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  1. .gitignore +163 -0
  2. LICENSE +674 -0
  3. app.py +55 -0
  4. config.py +13 -0
  5. models.py +92 -0
  6. requirements.txt +13 -0
  7. settings.toml +39 -0
  8. ui/__init__.py +0 -0
  9. ui/chat.py +207 -0
  10. ui/summary.py +179 -0
  11. utils/__init__.py +0 -0
  12. utils/chatpdf.py +157 -0
  13. utils/llm.py +50 -0
  14. utils/singleton.py +37 -0
.gitignore ADDED
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+ # Byte-compiled / optimized / DLL files
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+ __pycache__/
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+ *.py[cod]
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+ *$py.class
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+
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+ # C extensions
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+ *.so
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+
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+ # Distribution / packaging
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+ .Python
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+ build/
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+ develop-eggs/
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+ dist/
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+ downloads/
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+ eggs/
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+ .eggs/
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+ lib/
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+ lib64/
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+ parts/
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+ sdist/
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+ var/
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+ wheels/
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+ share/python-wheels/
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+ *.egg-info/
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+ .installed.cfg
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+ *.egg
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+ MANIFEST
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+
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+ # PyInstaller
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+ # Usually these files are written by a python script from a template
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+ # before PyInstaller builds the exe, so as to inject date/other infos into it.
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+ *.manifest
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+ *.spec
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+
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+ # Installer logs
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+ pip-log.txt
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+ pip-delete-this-directory.txt
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+
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+ # Unit test / coverage reports
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+ htmlcov/
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+ .tox/
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+ .nox/
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+ .coverage
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+ .coverage.*
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+ .cache
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+ nosetests.xml
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+ coverage.xml
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+ *.cover
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+ *.py,cover
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+ .hypothesis/
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+ .pytest_cache/
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+ cover/
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+
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+ # Translations
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+ *.mo
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+ *.pot
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+
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+ # Django stuff:
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+ *.log
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+ local_settings.py
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+ db.sqlite3
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+ db.sqlite3-journal
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+
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+ # Flask stuff:
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+ instance/
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+ .webassets-cache
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+
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+ # Scrapy stuff:
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+ .scrapy
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+
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+ # Sphinx documentation
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+ docs/_build/
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+
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+ # PyBuilder
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+ .pybuilder/
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+ target/
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+
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+ # Jupyter Notebook
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+ .ipynb_checkpoints
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+
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+ # IPython
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+ profile_default/
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+ ipython_config.py
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+
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+ # pyenv
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+ # For a library or package, you might want to ignore these files since the code is
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+ # intended to run in multiple environments; otherwise, check them in:
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+ # .python-version
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+
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+ # pipenv
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+ # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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+ # However, in case of collaboration, if having platform-specific dependencies or dependencies
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+ # having no cross-platform support, pipenv may install dependencies that don't work, or not
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+ # install all needed dependencies.
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+ #Pipfile.lock
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+
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+ # poetry
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+ # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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+ # This is especially recommended for binary packages to ensure reproducibility, and is more
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+ # commonly ignored for libraries.
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+ # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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+ #poetry.lock
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+
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+ # pdm
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+ # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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+ #pdm.lock
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+ # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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+ # in version control.
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+ # https://pdm.fming.dev/#use-with-ide
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+ .pdm.toml
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+
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+ # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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+ __pypackages__/
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+
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+ # Celery stuff
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+ celerybeat-schedule
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+ celerybeat.pid
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+
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+ # SageMath parsed files
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+ *.sage.py
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+
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+ # Environments
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+ .env
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+ .venv
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+ env/
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+ venv/
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+ ENV/
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+ env.bak/
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+ venv.bak/
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+
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+ # Spyder project settings
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+ .spyderproject
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+ .spyproject
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+
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+ # Rope project settings
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+ .ropeproject
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+
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+ # mkdocs documentation
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+ /site
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+
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+ # mypy
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+ .mypy_cache/
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+ .dmypy.json
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+ dmypy.json
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+
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+ # Pyre type checker
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+ .pyre/
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+
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+ # pytype static type analyzer
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+ .pytype/
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+
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+ # Cython debug symbols
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+ cython_debug/
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+
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+ # PyCharm
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+ # JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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+ # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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+ # and can be added to the global gitignore or merged into this file. For a more nuclear
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+ # option (not recommended) you can uncomment the following to ignore the entire idea folder.
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+ #.idea/
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+
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+ # Ignore dynaconf secret files
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+ .secrets.*
LICENSE ADDED
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+ Nothing in this License shall be construed as excluding or limiting
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+ 12. No Surrender of Others' Freedom.
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+ If conditions are imposed on you (whether by court order, agreement or
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+ 13. Use with the GNU Affero General Public License.
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+ 15. Disclaimer of Warranty.
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+ THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
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+ END OF TERMS AND CONDITIONS
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+ How to Apply These Terms to Your New Programs
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+
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+ If you develop a new program, and you want it to be of the greatest
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+ This program is free software: you can redistribute it and/or modify
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+ You should have received a copy of the GNU General Public License
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+ along with this program. If not, see <https://www.gnu.org/licenses/>.
