Merge pull request 'docker_webserver' (#10) from docker_webserver into main
Reviewed-on: http://192.168.1.2:3000/brian/visual_critical_discourse_analysis/pulls/10
This commit was merged in pull request #10.
This commit is contained in:
Brian Bjarke Jensen
+1
-1
@@ -121,7 +121,7 @@ celerybeat.pid
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*.sage.py
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*.sage.py
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# Environments
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# Environments
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.env
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*.env
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||||||
.venv
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.venv
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env/
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env/
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||||||
venv/
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venv/
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||||||
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|||||||
+3
-3
@@ -34,7 +34,7 @@ ENV PATH="${POETRY_HOME}/bin:$PATH"
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# install runtime dependencies
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# install runtime dependencies
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WORKDIR ${APP_HOME}
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WORKDIR ${APP_HOME}
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COPY poetry.lock pyproject.toml ./
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COPY poetry.lock pyproject.toml ./
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RUN --mount=type=cache,target=${POETRY_CACHE_DIR} poetry install --no-root
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RUN --mount=type=cache,target=${POETRY_CACHE_DIR} poetry install
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||||||
#########
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#########
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# FINAL #
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# FINAL #
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@@ -56,9 +56,9 @@ RUN mkdir -p /home/app && \
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# add code while changing ownership
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# add code while changing ownership
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WORKDIR $APP_HOME
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WORKDIR $APP_HOME
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COPY --chown=app:app ./src .
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COPY --chown=app:app ./src ./src
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# change to the app user
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# change to the app user
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USER app
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USER app
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ENTRYPOINT [ "gunicorn", "web:server", "-b", "0.0.0.0:8050" ]
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ENTRYPOINT [ "gunicorn", "src.main:server", "-b", "0.0.0.0:8050" ]
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@@ -1,3 +1,4 @@
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version: '3.7'
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services:
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services:
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app:
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app:
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image: visual_critical_discourse_analysis:dev
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image: visual_critical_discourse_analysis:dev
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@@ -5,11 +6,36 @@ services:
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build:
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build:
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context: .
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context: .
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dockerfile: Dockerfile
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dockerfile: Dockerfile
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env_file:
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||||||
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- local.env
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ports:
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ports:
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- 8050:8050
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- 8050:8050
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networks:
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networks:
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- backend
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- backend
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depends_on:
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- mongo
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mongo:
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image: mongo:latest
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container_name: mongo
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env_file:
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- local.env
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ports:
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- "27017:27017"
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networks:
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- backend
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mongo-express:
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image: mongo-express
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ports:
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||||||
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- 8081:8081
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env_file:
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||||||
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- local.env
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links:
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- mongo
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networks:
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- backend
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depends_on:
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- mongo
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||||||
|
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networks:
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networks:
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backend:
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backend:
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external: false
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driver: bridge
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@@ -0,0 +1,12 @@
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version: '3.7'
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services:
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app:
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image: visual_critical_discourse_analysis:dev
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container_name: visual_critical_discourse_analysis
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build:
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context: .
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dockerfile: Dockerfile
