的服务端口，*默认是8000*。 |
 | `--inference-mode`| 是否使用GPU(cuda或mps)推理，*默认为CPU*。 |
 | `--num_beams` | beam search的beam数量，*默认是1*。 |
 | `--num_replicas`| 在服务器上运行的服务副本数量，*默认1个副本*。你可以使用更多的副本来获取更大的吞吐量。 |
 | `--ncpu_per_replica` | 每个服务副本所用的CPU核心数，*默认为1*。 |
 | `--ngpu_per_replica` | 每个服务副本所用的GPU数量，*默认为1*。你可以把这个值设置成 0~1之间的数，这样会在一个GPU上运行多个服务副本来共享GPU，从而提高GPU的利用率。(注意，如果 --num_replicas 2, --ngpu_per_replica 0.7, 那么就必须要有2个GPU可用) |
 > [!NOTE]
 > 一个客户端demo可以在 `TexTeller/client/demo.py`找到，你可以参考 `demo.py`来给server发送请求
 ## 🏋️‍♂️ 训练
 ### 数据集
 我们在 `TexTeller/src/models/ocr_model/train/dataset`目录中提供了一个数据集的例子，你可以把自己的图片放在 `images`目录然后在 `formulas.jsonl`中为每张图片标注对应的公式。
 准备好数据集后，你需要在 `.../dataset/loader.py`中把 **`DIR_URL`变量改成你自己数据集的路径**
 ### 重新训练分词器
 如果你使用了不一样的数据集，你可能需要重新训练tokenizer来得到一个不一样的字典。配置好数据集后，可以通过以下命令来训练自己的tokenizer：
 1. 在 `TexTeller/src/models/tokenizer/train.py`中，修改 `new_tokenizer.save_pretrained('./your_dir_name')`为你自定义的输出目录
   > 注意：如果要用一个不一样大小的字典(默认1W个token)，你需要在 `TexTeller/src/models/globals.py`中修改 `VOCAB_SIZE`变量
   >
 2. **在 `TexTeller/src` 目录下**运行以下命令:
   ```bash
   python -m models.tokenizer.train
   ```
 ### 训练模型
 要想训练模型, 你需要在 `TexTeller/src`目录下运行以下命令：
 ```bash
 python -m models.ocr_model.train.train
 ```
 你可以在 `TexTeller/src/models/ocr_model/train/train.py`中设置自己的tokenizer和checkpoint路径（请参考 `train.py`）。如果你使用了与TexTeller一样的架构和相同的字典，你还可以用自己的数据集来微调TexTeller的默认权重。
 在 `TexTeller/src/globals.py`和 `TexTeller/src/models/ocr_model/train/train_args.py`中，你可以改变模型的架构以及训练的超参数。
 > [!NOTE]
 > 我们的训练脚本使用了[Hugging Face Transformers](https://github.com/huggingface/transformers)库, 所以你可以参考他们提供的[文档](https://huggingface.co/docs/transformers/v4.32.1/main_classes/trainer#transformers.TrainingArguments)来获取更多训练参数的细节以及配置。
 ## 🚧 不足
 * 不支持扫描图片以及PDF文档识别
 * 不支持手写体公式
 ## 📅 计划
 - [X] ~~使用更大的数据集来训练模型(7.5M样本，即将发布)~~
 - [ ] 扫描图片识别
 - [ ] PDF文档识别 + 中英文场景支持
 - [ ] 推理加速
 - [ ] ...
 ## ⭐️ 观星曲线
 [![Stargazers over time](https://starchart.cc/OleehyO/TexTeller.svg?variant=adaptive)](https://starchart.cc/OleehyO/TexTeller)
 ## 💖 感谢
 Thanks to [LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR) which has brought me a lot of inspiration, and [im2latex-100K](https://zenodo.org/records/56198#.V2px0jXT6eA) which enriches our dataset.
 ## 👥 贡献者
 <a href="https://github.com/OleehyO/TexTeller/graphs/contributors">
   <a href="https://github.com/OleehyO/TexTeller/graphs/contributors">
      <img src="https://contrib.rocks/image?repo=OleehyO/TexTeller" />
   </a>
 </a>
--- a/assets/css.css
+++ b/assets/css.css
@@ -1,157 +0,0 @@
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 </svg>
--- a/assets/test.pdf
+++ b/assets/test.pdf
--- a/assets/web_demo.gif
+++ b/assets/web_demo.gif
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,10 +1,6 @@
 transformers
 datasets
 evaluate
 streamlit
 gradio
 opencv-python
 ray[serve]
 accelerate
@@ -12,4 +8,8 @@ tensorboardX
 nltk
 python-multipart
-augraphy
+augraphy
 onnxruntime
 streamlit==1.30
 streamlit-paste-button
--- a/src/infer_det.py
+++ b/src/infer_det.py
@@ -0,0 +1,197 @@
 import os
 import yaml
 import argparse
 import numpy as np
 import glob
 from onnxruntime import InferenceSession
 from tqdm import tqdm
 from models.det_model.preprocess import Compose
 import cv2
 # 注意：文件名要标准，最好都用下划线
 # Global dictionary
 SUPPORT_MODELS = {
    'YOLO', 'PPYOLOE', 'RCNN', 'SSD', 'Face', 'FCOS', 'SOLOv2', 'TTFNet',
    'S2ANet', 'JDE', 'FairMOT', 'DeepSORT', 'GFL', 'PicoDet', 'CenterNet',
    'TOOD', 'RetinaNet', 'StrongBaseline', 'STGCN', 'YOLOX', 'HRNet', 
    'DETR'
 }
 parser = argparse.ArgumentParser(description=__doc__)
 parser.add_argument("--infer_cfg", type=str, help="infer_cfg.yml",
                    default="./models/det_model/model/infer_cfg.yml"
                    )
 parser.add_argument('--onnx_file', type=str, help="onnx model file path",
                    default="./models/det_model/model/rtdetr_r50vd_6x_coco.onnx"
                    )
 parser.add_argument("--image_dir", type=str)
 parser.add_argument("--image_file", type=str, default='/data/ljm/TexTeller/src/Tr00_0001015-page02.jpg')
 parser.add_argument("--imgsave_dir", type=str, 
                    default="."