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+ Also add information on how to contact you by electronic and paper mail.
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+ If the program does terminal interaction, make it output a short
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+ This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
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+ This is free software, and you are welcome to redistribute it
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+ You should also get your employer (if you work as a programmer) or school,
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+ For more information on this, and how to apply and follow the GNU GPL, see
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+ <https://www.gnu.org/licenses/>.
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+
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+ The GNU General Public License does not permit incorporating your program
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+ into proprietary programs. If your program is a subroutine library, you
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+ may consider it more useful to permit linking proprietary applications with
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+ the library. If this is what you want to do, use the GNU Lesser General
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+ Public License instead of this License. But first, please read
674
+ <https://www.gnu.org/licenses/why-not-lgpl.html>.
app.py ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+ from ui import chat
3
+ from ui import summary
4
+ from models import llm_model_dict, llm_model_dict_list, \
5
+ embedding_model_dict_list
6
+ from models import models
7
+
8
+ block_css = """.importantButton {
9
+ background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important;
10
+ border: none !important;
11
+ }
12
+ .importantButton:hover {
13
+ background: linear-gradient(45deg, #ff00e0,#8500ff, #6e00ff) !important;
14
+ border: none !important;
15
+ }"""
16
+
17
+ webui_title = """
18
+ # 🎉ChatGLM-Chinese-Summary WebUI🎉
19
+ Link in: [https://github.com/zhongpei/ChatGLM-Chinese-Summary](https://github.com/zhongpei/ChatGLM-Chinese-Summary)
20
+ """
21
+
22
+ with gr.Blocks(css=block_css) as demo:
23
+ gr.Markdown(webui_title)
24
+ with gr.Tab("load model"):
25
+ llm_model = gr.Radio(llm_model_dict_list,
26
+ label="LLM 模型",
27
+ value=list(llm_model_dict.keys())[0],
28
+ interactive=True)
29
+ llm_lora = gr.Textbox(label="lora path", value="")
30
+ embedding_model = gr.Radio(embedding_model_dict_list,
31
+ label="Embedding 模型",
32
+ value=embedding_model_dict_list[0],
33
+ interactive=True)
34
+
35
+ result = gr.Label("模型未加载")
36
+
37
+ load_model_button = gr.Button(
38
+ "重新加载模型" if models.is_active() else "加载模型"
39
+ )
40
+ load_model_button.click(
41
+ models.init_model,
42
+ show_progress=True,
43
+ inputs=[llm_model, llm_lora, embedding_model],
44
+ outputs=result
45
+ )
46
+
47
+ with gr.Tab("chat"):
48
+ chat.chat_ui(embedding_model)
49
+
50
+ with gr.Tab("Summary"):
51
+ summary.summary_ui()
52
+
53
+ demo.queue(concurrency_count=3).launch(
54
+ server_name='0.0.0.0', share=False, inbrowser=False
55
+ )
config.py ADDED
@@ -0,0 +1,13 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from dynaconf import Dynaconf
2
+
3
+ settings = Dynaconf(
4
+ envvar_prefix="DYNACONF",
5
+ settings_files=['settings.toml', '.secrets.toml'],
6
+
7
+ )
8
+
9
+ # `envvar_prefix` = export envvars with `export DYNACONF_FOO=bar`.
10
+ # `settings_files` = Load these files in the order.
11
+
12
+ llm_model_dict_list = list(llm_model_dict.keys())
13
+ embedding_model_dict_list = list(embedding_model_dict.keys())
models.py ADDED
@@ -0,0 +1,92 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+
3
+ from loguru import logger
4
+
5
+ from utils.chatpdf import ChatPDF
6
+ from utils.llm import LLM
7
+ from utils.singleton import Singleton
8
+
9
+ MAX_INPUT_LEN = 2048
10
+
11
+ embedding_model_dict = {
12
+ "text2vec-large": "GanymedeNil/text2vec-large-chinese",
13
+ "text2vec-base": "shibing624/text2vec-base-chinese",
14
+ "sentence-transformers": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