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||||||
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env_file:
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||||||
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- server.env
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ports:
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- 8050:8050
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||||||
Generated
+333
-4
@@ -1,5 +1,16 @@
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|||||||
# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
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# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
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||||||
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||||||
|
[[package]]
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||||||
|
name = "annotated-types"
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version = "0.6.0"
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||||||
|
description = "Reusable constraint types to use with typing.Annotated"
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||||||
|
optional = false
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||||||
|
python-versions = ">=3.8"
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||||||
|
files = [
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||||||
|
{file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"},
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||||||
|
{file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
|
||||||
|
]
|
||||||
|
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||||||
[[package]]
|
[[package]]
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||||||
name = "blinker"
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name = "blinker"
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||||||
version = "1.7.0"
|
version = "1.7.0"
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||||||
@@ -257,6 +268,26 @@ requests = ">=2.28.1,<3.0.0"
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|||||||
[package.extras]
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[package.extras]
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||||||
async = ["httpx (>=0.23.0,<0.24.0)"]
|
async = ["httpx (>=0.23.0,<0.24.0)"]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "dnspython"
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||||||
|
version = "2.6.1"
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||||||
|
description = "DNS toolkit"
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||||||
|
optional = false
|
||||||
|
python-versions = ">=3.8"
|
||||||
|
files = [
|
||||||
|
{file = "dnspython-2.6.1-py3-none-any.whl", hash = "sha256:5ef3b9680161f6fa89daf8ad451b5f1a33b18ae8a1c6778cdf4b43f08c0a6e50"},
|
||||||
|
{file = "dnspython-2.6.1.tar.gz", hash = "sha256:e8f0f9c23a7b7cb99ded64e6c3a6f3e701d78f50c55e002b839dea7225cff7cc"},
|
||||||
|
]
|
||||||
|
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||||||
|
[package.extras]
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||||||
|
dev = ["black (>=23.1.0)", "coverage (>=7.0)", "flake8 (>=7)", "mypy (>=1.8)", "pylint (>=3)", "pytest (>=7.4)", "pytest-cov (>=4.1.0)", "sphinx (>=7.2.0)", "twine (>=4.0.0)", "wheel (>=0.42.0)"]
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||||||
|
dnssec = ["cryptography (>=41)"]
|
||||||
|
doh = ["h2 (>=4.1.0)", "httpcore (>=1.0.0)", "httpx (>=0.26.0)"]
|
||||||
|
doq = ["aioquic (>=0.9.25)"]
|
||||||
|
idna = ["idna (>=3.6)"]
|
||||||
|
trio = ["trio (>=0.23)"]
|
||||||
|
wmi = ["wmi (>=1.5.1)"]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "flask"
|
name = "flask"
|
||||||
version = "3.0.2"
|
version = "3.0.2"
|
||||||
@@ -448,6 +479,91 @@ files = [
|
|||||||
{file = "packaging-23.2.tar.gz", hash = "sha256:048fb0e9405036518eaaf48a55953c750c11e1a1b68e0dd1a9d62ed0c092cfc5"},
|
{file = "packaging-23.2.tar.gz", hash = "sha256:048fb0e9405036518eaaf48a55953c750c11e1a1b68e0dd1a9d62ed0c092cfc5"},
|
||||||
]
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "pillow"
|
||||||
|
version = "10.2.0"
|
||||||
|
description = "Python Imaging Library (Fork)"
|
||||||
|
optional = false
|
||||||
|
python-versions = ">=3.8"
|
||||||
|
files = [
|
||||||
|
{file = "pillow-10.2.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:7823bdd049099efa16e4246bdf15e5a13dbb18a51b68fa06d6c1d4d8b99a796e"},
|
||||||
|
{file = "pillow-10.2.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:83b2021f2ade7d1ed556bc50a399127d7fb245e725aa0113ebd05cfe88aaf588"},
|
||||||
|
{file = "pillow-10.2.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6fad5ff2f13d69b7e74ce5b4ecd12cc0ec530fcee76356cac6742785ff71c452"},
|
||||||
|
{file = "pillow-10.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:da2b52b37dad6d9ec64e653637a096905b258d2fc2b984c41ae7d08b938a67e4"},
|
||||||
|
{file = "pillow-10.2.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:47c0995fc4e7f79b5cfcab1fc437ff2890b770440f7696a3ba065ee0fd496563"},
|
||||||
|
{file = "pillow-10.2.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:322bdf3c9b556e9ffb18f93462e5f749d3444ce081290352c6070d014c93feb2"},
|
||||||
|
{file = "pillow-10.2.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:51f1a1bffc50e2e9492e87d8e09a17c5eea8409cda8d3f277eb6edc82813c17c"},
|
||||||
|
{file = "pillow-10.2.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:69ffdd6120a4737710a9eee73e1d2e37db89b620f702754b8f6e62594471dee0"},
|
||||||
|
{file = "pillow-10.2.0-cp310-cp310-win32.whl", hash = "sha256:c6dafac9e0f2b3c78df97e79af707cdc5ef8e88208d686a4847bab8266870023"},
|
||||||
|
{file = "pillow-10.2.0-cp310-cp310-win_amd64.whl", hash = "sha256:aebb6044806f2e16ecc07b2a2637ee1ef67a11840a66752751714a0d924adf72"},
|
||||||
|
{file = "pillow-10.2.0-cp310-cp310-win_arm64.whl", hash = "sha256:7049e301399273a0136ff39b84c3678e314f2158f50f517bc50285fb5ec847ad"},
|
||||||
|
{file = "pillow-10.2.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:35bb52c37f256f662abdfa49d2dfa6ce5d93281d323a9af377a120e89a9eafb5"},
|
||||||
|
{file = "pillow-10.2.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:9c23f307202661071d94b5e384e1e1dc7dfb972a28a2310e4ee16103e66ddb67"},
|
||||||
|
{file = "pillow-10.2.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:773efe0603db30c281521a7c0214cad7836c03b8ccff897beae9b47c0b657d61"},
|
||||||
|
{file = "pillow-10.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:11fa2e5984b949b0dd6d7a94d967743d87c577ff0b83392f17cb3990d0d2fd6e"},
|
||||||
|
{file = "pillow-10.2.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:716d30ed977be8b37d3ef185fecb9e5a1d62d110dfbdcd1e2a122ab46fddb03f"},
|
||||||
|
{file = "pillow-10.2.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:a086c2af425c5f62a65e12fbf385f7c9fcb8f107d0849dba5839461a129cf311"},
|
||||||
|
{file = "pillow-10.2.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:c8de2789052ed501dd829e9cae8d3dcce7acb4777ea4a479c14521c942d395b1"},
|
||||||
|
{file = "pillow-10.2.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:609448742444d9290fd687940ac0b57fb35e6fd92bdb65386e08e99af60bf757"},
|
||||||
|
{file = "pillow-10.2.0-cp311-cp311-win32.whl", hash = "sha256:823ef7a27cf86df6597fa0671066c1b596f69eba53efa3d1e1cb8b30f3533068"},
|
||||||
|
{file = "pillow-10.2.0-cp311-cp311-win_amd64.whl", hash = "sha256:1da3b2703afd040cf65ec97efea81cfba59cdbed9c11d8efc5ab09df9509fc56"},
|
||||||
|
{file = "pillow-10.2.0-cp311-cp311-win_arm64.whl", hash = "sha256:edca80cbfb2b68d7b56930b84a0e45ae1694aeba0541f798e908a49d66b837f1"},
|
||||||
|
{file = "pillow-10.2.0-cp312-cp312-macosx_10_10_x86_64.whl", hash = "sha256:1b5e1b74d1bd1b78bc3477528919414874748dd363e6272efd5abf7654e68bef"},
|
||||||
|
