brian/visual_critical_discourse_analysis
1
0
Fork 0
Code Issues 11 Pull Requests Actions Projects 1 Releases Activity

Merge pull request 'docker_webserver' (#10) from docker_webserver into main

Browse Source 
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
2024-02-21 20:07:06 +01:00
parent 78ef04cbf4 f1d08292f9
commit bd5897b27f
26 changed files with 975 additions and 76 deletions
Download Patch File Download Diff File Expand all files Collapse all files
.gitignore
+1 -1
View File
@@ -121,7 +121,7 @@ celerybeat.pid
*.sage.py
# Environments
.env
*.env
.venv
env/
venv/
Dockerfile
+3 -3
View File
@@ -34,7 +34,7 @@ ENV PATH="${POETRY_HOME}/bin:$PATH"
# install runtime dependencies
WORKDIR ${APP_HOME}
COPY poetry.lock pyproject.toml ./
RUN --mount=type=cache,target=${POETRY_CACHE_DIR} poetry install --no-root
RUN --mount=type=cache,target=${POETRY_CACHE_DIR} poetry install
#########
# FINAL #
@@ -56,9 +56,9 @@ RUN mkdir -p /home/app && \
# add code while changing ownership
WORKDIR $APP_HOME
COPY --chown=app:app ./src .
COPY --chown=app:app ./src ./src
# change to the app user
USER app
ENTRYPOINT [ "gunicorn", "web:server", "-b", "0.0.0.0:8050" ]
ENTRYPOINT [ "gunicorn", "src.main:server", "-b", "0.0.0.0:8050" ]
docker-compose.local.yml
+27 -1
View File
@@ -1,3 +1,4 @@
version: '3.7'
services:
app:
image: visual_critical_discourse_analysis:dev
@@ -5,11 +6,36 @@ services:
build:
context: .
dockerfile: Dockerfile
env_file:
- local.env
ports:
- 8050:8050
networks:
- backend
depends_on:
- mongo
mongo:
image: mongo:latest
container_name: mongo
env_file:
- local.env
ports:
- "27017:27017"
networks:
- backend
mongo-express:
image: mongo-express
ports:
- 8081:8081
env_file:
- local.env
links:
- mongo
networks:
- backend
depends_on:
- mongo
networks:
backend:
external: false
driver: bridge
docker-compose.server.yml
+12
View File
@@ -0,0 +1,12 @@
version: '3.7'
services:
app:
image: visual_critical_discourse_analysis:dev
container_name: visual_critical_discourse_analysis
build:
context: .
dockerfile: Dockerfile
env_file:
- server.env
ports:
- 8050:8050
poetry.lock
Generated
+333 -4
View File
@@ -1,5 +1,16 @@
# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
[[package]]
name = "annotated-types"
version = "0.6.0"
description = "Reusable constraint types to use with typing.Annotated"
optional = false
python-versions = ">=3.8"
files = [
{file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"},
{file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
]
[[package]]
name = "blinker"
version = "1.7.0"
@@ -257,6 +268,26 @@ requests = ">=2.28.1,<3.0.0"
[package.extras]
async = ["httpx (>=0.23.0,<0.24.0)"]
[[package]]
name = "dnspython"
version = "2.6.1"
description = "DNS toolkit"
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"},
]
[package.extras]
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)"]
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]]
name = "flask"
version = "3.0.2"
@@ -448,6 +479,91 @@ files = [
{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"},
{file = "pillow-10.2.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:9d189550615b4948f45252d7f005e53c2040cea1af5b60d6f79491a6e147eef7"},
{file = "pillow-10.2.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:49d9ba1ed0ef3e061088cd1e7538a0759aab559e2e0a80a36f9fd9d8c0c21591"},
{file = "pillow-10.2.0-cp39-cp39-win32.whl", hash = "sha256:babf5acfede515f176833ed6028754cbcd0d206f7f614ea3447d67c33be12516"},
{file = "pillow-10.2.0-cp39-cp39-win_amd64.whl", hash = "sha256:0304004f8067386b477d20a518b50f3fa658a28d44e4116970abfcd94fac34a8"},
{file = "pillow-10.2.0-cp39-cp39-win_arm64.whl", hash = "sha256:0fb3e7fc88a14eacd303e90481ad983fd5b69c761e9e6ef94c983f91025da869"},
{file = "pillow-10.2.0-pp310-pypy310_pp73-macosx_10_10_x86_64.whl", hash = "sha256:322209c642aabdd6207517e9739c704dc9f9db943015535783239022002f054a"},