15
+ "ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
16
+ "ernie-base": "nghuyong/ernie-3.0-base-zh",
17
+ }
18
+
19
+ # supported LLM models
20
+ llm_model_dict = {
21
+ #"chatglm-6b": "E:\\sdwebui\\image2text_prompt_generator\\models\\chatglm-6b",
22
+ #"chatglm-6b-int4": "E:\\sdwebui\\image2text_prompt_generator\\models\\chatglm-6b-int4",
23
+ "chatglm-6b": "THUDM/chatglm-6b",
24
+ "chatglm-6b-int4": "THUDM/chatglm-6b-int4",
25
+ "llama-7b": "decapoda-research/llama-7b-hf",
26
+ "llama-13b": "decapoda-research/llama-13b-hf",
27
+ "t5-lamini-flan-783M": "MBZUAI/LaMini-Flan-T5-783M",
28
+ }
29
+
30
+ llm_model_dict_list = list(llm_model_dict.keys())
31
+ embedding_model_dict_list = list(embedding_model_dict.keys())
32
+
33
+
34
+ @Singleton
35
+ class Models(object):
36
+
37
+ def __init__(self):
38
+ self._chatpdf = None
39
+ self._llm_model = None
40
+
41
+ def is_active(self):
42
+ return self._chatpdf is not None and self._llm_model is not None
43
+
44
+ @property
45
+ def chatpdf(self):
46
+ return self._chatpdf
47
+
48
+ @property
49
+ def llm_model(self):
50
+ return self._llm_model
51
+
52
+ def reset_model(self):
53
+ if self._chatpdf is not None:
54
+ del self._chatpdf
55
+
56
+ if self._llm_model is not None:
57
+ del self._llm_model
58
+
59
+ self._chatpdf = None
60
+ self._llm_model = None
61
+
62
+ def init_model(self, llm_model, llm_lora, embedding_model):
63
+ try:
64
+ self.reset_model()
65
+
66
+ llm_lora_path = None
67
+ if llm_lora is not None and os.path.exists(llm_lora):
68
+ llm_lora_path = llm_lora
69
+ self._chatpdf = ChatPDF(
70
+ sim_model_name_or_path=embedding_model_dict.get(
71
+ embedding_model,
72
+ "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
73
+ ),
74
+
75
+ )
76
+ self._llm_model = LLM(
77
+ gen_model_type=llm_model.split('-')[0],
78
+ gen_model_name_or_path=llm_model_dict.get(llm_model, "THUDM/chatglm-6b-int4"),
79
+ lora_model_name_or_path=llm_lora_path
80
+ )
81
+ if self._chatpdf is not None and self._llm_model is not None:
82
+ model_status = f"模型{llm_model} lora:{llm_lora} embedding:{embedding_model}已成功加载"
83
+ else:
84
+ model_status = f"llm:{self._llm_model} pdf:{self._chatpdf}加载失败"
85
+ logger.info(model_status)
86
+ return model_status
87
+ except Exception as e:
88
+ logger.error(f"加载模型失败:{e}")
89
+ raise e
90
+
91
+
92
+ models = Models.instance()
requirements.txt ADDED
@@ -0,0 +1,13 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ gradio
2
+ similarities>=1.0.5
3
+ sentencepiece
4
+ textgen
5
+ markdown
6
+ PyPDF2
7
+ python-docx
8
+ pandas
9
+ protobuf
10
+ cpm-kernels
11
+ loguru
12
+ bs4
13
+ dynaconf
settings.toml ADDED
@@ -0,0 +1,39 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [models]
2
+
3
+ [models.llm."chatglm-6b"]
4
+ type = "chatglm"
5
+ path = "THUDM/chatglm-6b"
6
+ [models.llm."chatglm-6b-int8"]
7
+ type = "chatglm"
8
+ path = "THUDM/chatglm-6b-int8"
9
+ [models.llm."chatglm-6b-int4"]
10
+ type = "chatglm"
11
+ path = "THUDM/chatglm-6b-int4"
12
+ [models.llm."phoenix-inst-chat-7b"]
13
+ type = "phoenix"
14
+ path = "FreedomIntelligence/phoenix-inst-chat-7b"
15
+ [models.llm."phoenix-inst-chat-7b-int4"]
16
+ type = "phoenix"
17
+ path = "FreedomIntelligence/phoenix-inst-chat-7b-int4"
18
+
19
+ [models.embeddings]
20
+ [models.embeddings."text2vec-large-chinese"]
21
+ type = "default"
22
+ path = "GanymedeNil/text2vec-large-chinese"
23
+ [models.embeddings."text2vec-base"]
24
+ type = "default"
25
+ path = "shibing624/text2vec-base-chinese"
26
+ [models.embeddings."text2vec-base"]
27
+ type = "default"
28
+ path = "shibing624/text2vec-base-chinese"
29
+ [models.embeddings."sentence-transformers"]
30
+ type = "default"
31
+ path = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
32
+ [models.embeddings."ernie-tiny"]
33
+ type = "default"
34
+ path = "nghuyong/ernie-3.0-nano-zh"
35
+ [models.embeddings."ernie-base"]
36
+ type = "default"
37
+ path = "nghuyong/ernie-3.0-base-zh"
38
+
39
+
ui/__init__.py ADDED
File without changes
ui/chat.py ADDED
@@ -0,0 +1,207 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+ import os
3
+ import shutil
4
+ from loguru import logger
5
+ from utils.chatpdf import ChatPDF
6
+ import hashlib
7
+ from utils.llm import LLM
8
+ from models import MAX_INPUT_LEN, models