                    )
 def get_test_images(infer_dir, infer_img):
    """
    Get image path list in TEST mode
    """
    assert infer_img is not None or infer_dir is not None, \
        "--image_file or --image_dir should be set"
    assert infer_img is None or os.path.isfile(infer_img), \
            "{} is not a file".format(infer_img)
    assert infer_dir is None or os.path.isdir(infer_dir), \
            "{} is not a directory".format(infer_dir)
    # infer_img has a higher priority
    if infer_img and os.path.isfile(infer_img):
        return [infer_img]
    images = set()
    infer_dir = os.path.abspath(infer_dir)
    assert os.path.isdir(infer_dir), \
        "infer_dir {} is not a directory".format(infer_dir)
    exts = ['jpg', 'jpeg', 'png', 'bmp']
    exts += [ext.upper() for ext in exts]
    for ext in exts:
        images.update(glob.glob('{}/*.{}'.format(infer_dir, ext)))
    images = list(images)
    assert len(images) > 0, "no image found in {}".format(infer_dir)
    print("Found {} inference images in total.".format(len(images)))
    return images
 class PredictConfig(object):
    """set config of preprocess, postprocess and visualize
    Args:
        infer_config (str): path of infer_cfg.yml
    """
    def __init__(self, infer_config):
        # parsing Yaml config for Preprocess
        with open(infer_config) as f:
            yml_conf = yaml.safe_load(f)
        self.check_model(yml_conf)
        self.arch = yml_conf['arch']
        self.preprocess_infos = yml_conf['Preprocess']
        self.min_subgraph_size = yml_conf['min_subgraph_size']
        self.label_list = yml_conf['label_list']
        self.use_dynamic_shape = yml_conf['use_dynamic_shape']
        self.draw_threshold = yml_conf.get("draw_threshold", 0.5)
        self.mask = yml_conf.get("mask", False)
        self.tracker = yml_conf.get("tracker", None)
        self.nms = yml_conf.get("NMS", None)
        self.fpn_stride = yml_conf.get("fpn_stride", None)
        # 预定义颜色池
        color_pool = [(0, 255, 0), (255, 0, 0), (0, 0, 255), (255, 255, 0), (0, 255, 255)]
        # 根据label_list动态生成颜色映射
        self.colors = {label: color_pool[i % len(color_pool)] for i, label in enumerate(self.label_list)}
        if self.arch == 'RCNN' and yml_conf.get('export_onnx', False):
            print(
                'The RCNN export model is used for ONNX and it only supports batch_size = 1'
            )
        self.print_config()
    def check_model(self, yml_conf):
        """
        Raises:
            ValueError: loaded model not in supported model type
        """
        for support_model in SUPPORT_MODELS:
            if support_model in yml_conf['arch']:
                return True
        raise ValueError("Unsupported arch: {}, expect {}".format(yml_conf[
            'arch'], SUPPORT_MODELS))
    def print_config(self):
        print('-----------  Model Configuration -----------')
        print('%s: %s' % ('Model Arch', self.arch))
        print('%s: ' % ('Transform Order'))
        for op_info in self.preprocess_infos:
            print('--%s: %s' % ('transform op', op_info['type']))
        print('--------------------------------------------')
 def draw_bbox(image, outputs, infer_config):
    for output in outputs:
        cls_id, score, xmin, ymin, xmax, ymax = output
        if score > infer_config.draw_threshold:
            # 获取类别名
            label = infer_config.label_list[int(cls_id)]
            # 根据类别名获取颜色
            color = infer_config.colors[label]
            cv2.rectangle(image, (int(xmin), int(ymin)), (int(xmax), int(ymax)), color, 2)
            cv2.putText(image, "{}: {:.2f}".format(label, score),
                        (int(xmin), int(ymin - 5)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
    return image
 def predict_image(infer_config, predictor, img_list):
    # load preprocess transforms
    transforms = Compose(infer_config.preprocess_infos)
    errImgList = []
    # Check and create subimg_save_dir if not exist
    subimg_save_dir = os.path.join(FLAGS.imgsave_dir, 'subimages')
    os.makedirs(subimg_save_dir, exist_ok=True)
    # predict image
    for img_path in tqdm(img_list):
        img = cv2.imread(img_path)
        if img is None:
            print(f"Warning: Could not read image {img_path}. Skipping...")