{file = "pillow-10.2.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3eedd52442c0a5ff4f887fab0c1c0bb164d8635b32c894bc1faf4c618dd89df2"},
{file = "pillow-10.2.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cb28c753fd5eb3dd859b4ee95de66cc62af91bcff5db5f2571d32a520baf1f04"},
{file = "pillow-10.2.0-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:33870dc4653c5017bf4c8873e5488d8f8d5f8935e2f1fb9a2208c47cdd66efd2"},
{file = "pillow-10.2.0-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:3c31822339516fb3c82d03f30e22b1d038da87ef27b6a78c9549888f8ceda39a"},
{file = "pillow-10.2.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:a2b56ba36e05f973d450582fb015594aaa78834fefe8dfb8fcd79b93e64ba4c6"},
{file = "pillow-10.2.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:d8e6aeb9201e655354b3ad049cb77d19813ad4ece0df1249d3c793de3774f8c7"},
{file = "pillow-10.2.0-pp39-pypy39_pp73-macosx_10_10_x86_64.whl", hash = "sha256:2247178effb34a77c11c0e8ac355c7a741ceca0a732b27bf11e747bbc950722f"},
{file = "pillow-10.2.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:15587643b9e5eb26c48e49a7b33659790d28f190fc514a322d55da2fb5c2950e"},
{file = "pillow-10.2.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:753cd8f2086b2b80180d9b3010dd4ed147efc167c90d3bf593fe2af21265e5a5"},
{file = "pillow-10.2.0-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:7c8f97e8e7a9009bcacbe3766a36175056c12f9a44e6e6f2d5caad06dcfbf03b"},
{file = "pillow-10.2.0-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:d1b35bcd6c5543b9cb547dee3150c93008f8dd0f1fef78fc0cd2b141c5baf58a"},
{file = "pillow-10.2.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:fe4c15f6c9285dc54ce6553a3ce908ed37c8f3825b5a51a15c91442bb955b868"},
{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]]
name = "plotly"
version = "5.19.0"
@@ -463,6 +579,219 @@ files = [
packaging = "*"
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 = [
{file = "pydantic-2.6.1-py3-none-any.whl", hash = "sha256:0b6a909df3192245cb736509a92ff69e4fef76116feffec68e93a567347bae6f"},
{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 = [
{file = "pydantic_core-2.16.2-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:3fab4e75b8c525a4776e7630b9ee48aea50107fea6ca9f593c98da3f4d11bf7c"},
{file = "pydantic_core-2.16.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8bde5b48c65b8e807409e6f20baee5d2cd880e0fad00b1a811ebc43e39a00ab2"},
{file = "pydantic_core-2.16.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2924b89b16420712e9bb8192396026a8fbd6d8726224f918353ac19c4c043d2a"},
{file = "pydantic_core-2.16.2-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:16aa02e7a0f539098e215fc193c8926c897175d64c7926d00a36188917717a05"},
{file = "pydantic_core-2.16.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:936a787f83db1f2115ee829dd615c4f684ee48ac4de5779ab4300994d8af325b"},
{file = "pydantic_core-2.16.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:459d6be6134ce3b38e0ef76f8a672924460c455d45f1ad8fdade36796df1ddc8"},
{file = "pydantic_core-2.16.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4f9ee4febb249c591d07b2d4dd36ebcad0ccd128962aaa1801508320896575ef"},
{file = "pydantic_core-2.16.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:40a0bd0bed96dae5712dab2aba7d334a6c67cbcac2ddfca7dbcc4a8176445990"},
{file = "pydantic_core-2.16.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:870dbfa94de9b8866b37b867a2cb37a60c401d9deb4a9ea392abf11a1f98037b"},
{file = "pydantic_core-2.16.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:308974fdf98046db28440eb3377abba274808bf66262e042c412eb2adf852731"},
{file = "pydantic_core-2.16.2-cp310-none-win32.whl", hash = "sha256:a477932664d9611d7a0816cc3c0eb1f8856f8a42435488280dfbf4395e141485"},
{file = "pydantic_core-2.16.2-cp310-none-win_amd64.whl", hash = "sha256:8f9142a6ed83d90c94a3efd7af8873bf7cefed2d3d44387bf848888482e2d25f"},