9
+
10
+ pwd_path = os.path.abspath(os.path.dirname(__file__))
11
+
12
+ CONTENT_DIR = os.path.join(pwd_path, "content")
13
+ logger.info(f"CONTENT_DIR: {CONTENT_DIR}")
14
+ VECTOR_SEARCH_TOP_K = 3
15
+
16
+
17
+ def get_file_list():
18
+ if not os.path.exists("content"):
19
+ return []
20
+ return [f for f in os.listdir("content") if
21
+ f.endswith(".txt") or f.endswith(".pdf") or f.endswith(".docx") or f.endswith(".md")]
22
+
23
+
24
+ def upload_file(file, file_list):
25
+ if not os.path.exists(CONTENT_DIR):
26
+ os.mkdir(CONTENT_DIR)
27
+ filename = os.path.basename(file.name)
28
+ shutil.move(file.name, os.path.join(CONTENT_DIR, filename))
29
+ # file_list首位插入新上传的文件
30
+ file_list.insert(0, filename)
31
+ return gr.Dropdown.update(choices=file_list, value=filename), file_list
32
+
33
+
34
+ def parse_text(text):
35
+ """copy from https://github.com/GaiZhenbiao/ChuanhuChatGPT/"""
36
+ lines = text.split("\n")
37
+ lines = [line for line in lines if line != ""]
38
+ count = 0
39
+ for i, line in enumerate(lines):
40
+ if "```" in line:
41
+ count += 1
42
+ items = line.split('`')
43
+ if count % 2 == 1:
44
+ lines[i] = f'<pre><code class="language-{items[-1]}">'
45
+ else:
46
+ lines[i] = f'<br></code></pre>'
47
+ else:
48
+ if i > 0:
49
+ if count % 2 == 1:
50
+ line = line.replace("`", "\`")
51
+ line = line.replace("<", "&lt;")
52
+ line = line.replace(">", "&gt;")
53
+ line = line.replace(" ", "&nbsp;")
54
+ line = line.replace("*", "&ast;")
55
+ line = line.replace("_", "&lowbar;")
56
+ line = line.replace("-", "&#45;")
57
+ line = line.replace(".", "&#46;")
58
+ line = line.replace("!", "&#33;")
59
+ line = line.replace("(", "&#40;")
60
+ line = line.replace(")", "&#41;")
61
+ line = line.replace("$", "&#36;")
62
+ lines[i] = "<br>" + line
63
+ text = "".join(lines)
64
+ return text
65
+
66
+
67
+ def get_answer(
68
+ query,
69
+ index_path,
70
+ history,
71
+ topn: int = VECTOR_SEARCH_TOP_K,
72
+ max_input_size: int = 1024,
73
+ chat_mode: str = "pdf"
74
+ ):
75
+ if not models.is_active():
76
+ return [None, "模型还未加载"], query
77
+ if index_path and chat_mode == "pdf":
78
+ if not models.chatpdf.sim_model.corpus_embeddings:
79
+ models.chatpdf.load_index(index_path)
80
+ response, empty_history, reference_results = models.chatpdf.query(
81
+ llm_model=models.llm_model,
82
+ query=query,
83
+ topn=topn,
84
+ max_input_size=max_input_size
85
+ )
86
+
87
+ logger.debug(f"query: {query}, response with content: {response}")
88
+ for i in range(len(reference_results)):
89
+ r = reference_results[i]
90
+ response += f"\n{r.strip()}"
91
+ response = parse_text(response)
92
+ history = history + [[query, response]]
93
+ else:
94
+ # 未加载文件,仅返回生成模型结果
95
+ response, empty_history = models.llm_model.chat(query, history)
96
+ response = parse_text(response)
97
+ history = history + [[query, response]]
98
+ logger.debug(f"query: {query}, response: {response}")
99
+ return history, ""
100
+
101
+
102
+ def update_status(history, status):
103
+ history = history + [[None, status]]
104
+ logger.info(status)
105
+ return history
106
+
107
+
108
+ def get_file_hash(fpath):
109
+ return hashlib.md5(open(fpath, 'rb').read()).hexdigest()
110
+
111
+
112
+ def get_vector_store(filepath, history, embedding_model):
113
+ logger.info(filepath, history)
114
+ index_path = None
115
+ file_status = ''
116
+ if models.chatpdf is not None:
117
+
118
+ local_file_path = os.path.join(CONTENT_DIR, filepath)
119
+
120
+ local_file_hash = get_file_hash(local_file_path)
121
+ index_file_name = f"{filepath}.{embedding_model}.{local_file_hash}.index.json"
122
+
123
+ local_index_path = os.path.join(CONTENT_DIR, index_file_name)
124
+
125
+ if os.path.exists(local_index_path):
126
+ models.chatpdf.load_index(local_index_path)
127
+ index_path = local_index_path
128
+ file_status = "文件已成功加载,请开始提问"
129
+
130
+ elif os.path.exists(local_file_path):
131
+ models.chatpdf.load_pdf_file(local_file_path)
132
+ models.chatpdf.save_index(local_index_path)
133
+ index_path = local_index_path
134
+ if index_path:
135
+ file_status = "文件索引并成功加载,请开始提问"
136
+ else:
137
+ file_status = "文件未成功加载,请重新上传文件"
138
+ else:
139
+ file_status = "模型未完成加载,请先在加载模型后再导入文件"
140
+
141
+ return index_path, history + [[None, file_status]]
142
+
143
+
144