            errImgList.append(img_path)
            continue
        inputs = transforms(img_path)
        inputs_name = [var.name for var in predictor.get_inputs()]
        inputs = {k: inputs[k][None, ] for k in inputs_name}
        outputs = predictor.run(output_names=None, input_feed=inputs)
        print("ONNXRuntime predict: ")
        if infer_config.arch in ["HRNet"]:
            print(np.array(outputs[0]))
        else:
            bboxes = np.array(outputs[0])
            for bbox in bboxes:
                if bbox[0] > -1 and bbox[1] > infer_config.draw_threshold:
                    print(f"{int(bbox[0])} {bbox[1]} "
                          f"{bbox[2]} {bbox[3]} {bbox[4]} {bbox[5]}")
        # Save the subimages (crop from the original image)
        subimg_counter = 1
        for output in np.array(outputs[0]):
            cls_id, score, xmin, ymin, xmax, ymax = output
            if score > infer_config.draw_threshold:
                label = infer_config.label_list[int(cls_id)]
                subimg = img[int(ymin):int(ymax), int(xmin):int(xmax)]
                subimg_filename = f"{os.path.splitext(os.path.basename(img_path))[0]}_{label}_{xmin:.2f}_{ymin:.2f}_{xmax:.2f}_{ymax:.2f}.jpg"
                subimg_path = os.path.join(subimg_save_dir, subimg_filename)
                cv2.imwrite(subimg_path, subimg)
                subimg_counter += 1
        # Draw bounding boxes and save the image with bounding boxes
        img_with_bbox = draw_bbox(img, np.array(outputs[0]), infer_config)
        output_dir = FLAGS.imgsave_dir
        os.makedirs(output_dir, exist_ok=True)
        output_file = os.path.join(output_dir, "output_" + os.path.basename(img_path))
        cv2.imwrite(output_file, img_with_bbox)
    print("ErrorImgs:")
    print(errImgList)
 if __name__ == '__main__':
    FLAGS = parser.parse_args()
    # load image list
    img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
    # load predictor
    predictor = InferenceSession(FLAGS.onnx_file)
    # load infer config
    infer_config = PredictConfig(FLAGS.infer_cfg)
    predict_image(infer_config, predictor, img_list)
--- a/src/inference.py
+++ b/src/inference.py
@@ -19,10 +19,16 @@ if __name__ == '__main__':
        help='path to the input image'
    )
    parser.add_argument(
-        '-cuda', 
+        '--inference-mode', 
-        default=False,
+        type=str,
-        action='store_true',
+        default='cpu',
-        help='use cuda or not'
+        help='Inference mode, select one of cpu, cuda, or mps'
    )
    parser.add_argument(
        '--num-beam', 
        type=int,
        default=1,
        help='number of beam search for decoding'
    )
    # =================  new feature  ==================
    parser.add_argument(
@@ -37,6 +43,7 @@ if __name__ == '__main__':
    # You can use your own checkpoint and tokenizer path.
    print('Loading model and tokenizer...')
    latex_rec_model = TexTeller.from_pretrained()
    latex_rec_model = TexTeller.from_pretrained()
    tokenizer = TexTeller.get_tokenizer()
    print('Model and tokenizer loaded.')
@@ -44,7 +51,7 @@ if __name__ == '__main__':
    img = cv.imread(args.img)
    print('Inference...')
    if not args.mix:
-        res = latex_inference(latex_rec_model, tokenizer, [img], args.cuda)
+        res = latex_inference(latex_rec_model, tokenizer, [img], args.inference_mode, args.num_beam)
        res = to_katex(res[0])
        print(res)
    else:
--- a/src/models/det_model/model/infer_cfg.yml
+++ b/src/models/det_model/model/infer_cfg.yml
@@ -0,0 +1,27 @@
 mode: paddle
 draw_threshold: 0.5
 metric: COCO
 use_dynamic_shape: false
 arch: DETR
 min_subgraph_size: 3
 Preprocess:
 - interp: 2
  keep_ratio: false
  target_size:
  - 640
  - 640
  type: Resize
 - mean:
  - 0.0
  - 0.0
  - 0.0
  norm_type: none
  std:
  - 1.0
  - 1.0
  - 1.0
  type: NormalizeImage
 - type: Permute
 label_list:
 - isolated
 - embedding
--- a/src/models/det_model/preprocess.py
+++ b/src/models/det_model/preprocess.py
@@ -0,0 +1,494 @@
 import numpy as np
 import cv2
 import copy
 def decode_image(img_path):
    with open(img_path, 'rb') as f:
        im_read = f.read()
    data = np.frombuffer(im_read, dtype='uint8')
    im = cv2.imdecode(data, 1)  # BGR mode, but need RGB mode
    im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
    img_info = {
        "im_shape": np.array(
            im.shape[:2], dtype=np.float32),
        "scale_factor": np.array(
            [1., 1.], dtype=np.float32)
    }
    return im, img_info
 class Resize(object):
    """resize image by target_size and max_size
    Args:
        target_size (int): the target size of image
        keep_ratio (bool): whether keep_ratio or not, default true
        interp (int): method of resize
    """
    def __init__(self, target_size, keep_ratio=True, interp=cv2.INTER_LINEAR):
        if isinstance(target_size, int):
            target_size = [target_size, target_size]
        self.target_size = target_size
        self.keep_ratio = keep_ratio
        self.interp = interp
    def __call__(self, im, im_info):
        """
        Args:
            im (np.ndarray): image (np.ndarray)
            im_info (dict): info of image
        Returns:
            im (np.ndarray):  processed image (np.ndarray)
            im_info (dict): info of processed image
        """
        assert len(self.target_size) == 2
        assert self.target_size[0] > 0 and self.target_size[1] > 0
        im_channel = im.shape[2]
        im_scale_y, im_scale_x = self.generate_scale(im)
        im = cv2.resize(
            im,
            None,
            None,
            fx=im_scale_x,
            fy=im_scale_y,
            interpolation=self.interp)
        im_info['im_shape'] = np.array(im.shape[:2]).astype('float32')
        im_info['scale_factor'] = np.array(
            [im_scale_y, im_scale_x]).astype('float32')
        return im, im_info
    def generate_scale(self, im):
        """
        Args:
