feat(server): add web server and docker container

Author: AmitMY

.github/workflows/publish-docker.yaml

@@ -0,0 +1,61 @@
+
+# Adapted from https://docs.github.com/en/actions/tutorials/publishing-packages/publishing-docker-images
+name: Publish a Docker image
+
+# Configures this workflow to run every time a new release is created in the repository.
+on:
+  release:
+    types: [ created ]
+
+# Defines two custom environment variables for the workflow. These are used for the Container registry domain, and a name for the Docker image that this workflow builds.
+env:
+  REGISTRY: ghcr.io
+  IMAGE_NAME: ${{ github.repository }}
+
+# There is a single job in this workflow. It's configured to run on the latest available version of Ubuntu.
+jobs:
+  build-and-push-image:
+    runs-on: ubuntu-latest
+    # Sets the permissions granted to the `GITHUB_TOKEN` for the actions in this job.
+    permissions:
+      contents: read
+      packages: write
+      attestations: write
+      id-token: write
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+      # Uses the `docker/login-action` action to log in to the Container registry registry using the account and password that will publish the packages. Once published, the packages are scoped to the account defined here.
+      - name: Log in to the Container registry
+        uses: docker/login-action@65b78e6e13532edd9afa3aa52ac7964289d1a9c1
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+      # This step uses [docker/metadata-action](https://github.com/docker/metadata-action#about) to extract tags and labels that will be applied to the specified image. The `id` "meta" allows the output of this step to be referenced in a subsequent step. The `images` value provides the base name for the tags and labels.
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@9ec57ed1fcdbf14dcef7dfbe97b2010124a938b7
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+      # This step uses the `docker/build-push-action` action to build the image, based on your repository's `Dockerfile`. If the build succeeds, it pushes the image to GitHub Packages.
+      # It uses the `context` parameter to define the build's context as the set of files located in the specified path. For more information, see [Usage](https://github.com/docker/build-push-action#usage) in the README of the `docker/build-push-action` repository.
+      # It uses the `tags` and `labels` parameters to tag and label the image with the output from the "meta" step.
+      - name: Build and push Docker image
+        id: push
+        uses: docker/build-push-action@f2a1d5e99d037542a71f64918e516c093c6f3fc4
+        with:
+          context: .
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+
+      # This step generates an artifact attestation for the image, which is an unforgeable statement about where and how it was built. It increases supply chain security for people who consume the image. For more information, see [Using artifact attestations to establish provenance for builds](/actions/security-guides/using-artifact-attestations-to-establish-provenance-for-builds).
+      - name: Generate artifact attestation
+        uses: actions/attest-build-provenance@v2
+        with:
+          subject-name: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME}}
+          subject-digest: ${{ steps.push.outputs.digest }}
+          push-to-registry: true
+

Dockerfile

@@ -0,0 +1,23 @@
+FROM python:3.12-slim
+
+# Allow statements and log messages to immediately appear in the Knative logs
+ENV PYTHONUNBUFFERED True
+
+# Setup local workdir and dependencies
+WORKDIR /app
+
+# Install python dependencies.
+ADD ./pyproject.toml ./pyproject.toml
+RUN mkdir -p sign_language_segmentation/src/utils
+RUN touch README.md
+RUN pip install --no-cache-dir ".[server]"
+
+# Copy local code to the container image.
+COPY ./sign_language_segmentation ./sign_language_segmentation
+
+# Run the web service on container startup. Here we use the gunicorn
+# webserver, with one worker process and 8 threads.
+# For environments with multiple CPU cores, increase the number of workers
+# to be equal to the cores available.
+# Timeout is set to 0 to disable the timeouts of the workers to allow Cloud Run to handle instance scaling.
+CMD exec gunicorn --bind :$PORT --workers 1 --threads 8 --timeout 0 sign_language_segmentation.server:app

README.md

@@ -18,6 +18,20 @@ To create an ELAN file with sign and sentence segments:
 pose_to_segments --pose="sign.pose" --elan="sign.eaf" [--video="sign.mp4"]
 ```
 
+### Web Server on Docker
+
+```bash
+docker build -t segmentation .
+
+docker run --rm -p 9876:8080 -e PORT=8080 \
+  -v $(pwd)/sign_language_segmentation/tests:/mnt/examples \
+  segmentation
+
+curl -X POST http://localhost:9876/ \
+    -H "Content-Type: application/json" \
+    -d '{"input": "/mnt/examples/example.pose", "output": "/mnt/examples/example.eaf"}'
+```
+
 ---
 
 ## Main Idea

pyproject.toml

@@ -27,6 +27,12 @@ dev = [
     "pandas"
 ]
 
+server = [
+    "Flask",
+    "Werkzeug",
+    "gunicorn",
+]
+
 [tool.yapf]
 based_on_style = "google"
 column_limit = 120

sign_language_segmentation/server.py

@@ -0,0 +1,64 @@
+import os
+import traceback
+from pathlib import Path
+
+from flask import Flask, request, abort, make_response, jsonify
+from pose_format import Pose
+
+from sign_language_segmentation.bin import segment_pose
+
+app = Flask(__name__)
+
+
+def resolve_path(uri: str):
+    # Map gs:// URIs to the gcsfuse mount point, or return as-is
+    return uri.replace("gs://", "/mnt/")
+
+
+@app.errorhandler(Exception)
+def handle_exception(e):
+    print("Exception", e)
+    traceback.print_exc()
+
+    code = e.code if hasattr(e, "code") else 500
+    message = str(e)
+    print("HTTP exception", code, message)
+
+    return make_response(jsonify(message=message, code=code), code)
+
+
+@app.route("/", methods=['POST'])
+def pose_segmentation():
+    # Get request parameters
+    body = request.get_json()
+    for param in ['input', 'output']:
+        if param not in body:
+            abort(make_response(jsonify(message=f"Missing `{param}` body property"), 400))
+
+    # Check if output file already exists
+    output_file_path = Path(resolve_path(body["output"]))
+    if output_file_path.exists():
+        return make_response(jsonify(message="Output file already exists", path=body["output"]), 208)
+
+    # Check if input file exists at all
+    pose_file_path = Path(resolve_path(body["input"]))
+    if not pose_file_path.exists():
+        raise Exception("File does not exist")
+
+    with pose_file_path.open("rb") as f:
+        pose = Pose.read(f)
+
+    eaf, tiers = segment_pose(pose)
+
+    output_file_path.parent.mkdir(parents=True, exist_ok=True)
+    print("Saving .eaf to disk ...")
+    eaf.to_file(output_file_path)
+
+    return make_response(jsonify(
+        message="Pose segmentation completed successfully",
+        path=body["output"],
+    ), 200)
+
+
+if __name__ == "__main__":
+    app.run(debug=True, host="0.0.0.0", port=int(os.environ.get("PORT", 8080)))