+ def reset_chat(chatbot, state):
145
+ return None, None
146
+
147
+
148
+ init_message = """欢迎使用 ChatPDF Web UI,可以直接提问或上传文件后提问 """
149
+
150
+
151
+ def chat_ui(embedding_model):
152
+ index_path, file_status, model_status = gr.State(""), gr.State(""), gr.State("")
153
+ file_list = gr.State(get_file_list())
154
+
155
+ with gr.Row():
156
+ with gr.Column(scale=2):
157
+ chatbot = gr.Chatbot([[None, init_message], [None, None]],
158
+ elem_id="chat-box",
159
+ show_label=False).style(height=700)
160
+ query = gr.Textbox(
161
+ show_label=False,
162
+ placeholder="请输入提问内容,按回车进行提交",
163
+ ).style(container=False)
164
+ clear_btn = gr.Button('🔄Clear!', elem_id='clear').style(full_width=True)
165
+
166
+ with gr.Column(scale=1):
167
+ with gr.Row():
168
+ chat_mode = gr.Radio(choices=["chat", "pdf"], value="pdf", label="聊天模式")
169
+
170
+ with gr.Row():
171
+ topn = gr.Slider(1, 100, 20, step=1, label="最大搜索数量")
172
+ max_input_size = gr.Slider(512, 4096, MAX_INPUT_LEN, step=10, label="摘要最大长度")
173
+
174
+ with gr.Tab("select"):
175
+ with gr.Row():
176
+ selectFile = gr.Dropdown(
177
+ file_list.value,
178
+ label="content file",
179
+ interactive=True,
180
+ value=file_list.value[0] if len(file_list.value) > 0 else None
181
+ )
182
+ # get_file_list_btn = gr.Button('🔄').style(width=10)
183
+ with gr.Tab("upload"):
184
+ file = gr.File(
185
+ label="content file",
186
+ file_types=['.txt', '.md', '.docx', '.pdf']
187
+ )
188
+ load_file_button = gr.Button("加载文件")
189
+
190
+ # 将上传的文件保存到content文件夹下,并更新下拉框
191
+ file.upload(
192
+ upload_file,
193
+ inputs=[file, file_list],
194
+ outputs=[selectFile, file_list]
195
+ )
196
+ load_file_button.click(
197
+ get_vector_store,
198
+ show_progress=True,
199
+ inputs=[selectFile, chatbot, embedding_model],
200
+ outputs=[index_path, chatbot],
201
+ )
202
+ query.submit(
203
+ get_answer,
204
+ [query, index_path, chatbot, topn, max_input_size, chat_mode],
205
+ [chatbot, query],
206
+ )
207
+ clear_btn.click(reset_chat, [chatbot, query], [chatbot, query])
ui/summary.py ADDED
@@ -0,0 +1,179 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import re
2
+
3
+ import gradio as gr
4
+ from typing import List
5
+ from models import models
6
+ from loguru import logger
7
+ import re
8
+
9
+ PROMPT_TEMPLATE = """\
10
+ 使用中文{query_str}:
11
+ {context_str}
12
+ """
13
+
14
+
15
+ def get_text_lines(input_txt: str) -> List[str]:
16
+ lines = input_txt.splitlines()
17
+ lines = [line.strip() for line in lines if line.strip()]
18
+ return lines
19
+
20
+
21
+ stop_chars_set = {
22
+ '.', '!', '?', '。', '!', '?', '…', ';', ';', ':', ':',
23
+ '”', '’', ')', '】', '》', '」', '』', '〕', '〉',
24
+ '》', '〗', '〞', '〟', '»', '"', "'", ')', ']', '}'
25
+ }
26
+
27
+
28
+ def split_in_line(input_txt: str, limit_length: int) -> List[str]:
29
+ new_text = ''
30
+ contents = []
31
+ outputs = []
32
+ for text in input_txt:
33
+ new_text += text
34
+ if text in stop_chars_set:
35
+ contents.append(new_text)
36
+ # logger.debug(f"{new_text}")
37
+ new_text = ''
38
+ # logger.debug(f"{input_txt[-1]} {input_txt[-1] not in stop_chars_set} {new_text}")
39
+ if input_txt[-1] not in stop_chars_set:
40
+ contents.append(new_text)
41
+
42
+ text = ""
43
+ text_length = 0
44
+ for idx, content in enumerate(contents):
45
+ text += content
46
+ text_length += len(content)
47
+ if text_length >= limit_length:
48
+ outputs.append(text)
49
+ text = ""
50
+ text_length = 0
51
+ if text_length < limit_length:
52
+ outputs.append(text)
53
+ return outputs
54
+
55
+
56
+ def get_text_limit_length(input_txt: str, max_length: int = 2048) -> List[str]:
57
+ lines = get_text_lines(input_txt)
58
+ output: List[str] = []
59
+ for line in lines:
60
+ if len(line) <= max_length:
61
+ output.append(line)
62
+ else:
63
+ text_lines = split_in_line(line, max_length)
64
+ logger.debug(f"split in line: {len(text_lines)}")
65
+ # logger.debug(f"{line} ==> {text_lines}")
66
+ output.extend(text_lines)
67
+ return output
68
+
69
+
70
+ def split_input_text(input_txt, strip_input_lines=0, max_length=2048):
71
+ if strip_input_lines > 0:
72
+ pattern = r'[\r\n]{' + str(strip_input_lines) + r',}'
73
+ re.compile(pattern=pattern)
74
+ logger.debug(f"strip input txt: {pattern}")
75
+ input_txt = re.sub(pattern, '', input_txt)