{file = "pydantic_core-2.16.2-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:406fac1d09edc613020ce9cf3f2ccf1a1b2f57ab00552b4c18e3d5276c67eb11"},
{file = "pydantic_core-2.16.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:ce232a6170dd6532096cadbf6185271e4e8c70fc9217ebe105923ac105da9978"},
{file = "pydantic_core-2.16.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a90fec23b4b05a09ad988e7a4f4e081711a90eb2a55b9c984d8b74597599180f"},
{file = "pydantic_core-2.16.2-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:8aafeedb6597a163a9c9727d8a8bd363a93277701b7bfd2749fbefee2396469e"},
{file = "pydantic_core-2.16.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9957433c3a1b67bdd4c63717eaf174ebb749510d5ea612cd4e83f2d9142f3fc8"},
{file = "pydantic_core-2.16.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b0d7a9165167269758145756db43a133608a531b1e5bb6a626b9ee24bc38a8f7"},
{file = "pydantic_core-2.16.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dffaf740fe2e147fedcb6b561353a16243e654f7fe8e701b1b9db148242e1272"},
{file = "pydantic_core-2.16.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f8ed79883b4328b7f0bd142733d99c8e6b22703e908ec63d930b06be3a0e7113"},
{file = "pydantic_core-2.16.2-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:cf903310a34e14651c9de056fcc12ce090560864d5a2bb0174b971685684e1d8"},
{file = "pydantic_core-2.16.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:46b0d5520dbcafea9a8645a8164658777686c5c524d381d983317d29687cce97"},
{file = "pydantic_core-2.16.2-cp311-none-win32.whl", hash = "sha256:70651ff6e663428cea902dac297066d5c6e5423fda345a4ca62430575364d62b"},
{file = "pydantic_core-2.16.2-cp311-none-win_amd64.whl", hash = "sha256:98dc6f4f2095fc7ad277782a7c2c88296badcad92316b5a6e530930b1d475ebc"},
{file = "pydantic_core-2.16.2-cp311-none-win_arm64.whl", hash = "sha256:ef6113cd31411eaf9b39fc5a8848e71c72656fd418882488598758b2c8c6dfa0"},
{file = "pydantic_core-2.16.2-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:88646cae28eb1dd5cd1e09605680c2b043b64d7481cdad7f5003ebef401a3039"},
{file = "pydantic_core-2.16.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:7b883af50eaa6bb3299780651e5be921e88050ccf00e3e583b1e92020333304b"},
{file = "pydantic_core-2.16.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7bf26c2e2ea59d32807081ad51968133af3025c4ba5753e6a794683d2c91bf6e"},
{file = "pydantic_core-2.16.2-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:99af961d72ac731aae2a1b55ccbdae0733d816f8bfb97b41909e143de735f522"},
{file = "pydantic_core-2.16.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:02906e7306cb8c5901a1feb61f9ab5e5c690dbbeaa04d84c1b9ae2a01ebe9379"},
{file = "pydantic_core-2.16.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d5362d099c244a2d2f9659fb3c9db7c735f0004765bbe06b99be69fbd87c3f15"},
{file = "pydantic_core-2.16.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3ac426704840877a285d03a445e162eb258924f014e2f074e209d9b4ff7bf380"},
{file = "pydantic_core-2.16.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:b94cbda27267423411c928208e89adddf2ea5dd5f74b9528513f0358bba019cb"},
{file = "pydantic_core-2.16.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:6db58c22ac6c81aeac33912fb1af0e930bc9774166cdd56eade913d5f2fff35e"},
{file = "pydantic_core-2.16.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:396fdf88b1b503c9c59c84a08b6833ec0c3b5ad1a83230252a9e17b7dfb4cffc"},
{file = "pydantic_core-2.16.2-cp312-none-win32.whl", hash = "sha256:7c31669e0c8cc68400ef0c730c3a1e11317ba76b892deeefaf52dcb41d56ed5d"},
{file = "pydantic_core-2.16.2-cp312-none-win_amd64.whl", hash = "sha256:a3b7352b48fbc8b446b75f3069124e87f599d25afb8baa96a550256c031bb890"},
{file = "pydantic_core-2.16.2-cp312-none-win_arm64.whl", hash = "sha256:a9e523474998fb33f7c1a4d55f5504c908d57add624599e095c20fa575b8d943"},
{file = "pydantic_core-2.16.2-cp38-cp38-macosx_10_12_x86_64.whl", hash = "sha256:ae34418b6b389d601b31153b84dce480351a352e0bb763684a1b993d6be30f17"},
