update(readme): Optimizing the Installation Process

README.md

@@ -82,21 +82,22 @@ conda create -n MinerU python=3.10
 conda activate MinerU
 ```
 
-### Usage Instructions
+### Installation and Configuration
 
 #### 1. Install Magic-PDF
 
-Install using pip:
-```bash
-pip install magic-pdf
-```
-Alternatively, for built-in high-precision model parsing capabilities, use:
+Install the full-feature package with pip:
+>Note: The pip-installed package supports CPU-only and is ideal for quick tests.
+>
+>For CUDA/MPS acceleration in production, see [Acceleration Using CUDA or MPS](#4-Acceleration-Using-CUDA-or-MPS).
+
 ```bash
 pip install magic-pdf[full-cpu]
 ```
-The high-precision models depend on detectron2, which requires a compiled installation.  
-If you need to compile it yourself, refer to https://github.com/facebookresearch/detectron2/issues/5114  
-Or directly use our pre-compiled wheel packages (limited to python 3.10):  
+The full-feature package depends on detectron2, which requires a compilation installation.   
+If you need to compile it yourself, please refer to https://github.com/facebookresearch/detectron2/issues/5114  
+Alternatively, you can directly use our precompiled whl package (limited to Python 3.10):
+
 ```bash
 pip install detectron2 --extra-index-url https://myhloli.github.io/wheels/
 ```
@@ -123,31 +124,8 @@ In magic-pdf.json, configure "models-dir" to point to the directory where the mo
 ```
 
 
-#### 4. Usage via Command Line
-
-###### simple
-
-```bash
-magic-pdf pdf-command --pdf "pdf_path" --inside_model true
-```
-After the program has finished, you can find the generated markdown files under the directory "/tmp/magic-pdf".  
-You can find the corresponding xxx_model.json file in the markdown directory.   
-If you intend to do secondary development on the post-processing pipeline, you can use the command:  
-```bash
-magic-pdf pdf-command --pdf "pdf_path" --model "model_json_path"
-```
-In this way, you won't need to re-run the model data, making debugging more convenient.
-
-
-###### more 
-
-```bash
-magic-pdf --help
-```
-
-
-#### 5. Acceleration Using CUDA or MPS
-
+#### 4. Acceleration Using CUDA or MPS
+If you have an available Nvidia GPU or are using a Mac with Apple Silicon, you can leverage acceleration with CUDA or MPS respectively.
 ##### CUDA
 
 You need to install the corresponding PyTorch version according to your CUDA version.  
@@ -172,13 +150,39 @@ You also need to modify the value of "device-mode" in the configuration file mag
 }
 ```
 
-#### 6. Usage via Api
+
+### Usage
+
+#### 1.Usage via Command Line
+
+###### simple
+
+```bash
+magic-pdf pdf-command --pdf "pdf_path" --inside_model true
+```
+After the program has finished, you can find the generated markdown files under the directory "/tmp/magic-pdf".  
+You can find the corresponding xxx_model.json file in the markdown directory.   
+If you intend to do secondary development on the post-processing pipeline, you can use the command:  
+```bash
+magic-pdf pdf-command --pdf "pdf_path" --model "model_json_path"
+```
+In this way, you won't need to re-run the model data, making debugging more convenient.
+
+
+###### more 
+
+```bash
+magic-pdf --help
+```
+
+
+#### 2. Usage via Api
 
 ###### Local
 ```python
 image_writer = DiskReaderWriter(local_image_dir)
 image_dir = str(os.path.basename(local_image_dir))
-jso_useful_key = {"_pdf_type": "", "model_list": model_json}
+jso_useful_key = {"_pdf_type": "", "model_list": []}
 pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
 pipe.pipe_classify()
 pipe.pipe_parse()
@@ -191,7 +195,7 @@ s3pdf_cli = S3ReaderWriter(pdf_ak, pdf_sk, pdf_endpoint)
 image_dir = "s3://img_bucket/"
 s3image_cli = S3ReaderWriter(img_ak, img_sk, img_endpoint, parent_path=image_dir)
 pdf_bytes = s3pdf_cli.read(s3_pdf_path, mode=s3pdf_cli.MODE_BIN)
-jso_useful_key = {"_pdf_type": "", "model_list": model_json}
+jso_useful_key = {"_pdf_type": "", "model_list": []}
 pipe = UNIPipe(pdf_bytes, jso_useful_key, s3image_cli)
 pipe.pipe_classify()
 pipe.pipe_parse()