{file = "pillow-10.2.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:0eae2073305f451d8ecacb5474997c08569fb4eb4ac231ffa4ad7d342fdc25ac"},
|
||||||
|
{file = "pillow-10.2.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b7c2286c23cd350b80d2fc9d424fc797575fb16f854b831d16fd47ceec078f2c"},
|
||||||
|
{file = "pillow-10.2.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1e23412b5c41e58cec602f1135c57dfcf15482013ce6e5f093a86db69646a5aa"},
|
||||||
|
{file = "pillow-10.2.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:52a50aa3fb3acb9cf7213573ef55d31d6eca37f5709c69e6858fe3bc04a5c2a2"},
|
||||||
|
{file = "pillow-10.2.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:127cee571038f252a552760076407f9cff79761c3d436a12af6000cd182a9d04"},
|
||||||
|
{file = "pillow-10.2.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:8d12251f02d69d8310b046e82572ed486685c38f02176bd08baf216746eb947f"},
|
||||||
|
{file = "pillow-10.2.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:54f1852cd531aa981bc0965b7d609f5f6cc8ce8c41b1139f6ed6b3c54ab82bfb"},
|
||||||
|
{file = "pillow-10.2.0-cp312-cp312-win32.whl", hash = "sha256:257d8788df5ca62c980314053197f4d46eefedf4e6175bc9412f14412ec4ea2f"},
|
||||||
|
{file = "pillow-10.2.0-cp312-cp312-win_amd64.whl", hash = "sha256:154e939c5f0053a383de4fd3d3da48d9427a7e985f58af8e94d0b3c9fcfcf4f9"},
|
||||||
|
{file = "pillow-10.2.0-cp312-cp312-win_arm64.whl", hash = "sha256:f379abd2f1e3dddb2b61bc67977a6b5a0a3f7485538bcc6f39ec76163891ee48"},
|
||||||
|
{file = "pillow-10.2.0-cp38-cp38-macosx_10_10_x86_64.whl", hash = "sha256:8373c6c251f7ef8bda6675dd6d2b3a0fcc31edf1201266b5cf608b62a37407f9"},
|
||||||
|
{file = "pillow-10.2.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:870ea1ada0899fd0b79643990809323b389d4d1d46c192f97342eeb6ee0b8483"},
|
||||||
|
{file = "pillow-10.2.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b4b6b1e20608493548b1f32bce8cca185bf0480983890403d3b8753e44077129"},
|
||||||
|
{file = "pillow-10.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3031709084b6e7852d00479fd1d310b07d0ba82765f973b543c8af5061cf990e"},
|
||||||
|
{file = "pillow-10.2.0-cp38-cp38-manylinux_2_28_aarch64.whl", hash = "sha256:3ff074fc97dd4e80543a3e91f69d58889baf2002b6be64347ea8cf5533188213"},
|
||||||
|
{file = "pillow-10.2.0-cp38-cp38-manylinux_2_28_x86_64.whl", hash = "sha256:cb4c38abeef13c61d6916f264d4845fab99d7b711be96c326b84df9e3e0ff62d"},
|
||||||
|
{file = "pillow-10.2.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:b1b3020d90c2d8e1dae29cf3ce54f8094f7938460fb5ce8bc5c01450b01fbaf6"},
|
||||||
|
{file = "pillow-10.2.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:170aeb00224ab3dc54230c797f8404507240dd868cf52066f66a41b33169bdbe"},
|
||||||
|
{file = "pillow-10.2.0-cp38-cp38-win32.whl", hash = "sha256:c4225f5220f46b2fde568c74fca27ae9771536c2e29d7c04f4fb62c83275ac4e"},
|
||||||
|
{file = "pillow-10.2.0-cp38-cp38-win_amd64.whl", hash = "sha256:0689b5a8c5288bc0504d9fcee48f61a6a586b9b98514d7d29b840143d6734f39"},
|
||||||
|
{file = "pillow-10.2.0-cp39-cp39-macosx_10_10_x86_64.whl", hash = "sha256:b792a349405fbc0163190fde0dc7b3fef3c9268292586cf5645598b48e63dc67"},
|
||||||
|
{file = "pillow-10.2.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c570f24be1e468e3f0ce7ef56a89a60f0e05b30a3669a459e419c6eac2c35364"},
|
||||||
|
{file = "pillow-10.2.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d8ecd059fdaf60c1963c58ceb8997b32e9dc1b911f5da5307aab614f1ce5c2fb"},
|
||||||
|
{file = "pillow-10.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c365fd1703040de1ec284b176d6af5abe21b427cb3a5ff68e0759e1e313a5e7e"},
|
||||||
|
{file = "pillow-10.2.0-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:70c61d4c475835a19b3a5aa42492409878bbca7438554a1f89d20d58a7c75c01"},
|
||||||
|
{file = "pillow-10.2.0-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:b6f491cdf80ae540738859d9766783e3b3c8e5bd37f5dfa0b76abdecc5081f13"},
|
||||||
|
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||||||
|
{file = "pillow-10.2.0.tar.gz", hash = "sha256:e87f0b2c78157e12d7686b27d63c070fd65d994e8ddae6f328e0dcf4a0cd007e"},
|
||||||
|
]
|
||||||
|
|
||||||
|
[package.extras]
|
||||||
|
docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
|
||||||
|
fpx = ["olefile"]
|
||||||
|
mic = ["olefile"]
|
||||||
|
tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
|
||||||
|
typing = ["typing-extensions"]
|
||||||
|
xmp = ["defusedxml"]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "plotly"
|
name = "plotly"
|
||||||
version = "5.19.0"
|
version = "5.19.0"
|
||||||
@@ -463,6 +579,219 @@ files = [
|
|||||||
packaging = "*"
|
packaging = "*"
|
||||||
tenacity = ">=6.2.0"
|
tenacity = ">=6.2.0"
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "pydantic"
|
||||||
|
version = "2.6.1"
|
||||||
|
description = "Data validation using Python type hints"
|
||||||
|
optional = false
|
||||||
|
python-versions = ">=3.8"
|
||||||
|
files = [
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|
{file = "pydantic-2.6.1-py3-none-any.whl", hash = "sha256:0b6a909df3192245cb736509a92ff69e4fef76116feffec68e93a567347bae6f"},
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|
{file = "pydantic-2.6.1.tar.gz", hash = "sha256:4fd5c182a2488dc63e6d32737ff19937888001e2a6d86e94b3f233104a5d1fa9"},
|
||||||
|
]
|
||||||
|
|
||||||
|
[package.dependencies]
|
||||||
|
annotated-types = ">=0.4.0"
|
||||||
|
pydantic-core = "2.16.2"
|
||||||
|
typing-extensions = ">=4.6.1"
|
||||||
|
|
||||||
|
[package.extras]
|
||||||
|
email = ["email-validator (>=2.0.0)"]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "pydantic-core"
|
||||||
|
version = "2.16.2"
|
||||||
|
description = ""
|
||||||
|
optional = false
|
||||||
|
python-versions = ">=3.8"
|
||||||
|
files = [
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|
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||||||
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]
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||||||
|
|
||||||
|
[package.dependencies]
|
||||||
|
typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "pymongo"
|
||||||
|
version = "4.6.1"
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||||||
|
description = "Python driver for MongoDB <http://www.mongodb.org>"
|
||||||
|
optional = false
|
||||||
|
python-versions = ">=3.7"
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files = [
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|
||||||
|
{file = "pymongo-4.6.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3f0e6a6c807fa887a0c51cc24fe7ea51bb9e496fe88f00d7930063372c3664c3"},
|
||||||
|
{file = "pymongo-4.6.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:dd1fa413f8b9ba30140de198e4f408ffbba6396864c7554e0867aa7363eb58b2"},
|
||||||
|
{file = "pymongo-4.6.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8d219b4508f71d762368caec1fc180960569766049bbc4d38174f05e8ef2fe5b"},
|
||||||
|
{file = "pymongo-4.6.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:27b81ecf18031998ad7db53b960d1347f8f29e8b7cb5ea7b4394726468e4295e"},
|
||||||
|
{file = "pymongo-4.6.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:56816e43c92c2fa8c11dc2a686f0ca248bea7902f4a067fa6cbc77853b0f041e"},
|
||||||
|
{file = "pymongo-4.6.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:ef801027629c5b511cf2ba13b9be29bfee36ae834b2d95d9877818479cdc99ea"},
|
||||||
|
{file = "pymongo-4.6.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:d4c2be9760b112b1caf649b4977b81b69893d75aa86caf4f0f398447be871f3c"},
|
||||||
|
{file = "pymongo-4.6.1-cp38-cp38-win32.whl", hash = "sha256:39d77d8bbb392fa443831e6d4ae534237b1f4eee6aa186f0cdb4e334ba89536e"},
|
||||||
|
{file = "pymongo-4.6.1-cp38-cp38-win_amd64.whl", hash = "sha256:4497d49d785482cc1a44a0ddf8830b036a468c088e72a05217f5b60a9e025012"},
