Commit
•
1879130
1
Parent(s):
b073f6b
Upload 8 files
Browse files- .dockerignore +34 -0
- Dockerfile +63 -0
- ERROR +0 -0
- README.Docker.md +22 -0
- compose.yaml +49 -0
- main.py +100 -0
- requirements.txt +6 -0
- test_main.py +70 -0
.dockerignore
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# Include any files or directories that you don't want to be copied to your
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# container here (e.g., local build artifacts, temporary files, etc.).
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#
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# For more help, visit the .dockerignore file reference guide at
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# https://docs.docker.com/go/build-context-dockerignore/
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**/.DS_Store
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**/__pycache__
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**/.venv
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**/.classpath
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**/.dockerignore
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**/.env
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**/.git
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**/.gitignore
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**/.project
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**/.settings
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**/.toolstarget
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**/.vs
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**/.vscode
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**/*.*proj.user
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**/*.dbmdl
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**/*.jfm
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**/bin
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**/charts
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**/docker-compose*
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**/compose*
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**/Dockerfile*
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**/node_modules
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**/npm-debug.log
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**/obj
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**/secrets.dev.yaml
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**/values.dev.yaml
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LICENSE
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README.md
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Dockerfile
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# syntax=docker/dockerfile:1
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# Comments are provided throughout this file to help you get started.
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# If you need more help, visit the Dockerfile reference guide at
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# https://docs.docker.com/go/dockerfile-reference/
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# Want to help us make this template better? Share your feedback here: https://forms.gle/ybq9Krt8jtBL3iCk7
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ARG PYTHON_VERSION=3.11.9
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FROM python:${PYTHON_VERSION}-slim as base
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# Prevents Python from writing pyc files.
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ENV PYTHONDONTWRITEBYTECODE=1
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# Keeps Python from buffering stdout and stderr to avoid situations where
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# the application crashes without emitting any logs due to buffering.
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ENV PYTHONUNBUFFERED=1
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WORKDIR /app
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# Create a non-privileged user that the app will run under.
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# See https://docs.docker.com/go/dockerfile-user-best-practices/
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ARG UID=10001
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RUN adduser \
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--disabled-password \
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--gecos "" \
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--home "/nonexistent" \
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--shell "/sbin/nologin" \
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--no-create-home \
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--uid "${UID}" \
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appuser
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# Download dependencies as a separate step to take advantage of Docker's caching.
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# Leverage a cache mount to /root/.cache/pip to speed up subsequent builds.
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# Leverage a bind mount to requirements.txt to avoid having to copy them into
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# into this layer.
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RUN --mount=type=cache,target=/root/.cache/pip \
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--mount=type=bind,source=requirements.txt,target=requirements.txt \
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python -m pip install -r requirements.txt
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# Switch to the non-privileged user to run the application.
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USER appuser
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# Set the TRANSFORMERS_CACHE environment variable
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ENV TRANSFORMERS_CACHE=/tmp/.cache/huggingface
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# Create the cache folder with appropriate permissions
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RUN mkdir -p $TRANSFORMERS_CACHE && chmod -R 777 $TRANSFORMERS_CACHE
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# Set NLTK data directory
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ENV NLTK_DATA=/tmp/nltk_data
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# Create the NLTK data directory with appropriate permissions
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RUN mkdir -p $NLTK_DATA && chmod -R 777 $NLTK_DATA
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# Copy the source code into the container.
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COPY . .
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# Expose the port that the application listens on.
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EXPOSE 8000
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# Run the application.
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CMD uvicorn 'main:app' --host=0.0.0.0 --port=7860
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ERROR
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README.Docker.md
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### Building and running your application
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When you're ready, start your application by running:
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`docker compose up --build`.
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Your application will be available at http://localhost:7860 .
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### Deploying your application to the cloud
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First, build your image, e.g.: `docker build -t myapp .`.
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If your cloud uses a different CPU architecture than your development
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machine (e.g., you are on a Mac M1 and your cloud provider is amd64),
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you'll want to build the image for that platform, e.g.:
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`docker build --platform=linux/amd64 -t myapp .`.
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Then, push it to your registry, e.g. `docker push myregistry.com/myapp`.