README_zh-CN.md

@@ -78,19 +78,18 @@ conda activate MinerU
 ```
 开发基于python 3.10，如果在其他版本python出现问题请切换至3.10。
 
-### 使用说明
+### 安装配置
 
 #### 1. 安装Magic-PDF
 
-使用pip安装：
-```bash
-pip install magic-pdf
-```
-或者，需要内置高精度模型解析功能，使用：
+使用pip安装完整功能包：
+>受pypi限制，pip安装的完整功能包仅支持cpu推理，建议只用于快速测试解析能力。
+>
+>如需在生产环境使用CUDA/MPS加速请参考[使用CUDA或MPS加速推理](#4-使用CUDA或MPS加速推理)
 ```bash
 pip install magic-pdf[full-cpu]
 ```
-高精度模型依赖于detectron2，该库需要编译安装，如需自行编译，请参考 https://github.com/facebookresearch/detectron2/issues/5114  
+完整功能包依赖detectron2，该库需要编译安装，如需自行编译，请参考 https://github.com/facebookresearch/detectron2/issues/5114  
 或是直接使用我们预编译的whl包(仅限python 3.10)：  
 ```bash
 pip install detectron2 --extra-index-url https://myhloli.github.io/wheels/
@@ -113,30 +112,9 @@ cp magic-pdf.template.json ~/magic-pdf.json
 }
 ```
 
-#### 4. 通过命令行使用
-
-###### 直接使用
-
-```bash
-magic-pdf pdf-command --pdf "pdf_path" --inside_model true
-```
-程序运行完成后，你可以在"/tmp/magic-pdf"目录下看到生成的markdown文件，markdown目录中可以找到对应的xxx_model.json文件  
-如果您有意对后处理pipeline进行二次开发，可以使用命令  
-```bash
-magic-pdf pdf-command --pdf "pdf_path" --model "model_json_path"
-```
-这样就不需要重跑模型数据，调试起来更方便
-
-###### 更多用法
-
-```bash
-magic-pdf --help
-```
-
-
-#### 5. 使用CUDA或MPS进行加速
-
-###### CUDA
+#### 4. 使用CUDA或MPS加速推理
+如您有可用的Nvidia显卡或在使用Apple Silicon的Mac，可以使用CUDA或MPS进行加速
+##### CUDA
 
 需要根据自己的CUDA版本安装对应的pytorch版本  
 以下是对应CUDA 11.8版本的安装命令，更多信息请参考 https://pytorch.org/get-started/locally/  
@@ -151,7 +129,7 @@ pip install --force-reinstall torch==2.3.1 torchvision==0.18.1 --index-url https
 }
 ```
 
-###### MPS
+##### MPS
 使用macOS(M系列芯片设备)可以使用MPS进行推理加速  
 需要修改配置文件magic-pdf.json中"device-mode"的值  
 ```json
@@ -161,13 +139,36 @@ pip install --force-reinstall torch==2.3.1 torchvision==0.18.1 --index-url https
 ```
 
 
-#### 6. 通过接口调用
+### 使用说明
+
+#### 1. 通过命令行使用
+
+###### 直接使用
+
+```bash
+magic-pdf pdf-command --pdf "pdf_path" --inside_model true
+```
+程序运行完成后，你可以在"/tmp/magic-pdf"目录下看到生成的markdown文件，markdown目录中可以找到对应的xxx_model.json文件  
+如果您有意对后处理pipeline进行二次开发，可以使用命令  
+```bash
+magic-pdf pdf-command --pdf "pdf_path" --model "model_json_path"
+```
+这样就不需要重跑模型数据，调试起来更方便
+
+###### 更多用法
+
+```bash
+magic-pdf --help
+```
+
+
+#### 2. 通过接口调用
 
 ###### 本地使用
 ```python
 image_writer = DiskReaderWriter(local_image_dir)
 image_dir = str(os.path.basename(local_image_dir))
-jso_useful_key = {"_pdf_type": "", "model_list": model_json}
+jso_useful_key = {"_pdf_type": "", "model_list": []}
 pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
 pipe.pipe_classify()
 pipe.pipe_parse()
@@ -180,7 +181,7 @@ s3pdf_cli = S3ReaderWriter(pdf_ak, pdf_sk, pdf_endpoint)
 image_dir = "s3://img_bucket/"
 s3image_cli = S3ReaderWriter(img_ak, img_sk, img_endpoint, parent_path=image_dir)
 pdf_bytes = s3pdf_cli.read(s3_pdf_path, mode=s3pdf_cli.MODE_BIN)
-jso_useful_key = {"_pdf_type": "", "model_list": model_json}
+jso_useful_key = {"_pdf_type": "", "model_list": []}
 pipe = UNIPipe(pdf_bytes, jso_useful_key, s3image_cli)
 pipe.pipe_classify()
 pipe.pipe_parse()

demo/demo.py

@@ -12,7 +12,8 @@ try:
     pdf_path = os.path.join(current_script_dir, f"{demo_name}.pdf")
     model_path = os.path.join(current_script_dir, f"{demo_name}.json")
     pdf_bytes = open(pdf_path, "rb").read()
-    model_json = json.loads(open(model_path, "r", encoding="utf-8").read())
+    # model_json = json.loads(open(model_path, "r", encoding="utf-8").read())
+    model_json = []  # model_json传空list使用内置模型解析
     jso_useful_key = {"_pdf_type": "", "model_list": model_json}
     local_image_dir = os.path.join(current_script_dir, 'images')
     image_dir = str(os.path.basename(local_image_dir))