            im (np.ndarray): image (np.ndarray)
        Returns:
            im_scale_x: the resize ratio of X
            im_scale_y: the resize ratio of Y
        """
        origin_shape = im.shape[:2]
        im_c = im.shape[2]
        if self.keep_ratio:
            im_size_min = np.min(origin_shape)
            im_size_max = np.max(origin_shape)
            target_size_min = np.min(self.target_size)
            target_size_max = np.max(self.target_size)
            im_scale = float(target_size_min) / float(im_size_min)
            if np.round(im_scale * im_size_max) > target_size_max:
                im_scale = float(target_size_max) / float(im_size_max)
            im_scale_x = im_scale
            im_scale_y = im_scale
        else:
            resize_h, resize_w = self.target_size
            im_scale_y = resize_h / float(origin_shape[0])
            im_scale_x = resize_w / float(origin_shape[1])
        return im_scale_y, im_scale_x
 class NormalizeImage(object):
    """normalize image
    Args:
        mean (list): im - mean
        std (list): im / std
        is_scale (bool): whether need im / 255
        norm_type (str): type in ['mean_std', 'none']
    """
    def __init__(self, mean, std, is_scale=True, norm_type='mean_std'):
        self.mean = mean
        self.std = std
        self.is_scale = is_scale
        self.norm_type = norm_type
    def __call__(self, im, im_info):
        """
        Args:
            im (np.ndarray): image (np.ndarray)
            im_info (dict): info of image
        Returns:
            im (np.ndarray):  processed image (np.ndarray)
            im_info (dict): info of processed image
        """
        im = im.astype(np.float32, copy=False)
        if self.is_scale:
            scale = 1.0 / 255.0
            im *= scale
        if self.norm_type == 'mean_std':
            mean = np.array(self.mean)[np.newaxis, np.newaxis, :]
            std = np.array(self.std)[np.newaxis, np.newaxis, :]
            im -= mean
            im /= std
        return im, im_info
 class Permute(object):
    """permute image
    Args:
        to_bgr (bool): whether convert RGB to BGR
        channel_first (bool): whether convert HWC to CHW
    """
    def __init__(self, ):
        super(Permute, self).__init__()
    def __call__(self, im, im_info):
        """
        Args:
            im (np.ndarray): image (np.ndarray)
            im_info (dict): info of image
        Returns:
            im (np.ndarray):  processed image (np.ndarray)
            im_info (dict): info of processed image
        """
        im = im.transpose((2, 0, 1)).copy()
        return im, im_info
 class PadStride(object):
    """ padding image for model with FPN, instead PadBatch(pad_to_stride) in original config
    Args:
        stride (bool): model with FPN need image shape % stride == 0
    """
    def __init__(self, stride=0):
        self.coarsest_stride = stride
    def __call__(self, im, im_info):
        """
        Args:
            im (np.ndarray): image (np.ndarray)
            im_info (dict): info of image
        Returns:
            im (np.ndarray):  processed image (np.ndarray)
            im_info (dict): info of processed image
        """
        coarsest_stride = self.coarsest_stride
        if coarsest_stride <= 0:
            return im, im_info
        im_c, im_h, im_w = im.shape
        pad_h = int(np.ceil(float(im_h) / coarsest_stride) * coarsest_stride)
        pad_w = int(np.ceil(float(im_w) / coarsest_stride) * coarsest_stride)
        padding_im = np.zeros((im_c, pad_h, pad_w), dtype=np.float32)
        padding_im[:, :im_h, :im_w] = im
        return padding_im, im_info
 class LetterBoxResize(object):
    def __init__(self, target_size):
        """
        Resize image to target size, convert normalized xywh to pixel xyxy
        format ([x_center, y_center, width, height] -> [x0, y0, x1, y1]).
        Args:
            target_size (int|list): image target size.
        """
        super(LetterBoxResize, self).__init__()
        if isinstance(target_size, int):
            target_size = [target_size, target_size]
        self.target_size = target_size
    def letterbox(self, img, height, width, color=(127.5, 127.5, 127.5)):
        # letterbox: resize a rectangular image to a padded rectangular
        shape = img.shape[:2]  # [height, width]
        ratio_h = float(height) / shape[0]
        ratio_w = float(width) / shape[1]
        ratio = min(ratio_h, ratio_w)
        new_shape = (round(shape[1] * ratio),
                     round(shape[0] * ratio))  # [width, height]
        padw = (width - new_shape[0]) / 2
        padh = (height - new_shape[1]) / 2
        top, bottom = round(padh - 0.1), round(padh + 0.1)
        left, right = round(padw - 0.1), round(padw + 0.1)
        img = cv2.resize(
            img, new_shape, interpolation=cv2.INTER_AREA)  # resized, no border
        img = cv2.copyMakeBorder(
            img, top, bottom, left, right, cv2.BORDER_CONSTANT,
            value=color)  # padded rectangular
        return img, ratio, padw, padh
    def __call__(self, im, im_info):
        """
        Args:
            im (np.ndarray): image (np.ndarray)
            im_info (dict): info of image
        Returns:
            im (np.ndarray):  processed image (np.ndarray)
            im_info (dict): info of processed image
        """
        assert len(self.target_size) == 2
        assert self.target_size[0] > 0 and self.target_size[1] > 0
        height, width = self.target_size
        h, w = im.shape[:2]
        im, ratio, padw, padh = self.letterbox(im, height=height, width=width)
        new_shape = [round(h * ratio), round(w * ratio)]
        im_info['im_shape'] = np.array(new_shape, dtype=np.float32)
        im_info['scale_factor'] = np.array([ratio, ratio], dtype=np.float32)
        return im, im_info
 class Pad(object):
    def __init__(self, size, fill_value=[114.0, 114.0, 114.0]):
        """
        Pad image to a specified size.