|
||||||
|
{file = "pymongo-4.6.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:69247f7a2835fc0984bbf0892e6022e9a36aec70e187fcfe6cae6a373eb8c4de"},
|
||||||
|
{file = "pymongo-4.6.1-cp39-cp39-manylinux1_i686.whl", hash = "sha256:7bb0e9049e81def6829d09558ad12d16d0454c26cabe6efc3658e544460688d9"},
|
||||||
|
{file = "pymongo-4.6.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:6a1810c2cbde714decf40f811d1edc0dae45506eb37298fd9d4247b8801509fe"},
|
||||||
|
{file = "pymongo-4.6.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:e2aced6fb2f5261b47d267cb40060b73b6527e64afe54f6497844c9affed5fd0"},
|
||||||
|
{file = "pymongo-4.6.1-cp39-cp39-manylinux2014_i686.whl", hash = "sha256:d0355cff58a4ed6d5e5f6b9c3693f52de0784aa0c17119394e2a8e376ce489d4"},
|
||||||
|
{file = "pymongo-4.6.1-cp39-cp39-manylinux2014_ppc64le.whl", hash = "sha256:3c74f4725485f0a7a3862cfd374cc1b740cebe4c133e0c1425984bcdcce0f4bb"},
|
||||||
|
{file = "pymongo-4.6.1-cp39-cp39-manylinux2014_s390x.whl", hash = "sha256:9c79d597fb3a7c93d7c26924db7497eba06d58f88f58e586aa69b2ad89fee0f8"},
|
||||||
|
{file = "pymongo-4.6.1-cp39-cp39-manylinux2014_x86_64.whl", hash = "sha256:8ec75f35f62571a43e31e7bd11749d974c1b5cd5ea4a8388725d579263c0fdf6"},
|
||||||
|
{file = "pymongo-4.6.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a5e641f931c5cd95b376fd3c59db52770e17bec2bf86ef16cc83b3906c054845"},
|
||||||
|
{file = "pymongo-4.6.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9aafd036f6f2e5ad109aec92f8dbfcbe76cff16bad683eb6dd18013739c0b3ae"},
|
||||||
|
{file = "pymongo-4.6.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1f2b856518bfcfa316c8dae3d7b412aecacf2e8ba30b149f5eb3b63128d703b9"},
|
||||||
|
{file = "pymongo-4.6.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5ec31adc2e988fd7db3ab509954791bbc5a452a03c85e45b804b4bfc31fa221d"},
|
||||||
|
{file = "pymongo-4.6.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9167e735379ec43d8eafa3fd675bfbb12e2c0464f98960586e9447d2cf2c7a83"},
|
||||||
|
{file = "pymongo-4.6.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:1461199b07903fc1424709efafe379205bf5f738144b1a50a08b0396357b5abf"},
|
||||||
|
{file = "pymongo-4.6.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:3094c7d2f820eecabadae76bfec02669567bbdd1730eabce10a5764778564f7b"},
|
||||||
|
{file = "pymongo-4.6.1-cp39-cp39-win32.whl", hash = "sha256:c91ea3915425bd4111cb1b74511cdc56d1d16a683a48bf2a5a96b6a6c0f297f7"},
|
||||||
|
{file = "pymongo-4.6.1-cp39-cp39-win_amd64.whl", hash = "sha256:ef102a67ede70e1721fe27f75073b5314911dbb9bc27cde0a1c402a11531e7bd"},
|
||||||
|
{file = "pymongo-4.6.1.tar.gz", hash = "sha256:31dab1f3e1d0cdd57e8df01b645f52d43cc1b653ed3afd535d2891f4fc4f9712"},
|
||||||
|
]
|
||||||
|
|
||||||
|
[package.dependencies]
|
||||||
|
dnspython = ">=1.16.0,<3.0.0"
|
||||||
|
|
||||||
|
[package.extras]
|
||||||
|
aws = ["pymongo-auth-aws (<2.0.0)"]
|
||||||
|
encryption = ["certifi", "pymongo[aws]", "pymongocrypt (>=1.6.0,<2.0.0)"]
|
||||||
|
gssapi = ["pykerberos", "winkerberos (>=0.5.0)"]
|
||||||
|
ocsp = ["certifi", "cryptography (>=2.5)", "pyopenssl (>=17.2.0)", "requests (<3.0.0)", "service-identity (>=18.1.0)"]
|
||||||
|
snappy = ["python-snappy"]
|
||||||
|
test = ["pytest (>=7)"]
|
||||||
|
zstd = ["zstandard"]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "python-dotenv"
|
name = "python-dotenv"
|
||||||
version = "1.0.1"
|
version = "1.0.1"
|
||||||
@@ -583,13 +912,13 @@ files = [
|
|||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "urllib3"
|
name = "urllib3"
|
||||||
version = "2.2.0"
|
version = "2.2.1"
|
||||||
description = "HTTP library with thread-safe connection pooling, file post, and more."
|
description = "HTTP library with thread-safe connection pooling, file post, and more."
|
||||||
optional = false
|
optional = false
|
||||||
python-versions = ">=3.8"
|
python-versions = ">=3.8"
|
||||||
files = [
|
files = [
|
||||||
{file = "urllib3-2.2.0-py3-none-any.whl", hash = "sha256:ce3711610ddce217e6d113a2732fafad960a03fd0318c91faa79481e35c11224"},
|
{file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
|
||||||
{file = "urllib3-2.2.0.tar.gz", hash = "sha256:051d961ad0c62a94e50ecf1af379c3aba230c66c710493493560c0c223c49f20"},
|
{file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
|
||||||
]
|
]
|
||||||
|
|
||||||
[package.extras]
|
[package.extras]
|
||||||
@@ -633,4 +962,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
|
|||||||
[metadata]
|
[metadata]
|
||||||
lock-version = "2.0"
|
lock-version = "2.0"
|
||||||
python-versions = "^3.12"
|
python-versions = "^3.12"
|
||||||
content-hash = "b80bd2598b5e94ab048aff2a8615067d938b520c5e4e131547e4cbc1a8f664d5"
|
content-hash = "e4aacea5a98281d935411e0d96152d1d24680f6c1e5288e9a1be913a0536b78e"
|
||||||
|
|||||||
+7
-1
@@ -1,9 +1,12 @@
|
|||||||
[tool.poetry]
|
[tool.poetry]
|
||||||
name = "visual-critical-discourse-analysis"
|
name = "visual_critical_discourse_analysis"
|
||||||
version = "0.1.0"
|
version = "0.1.0"
|
||||||
description = ""
|
description = ""
|
||||||
authors = ["Brian Bjarke Jensen <[email protected]>"]
|
authors = ["Brian Bjarke Jensen <[email protected]>"]
|
||||||
readme = "README.md"
|
readme = "README.md"
|
||||||
|
packages = [
|
||||||
|
{ include = "src" },
|
||||||
|
]
|
||||||
|
|
||||||
[tool.poetry.dependencies]
|
[tool.poetry.dependencies]
|
||||||
python = "^3.12"
|
python = "^3.12"
|
||||||
@@ -13,6 +16,9 @@ python-dotenv = "^1.0.1"
|
|||||||
dash = "^2.15.0"
|
dash = "^2.15.0"
|
||||||
dash-bootstrap-components = "^1.5.0"
|
dash-bootstrap-components = "^1.5.0"
|
||||||
dash-mantine-components = "^0.12.1"
|
dash-mantine-components = "^0.12.1"
|
||||||
|
pydantic = "^2.6.1"
|
||||||
|
pillow = "^10.2.0"
|
||||||
|
pymongo = "^4.6.1"
|
||||||
|
|
||||||
|
|
||||||
[build-system]
|
[build-system]
|
||||||
|
|||||||
@@ -0,0 +1,5 @@
|
|||||||
|
from .classes import (
|
||||||
|
ModelOutputs,
|
||||||
|
VisualCommunication
|
||||||
|
)
|
||||||
|
from .database import connect
|
||||||
@@ -0,0 +1,75 @@
|
|||||||
|
from __future__ import annotations
|
||||||
|
from pydantic import BaseModel, field_validator, field_serializer
|
||||||
|
from PIL import Image
|
||||||
|
from io import BytesIO
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
from src.model_experiential import ExperientialModelOutput
|
||||||
|
from src.model_interpersonal import (
|
||||||
|
ContactModelOutput,
|
||||||
|
AngleModelOutput,
|
||||||
|
PointOfViewModelOutput,
|
||||||
|
DistanceModelOutput,
|
||||||
|
ModalityLightingModelOutput,
|
||||||
|
ModalityColorModelOutput,
|
||||||
|
ModalityDepthModelOutput
|
||||||
|
)
|
||||||
|
from src.model_textual import (
|
||||||
|
InformationValueModelOutput,
|
||||||
|
FramingModelOutput,
|
||||||
|
SalienceModelOutput
|
||||||
|
)
|
||||||
|
|
||||||
|
class ModelOutputs(BaseModel):
|
||||||
|
experiential: ExperientialModelOutput
|
||||||
|
contact: ContactModelOutput
|
||||||
|
angle: AngleModelOutput
|
||||||
|
point_of_view: PointOfViewModelOutput
|
||||||
|
distance: DistanceModelOutput
|
||||||
|
modality_lighting: ModalityLightingModelOutput
|
||||||
|
modality_color: ModalityColorModelOutput
|
||||||
|
modality_depth: ModalityDepthModelOutput
|
||||||
|
information_value: InformationValueModelOutput
|
||||||
|
framing: FramingModelOutput
|
||||||
|
salience: SalienceModelOutput
|
||||||
|
|
||||||
|
|
||||||
|
class VisualCommunication(BaseModel):
|
||||||
|
name: str
|
||||||
|
image: Image.Image | BytesIO | bytes
|
||||||
|
annotation: ModelOutputs | None = None
|
||||||
|
prediction: ModelOutputs | None = None
|
||||||
|
|
||||||
|
class Config:
|
||||||
|
arbitrary_types_allowed = True
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def classname(cls) -> str:
|
||||||
|
"""Return classname."""