76
+ lines = get_text_limit_length(input_txt, max_length)
77
+ logger.debug(f"split input txt: {len(lines)}")
78
+ return "\n\n\n".join(lines)
79
+
80
+
81
+ def gen_keyword_summary(input_txt, keyword_prompt, summary_prompt, max_length=2048):
82
+ lines = input_txt.split("\n\n\n")
83
+ keywords_output = []
84
+ for line in lines:
85
+ keywords = models.llm_model.generate_answer(
86
+ keyword_prompt,
87
+ line,
88
+ history=None,
89
+ max_length=max_length,
90
+ prompt_template=PROMPT_TEMPLATE
91
+ )[0]
92
+ logger.debug(f"text len: {len(line)} ==> {keywords}")
93
+ keywords_output.extend(keywords.split())
94
+ keywords_output = [keyword.strip() for keyword in keywords_output if keyword.strip() != ""]
95
+ keywords_output = list(set(keywords_output))
96
+ return f"保留关键词:{' '.join(keywords_output)},{summary_prompt}"
97
+
98
+
99
+ def gen_summary(input_txt, summary_prompt, max_length=2048):
100
+ lines = input_txt.split("\n\n\n")
101
+ output_summary = []
102
+ summary = ""
103
+ for idx, line in enumerate(lines):
104
+ if idx == 1:
105
+ summary = models.llm_model.generate_answer(
106
+ summary_prompt,
107
+ line,
108
+ history=None,
109
+ max_length=max_length,
110
+ prompt_template=PROMPT_TEMPLATE
111
+ )[0]
112
+ logger.debug(f"text len: {len(line)} ==> {summary}")
113
+ else:
114
+ summary = models.llm_model.generate_answer(
115
+ summary_prompt,
116
+ f"{summary}{line}",
117
+ history=None,
118
+ max_length=max_length,
119
+ prompt_template=PROMPT_TEMPLATE
120
+ )[0]
121
+ logger.debug(f"summary: {len(summary)} + text: {len(line)} ==> {summary}")
122
+ output_summary.append(summary)
123
+
124
+ return "\n\n\n".join(output_summary)
125
+
126
+
127
+ def summary_ui():
128
+ with gr.Row():
129
+ with gr.Column(scale=1):
130
+ line_max_length = gr.Slider(minimum=512, maximum=4096, step=1, value=1024, label="每行最大长度")
131
+ strip_input_lines = gr.Slider(
132
+ label="去除输入文本连续的空行(0:不除去)",
133
+ minimum=1,
134
+ maximum=10,
135
+ step=1,
136
+ value=0
137
+ )
138
+ with gr.Column(scale=4):
139
+ keyword_prompt = gr.Textbox(
140
+ lines=1,
141
+ label="抽取关键词",
142
+ value="抽取以下内容的人物和地点:",
143
+ placeholder="请输入抽取关键词的Prompt"
144
+ )
145
+ summary_prompt = gr.Textbox(
146
+ lines=2,
147
+ label="生成摘要",
148
+ value="生成以下内容的摘要:",
149
+ placeholder="请输入生成摘要的Prompt"
150
+ )
151
+ keyword_summary_prompt = gr.Textbox(lines=4, label="关键词+摘要", placeholder="请输入关键词+摘要的Prompt")
152
+
153
+ with gr.Row():
154
+ input_text = gr.Textbox(lines=20, max_lines=60, label="输入文本", placeholder="请输入文本")
155
+ split_text = gr.Textbox(lines=20, max_lines=60, label="分段文本", placeholder="请输入分段文本")
156
+ summary = gr.Textbox(lines=20, max_lines=60, label="生成摘要", placeholder="请输入生成摘要的Prompt")
157
+
158
+ with gr.Row():
159
+ btn_split = gr.Button("分段")
160
+ btn_keyword = gr.Button("提取关键词")
161
+ btn_summary = gr.Button("生成摘要")
162
+
163
+ btn_split.click(
164
+ split_input_text,
165
+ inputs=[input_text, strip_input_lines, line_max_length],
166
+ outputs=[split_text]
167
+ )
168
+
169
+ btn_summary.click(
170
+ gen_summary,
171
+ inputs=[split_text, keyword_summary_prompt, line_max_length],
172
+ outputs=[summary]
173
+ )
174
+
175
+ btn_keyword.click(
176
+ gen_keyword_summary,
177
+ inputs=[split_text, keyword_prompt, summary_prompt, line_max_length],
178
+ outputs=[keyword_summary_prompt]
179
+ )
utils/__init__.py ADDED
File without changes
utils/chatpdf.py ADDED
@@ -0,0 +1,157 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from similarities import Similarity
2
+ from textgen import ChatGlmModel, LlamaModel
3
+ from transformers import pipeline
4
+ from loguru import logger
5
+ import torch
6
+
7
+ device_id = 0 if torch.cuda.is_available() else -1
8
+
9
+ PROMPT_TEMPLATE = """\
10
+ 基于以下已知信息,简洁和专业的来回答用户的问题。
11
+ 如果无法从中得到答案,请说 "根据已知信息无法回答该问题" 或 "没有提供足够的相关信息",不允许在答案中添加编造成分,答案请使用中文。
12
+
13
+ 已知内容:
14
+ {context_str}
15
+
16
+ 问题:
17
+ {query_str}
18
+ """
19
+
20
+
21
+ class ChatPDF:
22
+ def __init__(
23
+ self,
24
+ sim_model_name_or_path: str = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
25
+
26
+ ):
27
+ self.sim_model = Similarity(model_name_or_path=sim_model_name_or_path)
28
+
29
+ self.history = None
30
+ self.pdf_path = None
31
+
32
+ def load_pdf_file(self, pdf_path: str):
33
+ """Load a PDF file."""