        Args:
            size (list[int]): image target size
            fill_value (list[float]): rgb value of pad area, default (114.0, 114.0, 114.0)
        """
        super(Pad, self).__init__()
        if isinstance(size, int):
            size = [size, size]
        self.size = size
        self.fill_value = fill_value
    def __call__(self, im, im_info):
        im_h, im_w = im.shape[:2]
        h, w = self.size
        if h == im_h and w == im_w:
            im = im.astype(np.float32)
            return im, im_info
        canvas = np.ones((h, w, 3), dtype=np.float32)
        canvas *= np.array(self.fill_value, dtype=np.float32)
        canvas[0:im_h, 0:im_w, :] = im.astype(np.float32)
        im = canvas
        return im, im_info
 def rotate_point(pt, angle_rad):
    """Rotate a point by an angle.
    Args:
        pt (list[float]): 2 dimensional point to be rotated
        angle_rad (float): rotation angle by radian
    Returns:
        list[float]: Rotated point.
    """
    assert len(pt) == 2
    sn, cs = np.sin(angle_rad), np.cos(angle_rad)
    new_x = pt[0] * cs - pt[1] * sn
    new_y = pt[0] * sn + pt[1] * cs
    rotated_pt = [new_x, new_y]
    return rotated_pt
 def _get_3rd_point(a, b):
    """To calculate the affine matrix, three pairs of points are required. This
    function is used to get the 3rd point, given 2D points a & b.
    The 3rd point is defined by rotating vector `a - b` by 90 degrees
    anticlockwise, using b as the rotation center.
    Args:
        a (np.ndarray): point(x,y)
        b (np.ndarray): point(x,y)
    Returns:
        np.ndarray: The 3rd point.
    """
    assert len(a) == 2
    assert len(b) == 2
    direction = a - b
    third_pt = b + np.array([-direction[1], direction[0]], dtype=np.float32)
    return third_pt
 def get_affine_transform(center,
                         input_size,
                         rot,
                         output_size,
                         shift=(0., 0.),
                         inv=False):
    """Get the affine transform matrix, given the center/scale/rot/output_size.
    Args:
        center (np.ndarray[2, ]): Center of the bounding box (x, y).
        scale (np.ndarray[2, ]): Scale of the bounding box
            wrt [width, height].
        rot (float): Rotation angle (degree).
        output_size (np.ndarray[2, ]): Size of the destination heatmaps.
        shift (0-100%): Shift translation ratio wrt the width/height.
            Default (0., 0.).
        inv (bool): Option to inverse the affine transform direction.
            (inv=False: src->dst or inv=True: dst->src)
    Returns:
        np.ndarray: The transform matrix.
    """
    assert len(center) == 2
    assert len(output_size) == 2
    assert len(shift) == 2
    if not isinstance(input_size, (np.ndarray, list)):
        input_size = np.array([input_size, input_size], dtype=np.float32)
    scale_tmp = input_size
    shift = np.array(shift)
    src_w = scale_tmp[0]
    dst_w = output_size[0]
    dst_h = output_size[1]
    rot_rad = np.pi * rot / 180
    src_dir = rotate_point([0., src_w * -0.5], rot_rad)
    dst_dir = np.array([0., dst_w * -0.5])
    src = np.zeros((3, 2), dtype=np.float32)
    src[0, :] = center + scale_tmp * shift
    src[1, :] = center + src_dir + scale_tmp * shift
    src[2, :] = _get_3rd_point(src[0, :], src[1, :])
    dst = np.zeros((3, 2), dtype=np.float32)
    dst[0, :] = [dst_w * 0.5, dst_h * 0.5]
    dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir
    dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])
    if inv:
        trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))
    else:
        trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))
    return trans
 class WarpAffine(object):
    """Warp affine the image
    """
    def __init__(self,
                 keep_res=False,
                 pad=31,
                 input_h=512,
                 input_w=512,
                 scale=0.4,
                 shift=0.1):
        self.keep_res = keep_res
        self.pad = pad
        self.input_h = input_h
        self.input_w = input_w
        self.scale = scale
        self.shift = shift
    def __call__(self, im, im_info):
        """
        Args:
            im (np.ndarray): image (np.ndarray)
            im_info (dict): info of image
        Returns:
            im (np.ndarray):  processed image (np.ndarray)
            im_info (dict): info of processed image
        """
        img = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
        h, w = img.shape[:2]
        if self.keep_res:
            input_h = (h | self.pad) + 1
            input_w = (w | self.pad) + 1
            s = np.array([input_w, input_h], dtype=np.float32)
            c = np.array([w // 2, h // 2], dtype=np.float32)
        else:
            s = max(h, w) * 1.0
            input_h, input_w = self.input_h, self.input_w
            c = np.array([w / 2., h / 2.], dtype=np.float32)
        trans_input = get_affine_transform(c, s, 0, [input_w, input_h])
        img = cv2.resize(img, (w, h))
        inp = cv2.warpAffine(
            img, trans_input, (input_w, input_h), flags=cv2.INTER_LINEAR)
        return inp, im_info
 # keypoint preprocess
 def get_warp_matrix(theta, size_input, size_dst, size_target):
    """This code is based on
        https://github.com/open-mmlab/mmpose/blob/master/mmpose/core/post_processing/post_transforms.py
        Calculate the transformation matrix under the constraint of unbiased.
    Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased
    Data Processing for Human Pose Estimation (CVPR 2020).
    Args:
        theta (float): Rotation angle in degrees.
        size_input (np.ndarray): Size of input image [w, h].
        size_dst (np.ndarray): Size of output image [w, h].
        size_target (np.ndarray): Size of ROI in input plane [w, h].
    Returns:
        matrix (np.ndarray): A matrix for transformation.
    """
    theta = np.deg2rad(theta)
    matrix = np.zeros((2, 3), dtype=np.float32)
    scale_x = size_dst[0] / size_target[0]
    scale_y = size_dst[1] / size_target[1]
    matrix[0, 0] = np.cos(theta) * scale_x
    matrix[0, 1] = -np.sin(theta) * scale_x
    matrix[0, 2] = scale_x * (
        -0.5 * size_input[0] * np.cos(theta) + 0.5 * size_input[1] *
        np.sin(theta) + 0.5 * size_target[0])
    matrix[1, 0] = np.sin(theta) * scale_y
    matrix[1, 1] = np.cos(theta) * scale_y
    matrix[1, 2] = scale_y * (
        -0.5 * size_input[0] * np.sin(theta) - 0.5 * size_input[1] *
        np.cos(theta) + 0.5 * size_target[1])
    return matrix
 class TopDownEvalAffine(object):
    """apply affine transform to image and coords
    Args:
        trainsize (list): [w, h], the standard size used to train
        use_udp (bool): whether to use Unbiased Data Processing.
        records(dict): the dict contained the image and coords
    Returns:
        records (dict): contain the image and coords after tranformed
    """
    def __init__(self, trainsize, use_udp=False):
        self.trainsize = trainsize
        self.use_udp = use_udp
    def __call__(self, image, im_info):
        rot = 0
        imshape = im_info['im_shape'][::-1]
        center = im_info['center'] if 'center' in im_info else imshape / 2.
        scale = im_info['scale'] if 'scale' in im_info else imshape
        if self.use_udp:
            trans = get_warp_matrix(
                rot, center * 2.0,
                [self.trainsize[0] - 1.0, self.trainsize[1] - 1.0], scale)
            image = cv2.warpAffine(
                image,
                trans, (int(self.trainsize[0]), int(self.trainsize[1])),
                flags=cv2.INTER_LINEAR)
        else:
            trans = get_affine_transform(center, scale, rot, self.trainsize)
            image = cv2.warpAffine(
                image,
                trans, (int(self.trainsize[0]), int(self.trainsize[1])),
                flags=cv2.INTER_LINEAR)
        return image, im_info
 class Compose:
    def __init__(self, transforms):
        self.transforms = []
        for op_info in transforms:
            new_op_info = op_info.copy()
            op_type = new_op_info.pop('type')
            self.transforms.append(eval(op_type)(**new_op_info))
    def __call__(self, img_path):
        img, im_info = decode_image(img_path)
        for t in self.transforms:
            img, im_info = t(img, im_info)
        inputs = copy.deepcopy(im_info)
        inputs['image'] = img
        return inputs
--- a/src/models/ocr_model/utils/inference.py
+++ b/src/models/ocr_model/utils/inference.py
@@ -13,8 +13,8 @@ from models.globals import MAX_TOKEN_SIZE
 def inference(
    model: TexTeller, 
    tokenizer: RobertaTokenizerFast,
-    imgs: Union[List[str], List[np.ndarray]], 
+    imgs_path: Union[List[str], List[np.ndarray]], 
-    use_cuda: bool,
+    inf_mode: str = 'cpu',
    num_beams: int = 1,
 ) -> List[str]:
    model.eval()
@@ -26,9 +26,8 @@ def inference(
    imgs = inference_transform(imgs)
    pixel_values = torch.stack(imgs)
-    if use_cuda:
+    model = model.to(inf_mode)
-        model = model.to('cuda')
+    pixel_values = pixel_values.to(inf_mode)
        pixel_values = pixel_values.to('cuda')
    generate_config = GenerationConfig(
        max_new_tokens=MAX_TOKEN_SIZE,
--- a/src/rec_infer_from_crop_imgs.py
+++ b/src/rec_infer_from_crop_imgs.py
@@ -0,0 +1,59 @@
 import os
 import argparse
 import cv2 as cv
 from pathlib import Path
 from utils import to_katex
 from models.ocr_model.utils.inference import inference as latex_inference
 from models.ocr_model.model.TexTeller import TexTeller
 if __name__ == '__main__':
    os.chdir(Path(__file__).resolve().parent)
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '-img', 
        type=str, 
        required=True,
        help='path to the input image'
    )
    parser.add_argument(
        '--inference-mode', 
        type=str,
        default='cpu',
        help='Inference mode, select one of cpu, cuda, or mps'
    )
    parser.add_argument(
        '--num-beam', 
        type=int,
        default=1,
        help='number of beam search for decoding'
    )
    args = parser.parse_args()
    print('Loading model and tokenizer...')
    latex_rec_model = TexTeller.from_pretrained()
    tokenizer = TexTeller.get_tokenizer()
    print('Model and tokenizer loaded.')