|
||||||
|
return cls.__name__
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def from_file(cls, path: Path) -> VisualCommunication:
|
||||||
|
"""Instantiate from file."""
|
||||||
|
name = path.stem
|
||||||
|
image = Image.open(path)
|
||||||
|
image.load()
|
||||||
|
return VisualCommunication(name=name, image=image)
|
||||||
|
|
||||||
|
@field_serializer("image")
|
||||||
|
def serialize_image(image: Image.Image) -> bytes:
|
||||||
|
buffer = BytesIO()
|
||||||
|
image.save(buffer, format="JPEG")
|
||||||
|
return buffer.getvalue()
|
||||||
|
|
||||||
|
@field_validator("image", mode="before")
|
||||||
|
@classmethod
|
||||||
|
def convert_to_image(cls, image: Image.Image | BytesIO | bytes) -> Image.Image:
|
||||||
|
if isinstance(image, bytes):
|
||||||
|
image = BytesIO(image)
|
||||||
|
if isinstance(image, BytesIO):
|
||||||
|
image = Image.open(image)
|
||||||
|
return image
|
||||||
|
|
||||||
|
def __repr__(self) -> str:
|
||||||
|
return f"{self.classname()}(name='{self.name}')"
|
||||||
@@ -0,0 +1,21 @@
|
|||||||
|
from pymongo import MongoClient
|
||||||
|
from dotenv import load_dotenv
|
||||||
|
import os
|
||||||
|
|
||||||
|
def connect():
|
||||||
|
"""Connect to MongoDB."""
|
||||||
|
# load env vars
|
||||||
|
load_dotenv()
|
||||||
|
necessary_env_vars = [
|
||||||
|
"MONGO_HOST",
|
||||||
|
"MONGO_DB",
|
||||||
|
"MONGO_COLLECTION"
|
||||||
|
]
|
||||||
|
for env_var in necessary_env_vars:
|
||||||
|
assert env_var in os.environ, f"{env_var} not found"
|
||||||
|
# connect to database
|
||||||
|
client = MongoClient(os.getenv("MONGO_HOST"))
|
||||||
|
db = client[os.getenv("MONGO_DB")]
|
||||||
|
collection = db[os.getenv("MONGO_COLLECTION")]
|
||||||
|
collection.create_index("name", unique=True)
|
||||||
|
return collection, db, client
|
||||||
+2
-1
@@ -6,7 +6,7 @@ from pathlib import Path
|
|||||||
import logging
|
import logging
|
||||||
import os
|
import os
|
||||||
|
|
||||||
from web import app
|
from src.web import server
|
||||||
|
|
||||||
# load default values
|
# load default values
|
||||||
config = ConfigParser()
|
config = ConfigParser()
|
||||||
@@ -93,6 +93,7 @@ def initialise_app() -> None:
|
|||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
|
from src.web import app
|
||||||
# initialise_app()
|
# initialise_app()
|
||||||
app.run(debug=True)
|
app.run(debug=True)
|
||||||
logging.info("started app")
|
logging.info("started app")
|
||||||
@@ -1 +1,24 @@
|
|||||||
from .output import model_labels
|
from .classes import ExperientialModelOutput
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
# CLASS_NAME_LIST = Literal[
|
||||||
|
# "non transactional action",
|
||||||
|
# "non transactional reaction",
|
||||||
|
# "unidirectional transactional action",
|
||||||
|
# "unidirectional transactional reaction",
|
||||||
|
# "bidirectional transactional action",
|
||||||
|
# "bidirectional transactional reaction",
|
||||||
|
# "conversion",
|
||||||
|
# "speech process",
|
||||||
|
# "classification overt taxonomy",
|
||||||
|
# "analytical exhaustive",
|
||||||
|
# "analytical disarranged",
|
||||||
|
# "analytical temporal",
|
||||||
|
# "analytical distributed",
|
||||||
|
# "anaytical topological",
|
||||||
|
# "analytical exploded",
|
||||||
|
# "analytical inclusive",
|
||||||
|
# "symbolic suggestive",
|
||||||
|
# "symbolic attributive"
|
||||||
|
# ]
|
||||||
@@ -0,0 +1,68 @@
|
|||||||
|
from pydantic import BaseModel
|
||||||
|
from typing import List
|
||||||
|
import random
|
||||||
|
|
||||||
|
|
||||||
|
class ModelOutput(BaseModel):
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def classname(cls) -> str:
|
||||||
|
"""Return classname."""
|
||||||
|
return cls.__name__
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def list_fields(cls) -> List[str]:
|
||||||
|
"""List options that are stored as attributes."""
|
||||||
|
return list(cls.model_fields.keys())
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def from_random(cls):
|
||||||
|
"""Instantiate with random numbers."""
|
||||||
|
kwargs = {field: random.random() for field in cls.list_fields()}
|
||||||
|
return cls(**kwargs)
|
||||||
|
|
||||||
|
def __repr__(self) -> str:
|
||||||
|
model_dict = self.model_dump()
|
||||||
|
model_repr_str = f"{self.classname()}("
|
||||||
|
model_repr_str += ", ".join([f"{field}={value:.3f}" for field, value in model_dict.items()])
|
||||||
|
model_repr_str += ")"
|
||||||
|
return model_repr_str
|
||||||
|
|
||||||
|
def highest_score_field(self) -> str:
|
||||||
|
"""Return name of field with highest score."""
|
||||||
|
model_dict = self.model_dump()
|
||||||
|
return max(model_dict, key=lambda k: model_dict[k])
|
||||||
|
|
||||||
|
def highest_score_value(self) -> float:
|
||||||
|
"""Return value of field with highest score."""
|
||||||
|
model_dict = self.model_dump()
|
||||||
|
return max(model_dict.values())
|
||||||
|
|
||||||
|
|
||||||
|
class ExperientialModelOutput(ModelOutput):
|
||||||
|
non_transactional_action: float
|
||||||
|
non_transactional_reaction: float
|
||||||
|
unidirectional_transactional_action: float
|
||||||
|
unidirectional_transactional_reaction: float
|
||||||
|
bidirectional_transactional_action: float
|
||||||
|
bidirectional_transactional_reaction: float
|
||||||
|
conversion: float
|
||||||
|
speech_process: float
|
||||||
|
classification_overt_taxonomy: float
|
||||||
|
analytical_exhaustive: float
|
||||||
|
analytical_disarranged: float
|
||||||
|
analytical_temporal: float
|
||||||
|
analytical_distributed: float
|
||||||
|
analytical_topological: float
|
||||||
|
analytical_exploded: float
|
||||||
|
analytical_inclusive: float
|
||||||
|
symbolic_suggestive: float
|
||||||
|
symbolic_attributive: float
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
m = ExperientialModelOutput.from_random()
|
||||||
|
print(m)
|
||||||
|
print(repr(m))
|
||||||
|
print(m.highest_score_field())
|
||||||
|
print(m.highest_score_value())
|
||||||
@@ -1,5 +1,4 @@
|
|||||||
|
CLASS_NAMES = [
|
||||||
model_labels = [
|
|
||||||
"non transactional action",
|
"non transactional action",
|
||||||
"non transactional reaction",
|
"non transactional reaction",
|
||||||
"unidirectional transactional action",
|
"unidirectional transactional action",
|
||||||
|
|||||||
@@ -1 +1,9 @@
|
|||||||
from .output import model_labels
|
from .classes import (
|
||||||
|
ContactModelOutput,
|
||||||
|
AngleModelOutput,
|
||||||
|
PointOfViewModelOutput,
|
||||||
|
DistanceModelOutput,
|
||||||
|
ModalityLightingModelOutput,
|
||||||
|
ModalityColorModelOutput,
|
||||||
|
ModalityDepthModelOutput
|
||||||
|
)
|
||||||
@@ -0,0 +1,120 @@
|
|||||||
|
from pydantic import BaseModel
|
||||||
|
from typing import List
|
||||||
|
import random
|
||||||
|
|
||||||
|
|
||||||
|
class ModelOutput(BaseModel):
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def classname(cls) -> str:
|
||||||
|
"""Return classname."""