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Consult Docker's [getting started](https://docs.docker.com/go/get-started-sharing/)
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docs for more detail on building and pushing.
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### References
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* [Docker's Python guide](https://docs.docker.com/language/python/)
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compose.yaml
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# Comments are provided throughout this file to help you get started.
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# If you need more help, visit the Docker Compose reference guide at
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# https://docs.docker.com/go/compose-spec-reference/
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# Here the instructions define your application as a service called "server".
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# This service is built from the Dockerfile in the current directory.
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# You can add other services your application may depend on here, such as a
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# database or a cache. For examples, see the Awesome Compose repository:
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# https://github.com/docker/awesome-compose
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services:
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server:
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build:
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context: .
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ports:
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- 8000:8000
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# The commented out section below is an example of how to define a PostgreSQL
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# database that your application can use. `depends_on` tells Docker Compose to
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# start the database before your application. The `db-data` volume persists the
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# database data between container restarts. The `db-password` secret is used
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# to set the database password. You must create `db/password.txt` and add
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# a password of your choosing to it before running `docker compose up`.
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# depends_on:
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# db:
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# condition: service_healthy
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# db:
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# image: postgres
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# restart: always
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# user: postgres
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# secrets:
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# - db-password
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# volumes:
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# - db-data:/var/lib/postgresql/data
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# environment:
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# - POSTGRES_DB=example
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# - POSTGRES_PASSWORD_FILE=/run/secrets/db-password
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# expose:
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# - 5432
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# healthcheck:
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# test: [ "CMD", "pg_isready" ]
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# interval: 10s
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# timeout: 5s
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# retries: 5
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# volumes:
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# db-data:
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# secrets:
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# db-password:
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# file: db/password.txt
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main.py
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from contextlib import asynccontextmanager
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel, ValidationError
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from fastapi.encoders import jsonable_encoder
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# TEXT PREPROCESSING
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# --------------------------------------------------------------------
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import re
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import string
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import nltk
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nltk.download('punkt')
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nltk.download('wordnet')
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nltk.download('omw-1.4')
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from nltk.stem import WordNetLemmatizer
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# Function to remove URLs from text
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def remove_urls(text):
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return re.sub(r'http[s]?://\S+', '', text)
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# Function to remove punctuations from text
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def remove_punctuation(text):
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regular_punct = string.punctuation
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return str(re.sub(r'['+regular_punct+']', '', str(text)))
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# Function to convert the text into lower case
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def lower_case(text):
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return text.lower()
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# Function to lemmatize text
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def lemmatize(text):
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wordnet_lemmatizer = WordNetLemmatizer()
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tokens = nltk.word_tokenize(text)
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lemma_txt = ''
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for w in tokens:
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lemma_txt = lemma_txt + wordnet_lemmatizer.lemmatize(w) + ' '
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return lemma_txt
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def preprocess_text(text):
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# Preprocess the input text
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text = remove_urls(text)
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text = remove_punctuation(text)
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text = lower_case(text)
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text = lemmatize(text)
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return text
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# Load the model using FastAPI lifespan event so that the model is loaded at the beginning for efficiency
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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# Load the model from HuggingFace transformers library
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from transformers import pipeline
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global sentiment_task
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sentiment_task = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment-latest", tokenizer="cardiffnlp/twitter-roberta-base-sentiment-latest")
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yield
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# Clean up the model and release the resources
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del sentiment_task
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# Initialize the FastAPI app