    # Create the output directory if it doesn't exist
    os.makedirs(args.output_dir, exist_ok=True)
    # Loop through all images in the input directory
    for filename in os.listdir(args.img_dir):
        img_path = os.path.join(args.img_dir, filename)
        img = cv.imread(img_path)
        if img is not None:
            print(f'Inference for {filename}...')
            res = latex_inference(latex_rec_model, tokenizer, [img], inf_mode=args.inference_mode, num_beams=args.num_beam)
            res = to_katex(res[0])
            # Save the recognition result to a text file
            output_file = os.path.join(args.output_dir, os.path.splitext(filename)[0] + '.txt')
            with open(output_file, 'w') as f:
                f.write(res)
            print(f'Result saved to {output_file}')
        else:
            print(f"Warning: Could not read image {img_path}. Skipping...")
--- a/src/server.py
+++ b/src/server.py
@@ -23,8 +23,8 @@ parser.add_argument('--num_replicas', type=int, default=1)
 parser.add_argument('--ncpu_per_replica', type=float, default=1.0)
 parser.add_argument('--ngpu_per_replica', type=float, default=0.0)
-parser.add_argument('--use_cuda', action='store_true', default=False)
+parser.add_argument('--inference-mode', type=str, default='cpu')
-parser.add_argument('--num_beam', type=int, default=1)
+parser.add_argument('--num_beams', type=int, default=1)
 args = parser.parse_args()
 if args.ngpu_per_replica > 0 and not args.use_cuda:
@@ -43,18 +43,21 @@ class TexTellerServer:
        self, 
        checkpoint_path: str, 
        tokenizer_path: str, 
-        use_cuda: bool = False,
+        inf_mode: str = 'cpu',
-        num_beam: int = 1
+        num_beams: int = 1
    ) -> None:
        self.model = TexTeller.from_pretrained(checkpoint_path)
        self.tokenizer = TexTeller.get_tokenizer(tokenizer_path)
-        self.use_cuda = use_cuda
+        self.inf_mode = inf_mode
-        self.num_beam = num_beam
+        self.num_beams = num_beams
-        self.model = self.model.to('cuda') if use_cuda else self.model
+        self.model = self.model.to(inf_mode) if inf_mode != 'cpu' else self.model
    def predict(self, image_nparray) -> str:
-        return inference(self.model, self.tokenizer, [image_nparray], self.use_cuda, self.num_beam)[0]
+        return inference(
            self.model, self.tokenizer, [image_nparray],
            inf_mode=self.inf_mode, num_beams=self.num_beams
        )[0]
@serve.deployment()
@@ -78,7 +81,11 @@ if __name__ == '__main__':
    tknz_dir = args.tokenizer_dir
    serve.start(http_options={"port": args.server_port})
-    texteller_server = TexTellerServer.bind(ckpt_dir, tknz_dir, use_cuda=args.use_cuda, num_beam=args.num_beam)
+    texteller_server = TexTellerServer.bind(
        ckpt_dir, tknz_dir, 
        inf_mode=args.inference_mode,
        num_beams=args.num_beams
    )
    ingress = Ingress.bind(texteller_server)
    ingress_handle = serve.run(ingress, route_prefix="/predict")  
--- a/src/start_web.bat
+++ b/src/start_web.bat
@@ -0,0 +1,9 @@
@echo off
 SETLOCAL ENABLEEXTENSIONS
 set CHECKPOINT_DIR=default
 set TOKENIZER_DIR=default
 streamlit run web.py
 ENDLOCAL
--- a/src/start_web.sh
+++ b/src/start_web.sh
@@ -1,10 +1,7 @@
 #!/usr/bin/env bash
 set -exu
-export CHECKPOINT_DIR="/home/lhy/code/TexTeller/src/models/ocr_model/train/train_result/TexTellerv3/checkpoint-460000"
+export CHECKPOINT_DIR="default"
-# export CHECKPOINT_DIR="default"
+export TOKENIZER_DIR="default"
 export TOKENIZER_DIR="/home/lhy/code/TexTeller/src/models/tokenizer/roberta-tokenizer-7Mformulas"
 export USE_CUDA=True  # True or False (case-sensitive)
 export NUM_BEAM=3
 streamlit run web.py
--- a/src/web.py
+++ b/src/web.py
@@ -6,16 +6,22 @@ import shutil
 import streamlit as st
 from PIL import Image
 from streamlit_paste_button import paste_image_button as pbutton
 from models.ocr_model.utils.inference import inference
 from models.ocr_model.model.TexTeller import TexTeller
 from utils import to_katex
 st.set_page_config(
    page_title="TexTeller",
    page_icon="🧮"
 )
 html_string = '''
    <h1 style="color: black; text-align: center;">
-        <img src="https://slackmojis.com/emojis/429-troll/download" width="50">
+        <img src="https://raw.githubusercontent.com/OleehyO/TexTeller/main/assets/fire.svg" width="100">
-        TexTeller
+        𝚃𝚎𝚡𝚃𝚎𝚕𝚕𝚎𝚛
-        <img src="https://slackmojis.com/emojis/429-troll/download" width="50">
+        <img src="https://raw.githubusercontent.com/OleehyO/TexTeller/main/assets/fire.svg" width="100">
    </h1>
 '''
@@ -35,8 +41,6 @@ fail_gif_html = '''
    </h1>
 '''
@st.cache_resource
 def get_model():
    return TexTeller.from_pretrained(os.environ['CHECKPOINT_DIR'])
@@ -52,6 +56,12 @@ def get_image_base64(img_file):
    img.save(buffered, format="PNG")
    return base64.b64encode(buffered.getvalue()).decode()
 def on_file_upload():
    st.session_state["UPLOADED_FILE_CHANGED"] = True
 def change_side_bar():