|
||||||
|
return cls.__name__
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def list_fields(cls) -> List[str]:
|
||||||
|
"""List options that are stored as attributes."""
|
||||||
|
return list(cls.model_fields.keys())
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def from_random(cls):
|
||||||
|
"""Instantiate with random numbers."""
|
||||||
|
kwargs = {field: random.random() for field in cls.list_fields()}
|
||||||
|
return cls(**kwargs)
|
||||||
|
|
||||||
|
def __repr__(self) -> str:
|
||||||
|
model_dict = self.model_dump()
|
||||||
|
model_repr_str = f"{self.classname()}("
|
||||||
|
model_repr_str += ", ".join([f"{field}={value:.3f}" for field, value in model_dict.items()])
|
||||||
|
model_repr_str += ")"
|
||||||
|
return model_repr_str
|
||||||
|
|
||||||
|
def highest_score_field(self) -> str:
|
||||||
|
"""Return name of field with highest score."""
|
||||||
|
model_dict = self.model_dump()
|
||||||
|
return max(model_dict, key=lambda k: model_dict[k])
|
||||||
|
|
||||||
|
def highest_score_value(self) -> float:
|
||||||
|
"""Return value of field with highest score."""
|
||||||
|
model_dict = self.model_dump()
|
||||||
|
return max(model_dict.values())
|
||||||
|
|
||||||
|
|
||||||
|
class ContactModelOutput(ModelOutput):
|
||||||
|
offer: float
|
||||||
|
demand: float
|
||||||
|
|
||||||
|
|
||||||
|
class AngleModelOutput(ModelOutput):
|
||||||
|
high: float
|
||||||
|
eye_level: float
|
||||||
|
low: float
|
||||||
|
|
||||||
|
|
||||||
|
class PointOfViewModelOutput(ModelOutput):
|
||||||
|
frontal: float
|
||||||
|
oblique: float
|
||||||
|
|
||||||
|
|
||||||
|
class DistanceModelOutput(ModelOutput):
|
||||||
|
long: float
|
||||||
|
medium: float
|
||||||
|
close: float
|
||||||
|
|
||||||
|
|
||||||
|
class ModalityLightingModelOutput(ModelOutput):
|
||||||
|
high: float
|
||||||
|
medium: float
|
||||||
|
low: float
|
||||||
|
|
||||||
|
|
||||||
|
class ModalityColorModelOutput(ModelOutput):
|
||||||
|
high: float
|
||||||
|
medium: float
|
||||||
|
low: float
|
||||||
|
|
||||||
|
|
||||||
|
class ModalityDepthModelOutput(ModelOutput):
|
||||||
|
high: float
|
||||||
|
medium: float
|
||||||
|
low: float
|
||||||
|
|
||||||
|
|
||||||
|
# class InterpersonalModelOutput(BaseModel):
|
||||||
|
# contact: ContactModelOutput
|
||||||
|
# angle: AngleModelOutput
|
||||||
|
# point_of_view: PointOfViewModelOutput
|
||||||
|
# distance: DistanceModelOutput
|
||||||
|
# modality_lighting: ModalityLightingModelOutput
|
||||||
|
# modality_color: ModalityColorModelOutput
|
||||||
|
# modality_depth: ModalityDepthModelOutput
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
m = ContactModelOutput.from_random()
|
||||||
|
print(repr(m))
|
||||||
|
print(m.highest_score_field())
|
||||||
|
print(m.highest_score_value())
|
||||||
|
m = AngleModelOutput.from_random()
|
||||||
|
print(repr(m))
|
||||||
|
print(m.highest_score_field())
|
||||||
|
print(m.highest_score_value())
|
||||||
|
m = PointOfViewModelOutput.from_random()
|
||||||
|
print(repr(m))
|
||||||
|
print(m.highest_score_field())
|
||||||
|
print(m.highest_score_value())
|
||||||
|
m = DistanceModelOutput.from_random()
|
||||||
|
print(repr(m))
|
||||||
|
print(m.highest_score_field())
|
||||||
|
print(m.highest_score_value())
|
||||||
|
m = ModalityLightingModelOutput.from_random()
|
||||||
|
print(repr(m))
|
||||||
|
print(m.highest_score_field())
|
||||||
|
print(m.highest_score_value())
|
||||||
|
m = ModalityColorModelOutput.from_random()
|
||||||
|
print(repr(m))
|
||||||
|
print(m.highest_score_field())
|
||||||
|
print(m.highest_score_value())
|
||||||
|
m = ModalityDepthModelOutput.from_random()
|
||||||
|
print(repr(m))
|
||||||
|
print(m.highest_score_field())
|
||||||
|
print(m.highest_score_value())
|
||||||
@@ -1,8 +1,8 @@
|
|||||||
|
|
||||||
model_labels = {
|
model_labels = {
|
||||||
"contact": [
|
"contact": [
|
||||||
"contact offer",
|
"offer",
|
||||||
"contact demand"
|
"demand"
|
||||||
],
|
],
|
||||||
"angle": [
|
"angle": [
|
||||||
"high",
|
"high",
|
||||||
|
|||||||
@@ -1 +1,5 @@
|
|||||||
from .output import model_labels
|
from .classes import (
|
||||||
|
InformationValueModelOutput,
|
||||||
|
FramingModelOutput,
|
||||||
|
SalienceModelOutput
|
||||||
|
)
|
||||||
@@ -0,0 +1,75 @@
|
|||||||
|
from pydantic import BaseModel
|
||||||
|
from typing import List
|
||||||
|
import random
|
||||||
|
|
||||||
|
|
||||||
|
class ModelOutput(BaseModel):
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def classname(cls) -> str:
|
||||||
|
"""Return classname."""
|
||||||
|
return cls.__name__
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def list_fields(cls) -> List[str]:
|
||||||
|
"""List options that are stored as attributes."""
|
||||||
|
return list(cls.model_fields.keys())
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def from_random(cls):
|
||||||
|
"""Instantiate with random numbers."""
