init

.gitignore

@@ -0,0 +1,162 @@
+### Python template
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/
+

classification.py

@@ -0,0 +1,31 @@
+from pprint import pprint
+
+from pydantic import BaseModel
+from transformers import pipeline
+
+classifier = pipeline("zero-shot-classification",
+                      model="morit/chinese_xlm_xnli")
+
+
+# 返回一个结构化的内容
+class ClassifyResult(BaseModel):
+    sequence: str
+    labels: list
+    scores: list
+    prediction: str
+
+
+def classify(text: str, labels: list):
+    output = classifier(text, labels)
+
+    pprint(output)
+
+    # 根据 score，寻找最高的 label
+    prediction_rank = output['scores'].index(max(output['scores']))
+
+    return ClassifyResult(
+        sequence=text,
+        labels=output['labels'],
+        scores=output['scores'],
+        prediction=output['labels'][prediction_rank]
+    )

main.py

@@ -0,0 +1,29 @@
+from typing import Union
+
+import uvicorn
+from fastapi import FastAPI
+from pydantic import BaseModel
+import classification
+
+app = FastAPI()
+
+class TextClassificationRequest(BaseModel):
+    text: str
+    labels: list[str]
+
+class TextClassificationResponse(BaseModel):
+    prediction: str
+    labels: list[str]
+
+
+
+@app.post("/classify")
+def classify(req: TextClassificationRequest) -> TextClassificationResponse:
+    result = classification.classify(req.text, req.labels)
+
+    return TextClassificationResponse(prediction=result.labels[0], labels=result.labels)
+
+
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)

protos/classification.proto

@@ -0,0 +1,17 @@
+syntax = "proto3";
+
+service TextClassification {
+  rpc Classify(TextClassificationRequest) returns (TextClassificationResponse) {}
+}
+
+message TextClassificationRequest {
+  string text = 1;
+  repeated string labels = 2;
+}
+
+message TextClassificationResponse {
+  string sequence = 1;
+  string match = 2;
+  repeated string labels = 3;
+  repeated float scores = 4;
+}