    st.session_state["CHANGE_SIDEBAR_FLAG"] = True
 model = get_model()
 tokenizer = get_tokenizer()
@@ -59,37 +69,106 @@ if "start" not in st.session_state:
    st.session_state["start"] = 1
    st.toast('Hooray!', icon='🎉')
 if "UPLOADED_FILE_CHANGED" not in st.session_state:
    st.session_state["UPLOADED_FILE_CHANGED"] = False
-#  ============================     pages      =============================== #
+if "CHANGE_SIDEBAR_FLAG" not in st.session_state:
    st.session_state["CHANGE_SIDEBAR_FLAG"] = False
 #  ============================     begin sidebar      =============================== #
 with st.sidebar:
    num_beams = 1
    inf_mode = 'cpu'
    st.markdown("# 🔨️ Config")
    st.markdown("")
    model_type = st.selectbox(
        "Model type",
        ("TexTeller", "None"),
        on_change=change_side_bar
    )
    if model_type == "TexTeller":
        num_beams = st.number_input(
            'Number of beams',
            min_value=1,
            max_value=20,
            step=1,
            on_change=change_side_bar
        )
    inf_mode = st.radio(
        "Inference mode",
        ("cpu", "cuda", "mps"),
        on_change=change_side_bar
    )
 #  ============================     end sidebar      =============================== #
 #  ============================     begin pages      =============================== #
 st.markdown(html_string, unsafe_allow_html=True)
-uploaded_file = st.file_uploader("",type=['jpg', 'png', 'pdf'])
+uploaded_file = st.file_uploader(
    " ",
    type=['jpg', 'png'],
    on_change=on_file_upload
 )
 paste_result = pbutton(
    label="📋 Paste an image",
    background_color="#5BBCFF",
    hover_background_color="#3498db",
 )
 st.write("")
 if st.session_state["CHANGE_SIDEBAR_FLAG"] == True:
    st.session_state["CHANGE_SIDEBAR_FLAG"] = False
 elif uploaded_file or paste_result.image_data is not None:
    if st.session_state["UPLOADED_FILE_CHANGED"] == False and paste_result.image_data is not None:
        uploaded_file = io.BytesIO()
        paste_result.image_data.save(uploaded_file, format='PNG')
        uploaded_file.seek(0)
    if st.session_state["UPLOADED_FILE_CHANGED"] == True:
        st.session_state["UPLOADED_FILE_CHANGED"] = False
 if uploaded_file:
    img = Image.open(uploaded_file)
    temp_dir = tempfile.mkdtemp()
    png_file_path = os.path.join(temp_dir, 'image.png')
    img.save(png_file_path, 'PNG')
-    img_base64 = get_image_base64(uploaded_file)
+    with st.container(height=300):
        img_base64 = get_image_base64(uploaded_file)
        st.markdown(f"""
        <style>
        .centered-container {{
            text-align: center;
        }}
        .centered-image {{
            display: block;
            margin-left: auto;
            margin-right: auto;
            max-height: 350px;
            max-width: 100%;
        }}
        </style>
        <div class="centered-container">
            <img src="data:image/png;base64,{img_base64}" class="centered-image" alt="Input image">
        </div>
        """, unsafe_allow_html=True)
    st.markdown(f"""
    <style>
    .centered-container {{
        text-align: center;
    }}
    .centered-image {{
        display: block;
        margin-left: auto;
        margin-right: auto;
        max-width: 500px;
        max-height: 500px;
    }}
    </style>
    <div class="centered-container">
        <img src="data:image/png;base64,{img_base64}" class="centered-image" alt="Input image">
        <p style="color:gray;">Input image ({img.height}✖️{img.width})</p>
    </div>
    """, unsafe_allow_html=True)
@@ -102,15 +181,28 @@ if uploaded_file:
            model,
            tokenizer,
            [png_file_path],
-            True if os.environ['USE_CUDA'] == 'True' else False,
+            inf_mode=inf_mode,
-            int(os.environ['NUM_BEAM'])
+            num_beams=num_beams
        )[0]
        st.success('Completed!', icon="✅")
        st.markdown(suc_gif_html, unsafe_allow_html=True)
        katex_res = to_katex(TexTeller_result)
-        st.text_area(":red[Predicted formula]", katex_res, height=150)
+        st.text_area(":blue[***  𝑃r𝑒d𝑖c𝑡e𝑑 𝑓o𝑟m𝑢l𝑎  ***]", katex_res, height=150)
        st.latex(katex_res)
        st.write("")
        st.write("")
        with st.expander(":star2: :gray[Tips for better results]"):
            st.markdown('''
                * :mag_right: Use a clear and high-resolution image.
                * :scissors: Crop images as accurately as possible.
                * :jigsaw: Split large multi line formulas into smaller ones.
                * :page_facing_up: Use images with **white background and black text** as much as possible.
                * :book: Use a font with good readability.
            ''')
        shutil.rmtree(temp_dir)
-#  ============================     pages      =============================== #
+    paste_result.image_data = None
 #  ============================     end pages      =============================== #