|
||||||
|
kwargs = {field: random.random() for field in cls.list_fields()}
|
||||||
|
return cls(**kwargs)
|
||||||
|
|
||||||
|
def __repr__(self) -> str:
|
||||||
|
model_dict = self.model_dump()
|
||||||
|
model_repr_str = f"{self.classname()}("
|
||||||
|
model_repr_str += ", ".join([f"{field}={value:.3f}" for field, value in model_dict.items()])
|
||||||
|
model_repr_str += ")"
|
||||||
|
return model_repr_str
|
||||||
|
|
||||||
|
def highest_score_field(self) -> str:
|
||||||
|
"""Return name of field with highest score."""
|
||||||
|
model_dict = self.model_dump()
|
||||||
|
return max(model_dict, key=lambda k: model_dict[k])
|
||||||
|
|
||||||
|
def highest_score_value(self) -> float:
|
||||||
|
"""Return value of field with highest score."""
|
||||||
|
model_dict = self.model_dump()
|
||||||
|
return max(model_dict.values())
|
||||||
|
|
||||||
|
|
||||||
|
class InformationValueModelOutput(ModelOutput):
|
||||||
|
given_new: float
|
||||||
|
ideal_real: float
|
||||||
|
central_marginal: float
|
||||||
|
|
||||||
|
|
||||||
|
class FramingModelOutput(ModelOutput):
|
||||||
|
frame_lines: float
|
||||||
|
empty_space: float
|
||||||
|
colour_contrast: float
|
||||||
|
form_contrast: float
|
||||||
|
|
||||||
|
|
||||||
|
class SalienceModelOutput(ModelOutput):
|
||||||
|
size: float
|
||||||
|
colour: float
|
||||||
|
tone: float
|
||||||
|
form: float
|
||||||
|
positioning: float
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
m = InformationValueModelOutput.from_random()
|
||||||
|
print(repr(m))
|
||||||
|
print(m.highest_score_field())
|
||||||
|
print(m.highest_score_value())
|
||||||
|
m = FramingModelOutput.from_random()
|
||||||
|
print(repr(m))
|
||||||
|
print(m.highest_score_field())
|
||||||
|
print(m.highest_score_value())
|
||||||
|
m = SalienceModelOutput.from_random()
|
||||||
|
print(repr(m))
|
||||||
|
print(m.highest_score_field())
|
||||||
|
print(m.highest_score_value())
|
||||||
+4
-9
@@ -4,9 +4,6 @@ import dash_mantine_components as dmc
|
|||||||
|
|
||||||
from .header import generate_header
|
from .header import generate_header
|
||||||
from .body import generate_body
|
from .body import generate_body
|
||||||
from model_experiential import model_labels as experiential_labels
|
|
||||||
from model_interpersonal import model_labels as interpersonal_labels
|
|
||||||
from model_textual import model_labels as textual_labels
|
|
||||||
|
|
||||||
|
|
||||||
app = Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
|
app = Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
|
||||||
@@ -20,14 +17,12 @@ app.layout = dmc.MantineProvider(
|
|||||||
},
|
},
|
||||||
},
|
},
|
||||||
children=[
|
children=[
|
||||||
dmc.Container([
|
dmc.Container(
|
||||||
|
[
|
||||||
generate_header(),
|
generate_header(),
|
||||||
generate_body(
|
generate_body(),
|
||||||
experiential_labels,
|
], fluid=True
|
||||||
interpersonal_labels,
|
|
||||||
textual_labels
|
|
||||||
),
|
),
|
||||||
]),
|
|
||||||
],
|
],
|
||||||
)
|
)
|
||||||
|
|
||||||
+117
-45
@@ -1,71 +1,143 @@
|
|||||||
import dash_mantine_components as dmc
|
import dash_mantine_components as dmc
|
||||||
from dash import dcc, html
|
from dash import dcc, html
|
||||||
|
from typing import List
|
||||||
|
|
||||||
def generate_body(
|
from src.model_experiential import ExperientialModelOutput
|
||||||
experiential_labels,
|
from src.model_interpersonal import (
|
||||||
interpersonal_labels,
|
ContactModelOutput,
|
||||||
textual_labels
|
AngleModelOutput,
|
||||||
):
|
PointOfViewModelOutput,
|
||||||
|
DistanceModelOutput,
|
||||||
|
ModalityLightingModelOutput,
|
||||||
|
ModalityColorModelOutput,
|
||||||
|
ModalityDepthModelOutput
|
||||||
|
)
|
||||||
|
from src.model_textual import (
|
||||||
|
InformationValueModelOutput,
|
||||||
|
FramingModelOutput,
|
||||||
|
SalienceModelOutput
|
||||||
|
)
|
||||||
|
|
||||||
|
def generate_option_labels(model) -> List[str]:
|
||||||
|
"""Generate presentable list of attributes from an OutputModel."""
|
||||||
|
labels = [
|
||||||
|
label.replace('_', ' ').title()
|
||||||
|
for label in model.list_fields()
|
||||||
|
]
|
||||||
|
return labels
|
||||||
|
|
||||||
|
def generate_experiential_options_map():
|
||||||
|
"""Generate map of titles and options for experiential labels."""
|
||||||
|
options_map = {}
|
||||||
|
# add experiential labels
|
||||||
|
options_map["experiential".title()] = generate_option_labels(ExperientialModelOutput)
|
||||||
|
return options_map
|
||||||
|
|
||||||
|
def generate_interpersonal_options_map():
|
||||||
|
"""Generate map of titles and options for interpersonal labels."""
|
||||||
|
options_map = {}
|
||||||
|
# add interpersonal labels
|
||||||
|
options_map["contact".title()] = generate_option_labels(ContactModelOutput)
|
||||||
|
options_map["angle".title()] = generate_option_labels(AngleModelOutput)
|
||||||
|
options_map["point of view".title()] = generate_option_labels(PointOfViewModelOutput)
|
||||||
|
options_map["distance".title()] = generate_option_labels(DistanceModelOutput)
|
||||||
|
options_map["modality lighting".title()] = generate_option_labels(ModalityLightingModelOutput)
|
||||||
|
options_map["modality color".title()] = generate_option_labels(ModalityColorModelOutput)
|
||||||
|
options_map["modality depth".title()] = generate_option_labels(ModalityDepthModelOutput)
|
||||||
|
return options_map
|
||||||
|
|
||||||
|
def generate_textual_options_map():
|
||||||
|
"""Generate map of titles and options for textual labels."""