34
+ if pdf_path.endswith('.pdf'):
35
+ corpus = self.extract_text_from_pdf(pdf_path)
36
+ elif pdf_path.endswith('.docx'):
37
+ corpus = self.extract_text_from_docx(pdf_path)
38
+ elif pdf_path.endswith('.md'):
39
+ corpus = self.extract_text_from_markdown(pdf_path)
40
+ else:
41
+ corpus = self.extract_text_from_txt(pdf_path)
42
+ self.sim_model.add_corpus(corpus)
43
+ self.pdf_path = pdf_path
44
+
45
+ @staticmethod
46
+ def extract_text_from_pdf(file_path: str):
47
+ """Extract text content from a PDF file."""
48
+ import PyPDF2
49
+ contents = []
50
+ with open(file_path, 'rb') as f:
51
+ pdf_reader = PyPDF2.PdfReader(f)
52
+ for page in pdf_reader.pages:
53
+ page_text = page.extract_text().strip()
54
+ raw_text = [text.strip() for text in page_text.splitlines() if text.strip()]
55
+ new_text = ''
56
+ for text in raw_text:
57
+ new_text += text
58
+ if text[-1] in ['.', '!', '?', '。', '!', '?', '…', ';', ';', ':', ':', '”', '’', ')', '】', '》', '」',
59
+ '』', '〕', '〉', '》', '〗', '〞', '〟', '»', '"', "'", ')', ']', '}']:
60
+ contents.append(new_text)
61
+ new_text = ''
62
+ if new_text:
63
+ contents.append(new_text)
64
+ return contents
65
+
66
+ @staticmethod
67
+ def extract_text_from_txt(file_path: str):
68
+ """Extract text content from a TXT file."""
69
+ contents = []
70
+ with open(file_path, 'r', encoding='utf-8') as f:
71
+ contents = [text.strip() for text in f.readlines() if text.strip()]
72
+ return contents
73
+
74
+ @staticmethod
75
+ def extract_text_from_docx(file_path: str):
76
+ """Extract text content from a DOCX file."""
77
+ import docx
78
+ document = docx.Document(file_path)
79
+ contents = [paragraph.text.strip() for paragraph in document.paragraphs if paragraph.text.strip()]
80
+ return contents
81
+
82
+ @staticmethod
83
+ def extract_text_from_markdown(file_path: str):
84
+ """Extract text content from a Markdown file."""
85
+ import markdown
86
+ from bs4 import BeautifulSoup
87
+ with open(file_path, 'r', encoding='utf-8') as f:
88
+ markdown_text = f.read()
89
+ html = markdown.markdown(markdown_text)
90
+ soup = BeautifulSoup(html, 'html.parser')
91
+ contents = [text.strip() for text in soup.get_text().splitlines() if text.strip()]
92
+ return contents
93
+
94
+ @staticmethod
95
+ def _add_source_numbers(lst):
96
+ """Add source numbers to a list of strings."""
97
+ return [f'[{idx + 1}]\t "{item}"' for idx, item in enumerate(lst)]
98
+
99
+ def query(
100
+ self,
101
+ llm_model,
102
+ query,
103
+ topn: int = 5,
104
+ max_length: int = 1024,
105
+ max_input_size: int = 1024,
106
+ use_history: bool = False,
107
+
108
+ ):
109
+ """Query from corpus."""