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app = FastAPI(lifespan=lifespan)
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# Define the input data model
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class TextInput(BaseModel):
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text: str
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# Define the welcome endpoint
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@app.get('/')
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async def welcome():
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return "Welcome to our Text Classification API"
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# Validate input text length
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MAX_TEXT_LENGTH = 1000
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# Define the sentiment analysis endpoint
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@app.post('/analyze/{text}')
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async def classify_text(text_input:TextInput):
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try:
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# Convert input data to JSON serializable dictionary
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text_input_dict = jsonable_encoder(text_input)
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# Validate input data using Pydantic model
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text_data = TextInput(**text_input_dict) # Convert to Pydantic model
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# Validate input text length
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if len(text_input.text) > MAX_TEXT_LENGTH:
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raise HTTPException(status_code=400, detail="Text length exceeds maximum allowed length")
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elif len(text_input.text) == 0:
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raise HTTPException(status_code=400, detail="Text cannot be empty")
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except ValidationError as e:
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# Handle validation error
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raise HTTPException(status_code=422, detail=str(e))
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+
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try:
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# Perform text classification
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return sentiment_task(preprocess_text(text_input.text))
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except ValueError as ve:
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96 |
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# Handle value error
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97 |
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raise HTTPException(status_code=400, detail=str(ve))
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except Exception as e:
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# Handle other server errors
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raise HTTPException(status_code=500, detail=str(e))
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requirements.txt
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fastapi
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uvicorn
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nltk
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pydantic
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transformers
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+
torch
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test_main.py
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from fastapi.testclient import TestClient
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from main import app
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3 |
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from main import TextInput
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4 |
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from fastapi.encoders import jsonable_encoder
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5 |
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6 |
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client = TestClient(app)
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7 |
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# Test the welcome endpoint
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9 |
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def test_welcome():
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10 |
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# Test the welcome endpoint
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11 |
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response = client.get("/")
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assert response.status_code == 200
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13 |
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assert response.json() == "Welcome to our Text Classification API"
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14 |
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15 |
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# Test the sentiment analysis endpoint for positive sentiment
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16 |
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def test_positive_sentiment():
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17 |
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with client:
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18 |
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# Define the request payload
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19 |
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# Initialize payload as a TextInput object
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20 |
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payload = TextInput(text="I love this product! It's amazing!")
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21 |
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# Convert TextInput object to JSON-serializable dictionary
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23 |
+
payload_dict = jsonable_encoder(payload)
|
24 |
+
|
25 |
+
# Send a POST request to the sentiment analysis endpoint
|
26 |
+
response = client.post("/analyze/{text}", json=payload_dict)
|
27 |
+
|
28 |
+
# Assert that the response status code is 200 OK
|
29 |
+
assert response.status_code == 200
|
30 |
+
|
31 |
+
# Assert that the sentiment returned is positive
|
32 |
+
assert response.json()[0]['label'] == "positive"
|
33 |
+
|
34 |
+
# Test the sentiment analysis endpoint for negative sentiment
|
35 |
+
def test_negative_sentiment():
|
36 |
+
with client:
|
37 |
+
# Define the request payload
|
38 |
+
# Initialize payload as a TextInput object
|
39 |
+
payload = TextInput(text="I'm really disappointed with this service. It's terrible.")
|
40 |
+
|
41 |
+
# Convert TextInput object to JSON-serializable dictionary
|
42 |
+
payload_dict = jsonable_encoder(payload)
|
43 |
+
|
44 |
+
# Send a POST request to the sentiment analysis endpoint
|
45 |
+
response = client.post("/analyze/{text}", json=payload_dict)
|
46 |
+
|
47 |
+
# Assert that the response status code is 200 OK
|
48 |
+
assert response.status_code == 200
|
49 |
+
|
50 |
+
# Assert that the sentiment returned is positive
|
51 |
+
assert response.json()[0]['label'] == "negative"
|
52 |
+
|
53 |
+
# Test the sentiment analysis endpoint for neutral sentiment
|
54 |
+
def test_neutral_sentiment():
|
55 |
+
with client:
|
56 |
+
# Define the request payload
|
57 |
+
# Initialize payload as a TextInput object
|
58 |
+
payload = TextInput(text="This is a neutral statement.")
|
59 |
+
|
60 |
+
# Convert TextInput object to JSON-serializable dictionary
|
61 |
+
payload_dict = jsonable_encoder(payload)
|
62 |
+
|
63 |
+
# Send a POST request to the sentiment analysis endpoint
|
64 |
+
response = client.post("/analyze/{text}", json=payload_dict)
|
65 |
+
|
66 |
+
# Assert that the response status code is 200 OK
|
67 |
+
assert response.status_code == 200
|
68 |
+
|
69 |
+
# Assert that the sentiment returned is positive
|
70 |
+
assert response.json()[0]['label'] == "neutral"
|