|
||||||
|
options_map = {}
|
||||||
|
# add textual labels
|
||||||
|
options_map["information value".title()] = generate_option_labels(InformationValueModelOutput)
|
||||||
|
options_map["framing".title()] = generate_option_labels(FramingModelOutput)
|
||||||
|
options_map["salience".title()] = generate_option_labels(SalienceModelOutput)
|
||||||
|
return options_map
|
||||||
|
|
||||||
|
def generate_body():
|
||||||
image_container = dmc.Image(
|
image_container = dmc.Image(
|
||||||
width=400,
|
width=600,
|
||||||
height=400,
|
height=600,
|
||||||
withPlaceholder=True,
|
withPlaceholder=True,
|
||||||
placeholder=[dmc.Loader(color="gray", size="md")],
|
placeholder=[dmc.Loader(color="gray", size="md")],
|
||||||
)
|
)
|
||||||
|
# prepare experiential container
|
||||||
experiential_labels_container = dmc.Container(
|
experiential_map = generate_experiential_options_map()
|
||||||
|
experiential_container = dmc.Col(
|
||||||
children=[
|
children=[
|
||||||
html.H4("experiential labels".title()),
|
dmc.Container([
|
||||||
dcc.RadioItems(options=list(experiential_labels)),
|
html.H4(list(experiential_map.keys())[0]),
|
||||||
]
|
html.B("visual syntax".title()),
|
||||||
|
dcc.RadioItems(options=list(experiential_map.values())[0]),
|
||||||
|
])
|
||||||
|
], span=4
|
||||||
)
|
)
|
||||||
|
# prepare interpersonal container
|
||||||
interpersonal_labels_container = dmc.Container(
|
interpersonal_map = generate_interpersonal_options_map()
|
||||||
children=[]
|
interpersonal_container = dmc.Col(
|
||||||
)
|
|
||||||
for category, options in interpersonal_labels.items():
|
|
||||||
interpersonal_labels_container.children.append(html.H4(category.title()))
|
|
||||||
interpersonal_labels_container.children.append(dcc.RadioItems(options))
|
|
||||||
|
|
||||||
textual_labels_container = dmc.Container(
|
|
||||||
children=[]
|
|
||||||
)
|
|
||||||
for category, options in textual_labels.items():
|
|
||||||
textual_labels_container.children.append(html.H4(category.title()))
|
|
||||||
textual_labels_container.children.append(dcc.RadioItems(options))
|
|
||||||
|
|
||||||
label_container = dmc.Container(
|
|
||||||
children=[
|
children=[
|
||||||
experiential_labels_container,
|
html.H4("interpersonal".title()),
|
||||||
dmc.Divider(),
|
], span=4
|
||||||
interpersonal_labels_container,
|
|
||||||
dmc.Divider(),
|
|
||||||
textual_labels_container,
|
|
||||||
dmc.Divider(),
|
|
||||||
html.Button(
|
|
||||||
"confirm",
|
|
||||||
id="submit-button"
|
|
||||||
)
|
)
|
||||||
]
|
for title, options in interpersonal_map.items():
|
||||||
|
interpersonal_container.children.append(
|
||||||
|
dmc.Container([
|
||||||
|
html.B(title),
|
||||||
|
dcc.RadioItems(options)
|
||||||
|
])
|
||||||
)
|
)
|
||||||
|
# prepare textual container
|
||||||
|
textual_map = generate_textual_options_map()
|
||||||
|
textual_container = dmc.Col(
|
||||||
|
children=[
|
||||||
|
html.H4("textual".title()),
|
||||||
|
], span=4
|
||||||
|
)
|
||||||
|
for title, options in textual_map.items():
|
||||||
|
textual_container.children.append(
|
||||||
|
dmc.Container([
|
||||||
|
html.B(title),
|
||||||
|
dcc.RadioItems(options)
|
||||||
|
])
|
||||||
|
)
|
||||||
|
# prepare labels container
|
||||||
|
label_container = dmc.Grid(
|
||||||
|
children=[
|
||||||
|
experiential_container,
|
||||||
|
interpersonal_container,
|
||||||
|
textual_container,
|
||||||
|
],
|
||||||
|
)
|
||||||
|
# build the full body container
|
||||||
body_container = dmc.Container(
|
body_container = dmc.Container(
|
||||||
dmc.Grid(
|
dmc.Grid(
|
||||||
children=[
|
children=[
|
||||||
dmc.Col(
|
dmc.Col(
|
||||||
|
dmc.Center(
|
||||||
image_container,
|
image_container,
|
||||||
span=5,
|
|
||||||
),
|
),
|
||||||
dmc.Col(
|
span=5,
|
||||||
dmc.Divider(orientation="vertical"),
|
|
||||||
span=1,
|
|
||||||
),
|
),
|
||||||
dmc.Col(
|
dmc.Col(
|
||||||
# radio buttons part
|
# radio buttons part
|
||||||
|
children = [
|
||||||
label_container,
|
label_container,
|
||||||
span=5,
|
dmc.Button(
|
||||||
|
"confirm",
|
||||||
|
id="submit-button",
|
||||||
|
fullWidth=True,
|
||||||
|
color="lime",
|
||||||
|
radius="sm",
|
||||||
|
size="md",
|
||||||
|
style={
|
||||||
|
"height": "50px"
|
||||||
|
}
|
||||||
),
|
),
|
||||||
|
], span=7,
|
||||||
|
),
|
||||||
|
# dmc.Col(span=1),
|
||||||
], grow=True
|
], grow=True
|
||||||
)
|
), fluid=True
|
||||||
)
|
)
|
||||||
return body_container
|
return body_container
|
||||||
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|
After Width: | Height: | Size: 26 KiB |
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|
After Width: | Height: | Size: 29 KiB |
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|
After Width: | Height: | Size: 37 KiB |
@@ -0,0 +1,27 @@
|
|||||||
|
from pathlib import Path
|
||||||
|
from dotenv import load_dotenv
|
||||||
|
from pymongo import MongoClient
|
||||||
|
from typing import List
|
||||||
|
import os
|
||||||
|
|
||||||
|
from src.database import VisualCommunication, connect
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
# prepare env vars
|
||||||
|
env_path = Path(__file__).parent.parent / "local.env"
|
||||||
|
assert env_path.exists()
|
||||||
|
load_dotenv(env_path)
|
||||||
|
os.environ["MONGO_HOST"] = "localhost"
|
||||||
|
# connect to database
|
||||||
|
collection, db, client = connect()
|
||||||
|
print(client.server_info())
|
||||||
|
# download images
|
||||||
|
data = None
|
||||||
|
for data in collection.find().limit(3):
|
||||||
|
if data is None:
|
||||||
|
print("no document found")
|
||||||
|
break
|
||||||
|
vis_com: VisualCommunication = VisualCommunication.model_validate(data)
|
||||||
|
print(repr(vis_com))
|
||||||
|
if data is not None:
|
||||||
|
print(vis_com.image)
|
||||||
@@ -0,0 +1,33 @@
|
|||||||
|
from pathlib import Path
|
||||||
|
from dotenv import load_dotenv
|
||||||
|
from pymongo.errors import DuplicateKeyError
|
||||||
|
import os
|
||||||
|
|
||||||
|
from src.database import VisualCommunication, connect
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
# get list of image paths
|
||||||
|
test_dir = Path(__file__).parent
|
||||||
|
img_dir = test_dir / "imgs"
|
||||||
|
img_path_list = [path for path in img_dir.glob("*.jpeg") if path.is_file()]
|
||||||
|
print(img_path_list)
|
||||||
|
# instantiate data object
|
||||||
|
vis_com_list = [VisualCommunication.from_file(path) for path in img_path_list]
|
||||||
|
for vis_com in vis_com_list:
|
||||||
|
print(repr(vis_com))
|
||||||
|
# prepare env vars
|
||||||
|
env_path = test_dir.parent / "local.env"
|
||||||
|
assert env_path.exists()
|
||||||
|
load_dotenv(env_path)
|
||||||
|
os.environ["MONGO_HOST"] = "localhost"
|
||||||
|
# connect to database
|
||||||
|
collection, db, client = connect()
|
||||||
|
print(client.server_info())
|
||||||
|
# upload images
|
||||||
|
for vis_com in vis_com_list:
|
||||||
|
try:
|
||||||
|
result = collection.insert_one(vis_com.model_dump())
|
||||||
|
except DuplicateKeyError as exc:
|
||||||
|
print("ignoring:\n", exc)
|
||||||
|
else:
|
||||||
|
print(f"inserted document: {result}")
|
||||||
Reference in New Issue
Block a user