110
+
111
+ sim_contents = self.sim_model.most_similar(query, topn=topn)
112
+
113
+ reference_results = []
114
+ for query_id, id_score_dict in sim_contents.items():
115
+ for corpus_id, s in id_score_dict.items():
116
+ reference_results.append(self.sim_model.corpus[corpus_id])
117
+ if not reference_results:
118
+ return '没有提供足够的相关信息', reference_results
119
+ reference_results = self._add_source_numbers(reference_results)
120
+
121
+ context_str = '\n'.join(reference_results)[:(max_input_size - len(PROMPT_TEMPLATE))]
122
+
123
+ if use_history:
124
+ response, out_history = llm_model.generate_answer(query, context_str, self.history, max_length=max_length)
125
+ self.history = out_history
126
+ else:
127
+ response, out_history = llm_model.generate_answer(query, context_str)
128
+
129
+ return response, out_history, reference_results
130
+
131
+ def save_index(self, index_path=None):
132
+ """Save model."""
133
+ if index_path is None:
134
+ index_path = '.'.join(self.pdf_path.split('.')[:-1]) + '_index.json'
135
+ self.sim_model.save_index(index_path)
136
+
137
+ def load_index(self, index_path=None):
138
+ """Load model."""
139
+ if index_path is None:
140
+ index_path = '.'.join(self.pdf_path.split('.')[:-1]) + '_index.json'
141
+ self.sim_model.load_index(index_path)
142
+
143
+
144
+ if __name__ == "__main__":
145
+ import sys
146
+
147
+ if len(sys.argv) > 2:
148
+ gen_model_name_or_path = sys.argv[1]
149
+ else:
150
+ print('Usage: python chatpdf.py <gen_model_name_or_path>')
151
+ gen_model_name_or_path = "THUDM/chatglm-6b-int4"
152
+ m = ChatPDF(gen_model_name_or_path=gen_model_name_or_path)
153
+ m.load_pdf_file(pdf_path='sample.pdf')
154
+ response = m.query('自然语言中的非平行迁移是指什么?')
155
+ print(response[0])
156
+ response = m.query('本文作者是谁?')
157
+ print(response[0])
utils/llm.py ADDED
@@ -0,0 +1,50 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from textgen import ChatGlmModel, LlamaModel
2
+ from loguru import logger
3
+
4
+
5
+ class LLM(object):
6
+ def __init__(
7
+ self,
8
+ gen_model_type: str = "chatglm",
9
+ gen_model_name_or_path: str = "THUDM/chatglm-6b-int4",
10
+ lora_model_name_or_path: str = None,
11
+
12
+ ):
13
+
14
+ self.model_type = gen_model_type
15
+
16
+ if gen_model_type == "chatglm":
17
+ self.gen_model = ChatGlmModel(
18
+ gen_model_type,
19
+ gen_model_name_or_path,
20
+ lora_name=lora_model_name_or_path,
21
+ )
22
+ elif gen_model_type == "llama":
23
+
24
+ self.gen_model = LlamaModel(
25
+ gen_model_type,
26
+ gen_model_name_or_path,
27
+ lora_name=lora_model_name_or_path,
28
+ )
29
+
30
+ else:
31
+ raise ValueError('gen_model_type must be chatglm or llama.')
32
+ self.history = None
33
+
34
+ def generate_answer(self, query_str, context_str, history=None, max_length=1024, prompt_template=None):
35
+ """Generate answer from query and context."""
36
+ if self.model_type == "t5":
37
+ response = self.gen_model(query_str, max_length=max_length, do_sample=True)[0]['generated_text']
38
+ return response, history
39
+ prompt = prompt_template.format(context_str=context_str, query_str=query_str)
40
+ response, out_history = self.gen_model.chat(prompt, history, max_length=max_length)
41
+ return response, out_history
42
+
43
+ def chat(self, query_str, history=None, max_length=1024):
44
+ if self.model_type == "t5":
45
+ response = self.gen_model(query_str, max_length=max_length, do_sample=True)[0]['generated_text']
46
+ logger.debug(response)
47
+ return response, history
48
+
49
+ response, out_history = self.gen_model.chat(query_str, history, max_length=max_length)
50
+ return response, out_history
utils/singleton.py ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ class Singleton:
2
+ """
3
+ A non-thread-safe helper class to ease implementing singletons.
4
+ This should be used as a decorator -- not a metaclass -- to the
5
+ class that should be a singleton.
6
+
7
+ The decorated class can define one `__init__` function that
8
+ takes only the `self` argument. Also, the decorated class cannot be
9
+ inherited from. Other than that, there are no restrictions that apply
10
+ to the decorated class.
11
+
12
+ To get the singleton instance, use the `instance` method. Trying
13
+ to use `__call__` will result in a `TypeError` being raised.
14
+
15
+ """
16
+
17
+ def __init__(self, decorated):
18
+ self._decorated = decorated
19
+
20
+ def instance(self):
21
+ """
22
+ Returns the singleton instance. Upon its first call, it creates a
23
+ new instance of the decorated class and calls its `__init__` method.
24
+ On all subsequent calls, the already created instance is returned.
25
+
26
+ """
27
+ try:
28
+ return self._instance
29
+ except AttributeError:
30
+ self._instance = self._decorated()
31
+ return self._instance
32
+
33
+ def __call__(self):
34
+ raise TypeError('Singletons must be accessed through `instance()`.')
35
+
36
+ def __instancecheck__(self, inst):
37
+ return isinstance(inst, self._decorated)