{file = "pydantic_core-2.16.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:732bd062c9e5d9582a30e8751461c1917dd1ccbdd6cafb032f02c86b20d2e7ec"},
{file = "pydantic_core-2.16.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e4b52776a2e3230f4854907a1e0946eec04d41b1fc64069ee774876bbe0eab55"},
{file = "pydantic_core-2.16.2-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ef551c053692b1e39e3f7950ce2296536728871110e7d75c4e7753fb30ca87f4"},
{file = "pydantic_core-2.16.2-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ebb892ed8599b23fa8f1799e13a12c87a97a6c9d0f497525ce9858564c4575a4"},
{file = "pydantic_core-2.16.2-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:aa6c8c582036275997a733427b88031a32ffa5dfc3124dc25a730658c47a572f"},
{file = "pydantic_core-2.16.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e4ba0884a91f1aecce75202473ab138724aa4fb26d7707f2e1fa6c3e68c84fbf"},
{file = "pydantic_core-2.16.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:7924e54f7ce5d253d6160090ddc6df25ed2feea25bfb3339b424a9dd591688bc"},
{file = "pydantic_core-2.16.2-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:69a7b96b59322a81c2203be537957313b07dd333105b73db0b69212c7d867b4b"},
{file = "pydantic_core-2.16.2-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:7e6231aa5bdacda78e96ad7b07d0c312f34ba35d717115f4b4bff6cb87224f0f"},
{file = "pydantic_core-2.16.2-cp38-none-win32.whl", hash = "sha256:41dac3b9fce187a25c6253ec79a3f9e2a7e761eb08690e90415069ea4a68ff7a"},
{file = "pydantic_core-2.16.2-cp38-none-win_amd64.whl", hash = "sha256:f685dbc1fdadb1dcd5b5e51e0a378d4685a891b2ddaf8e2bba89bd3a7144e44a"},
{file = "pydantic_core-2.16.2-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:55749f745ebf154c0d63d46c8c58594d8894b161928aa41adbb0709c1fe78b77"},
{file = "pydantic_core-2.16.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:b30b0dd58a4509c3bd7eefddf6338565c4905406aee0c6e4a5293841411a1286"},
{file = "pydantic_core-2.16.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:18de31781cdc7e7b28678df7c2d7882f9692ad060bc6ee3c94eb15a5d733f8f7"},
{file = "pydantic_core-2.16.2-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:5864b0242f74b9dd0b78fd39db1768bc3f00d1ffc14e596fd3e3f2ce43436a33"},
{file = "pydantic_core-2.16.2-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b8f9186ca45aee030dc8234118b9c0784ad91a0bb27fc4e7d9d6608a5e3d386c"},
{file = "pydantic_core-2.16.2-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cc6f6c9be0ab6da37bc77c2dda5f14b1d532d5dbef00311ee6e13357a418e646"},
{file = "pydantic_core-2.16.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:aa057095f621dad24a1e906747179a69780ef45cc8f69e97463692adbcdae878"},
{file = "pydantic_core-2.16.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:6ad84731a26bcfb299f9eab56c7932d46f9cad51c52768cace09e92a19e4cf55"},
{file = "pydantic_core-2.16.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:3b052c753c4babf2d1edc034c97851f867c87d6f3ea63a12e2700f159f5c41c3"},
{file = "pydantic_core-2.16.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:e0f686549e32ccdb02ae6f25eee40cc33900910085de6aa3790effd391ae10c2"},
{file = "pydantic_core-2.16.2-cp39-none-win32.whl", hash = "sha256:7afb844041e707ac9ad9acad2188a90bffce2c770e6dc2318be0c9916aef1469"},
{file = "pydantic_core-2.16.2-cp39-none-win_amd64.whl", hash = "sha256:9da90d393a8227d717c19f5397688a38635afec89f2e2d7af0df037f3249c39a"},
{file = "pydantic_core-2.16.2-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:5f60f920691a620b03082692c378661947d09415743e437a7478c309eb0e4f82"},
{file = "pydantic_core-2.16.2-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:47924039e785a04d4a4fa49455e51b4eb3422d6eaacfde9fc9abf8fdef164e8a"},
{file = "pydantic_core-2.16.2-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e6294e76b0380bb7a61eb8a39273c40b20beb35e8c87ee101062834ced19c545"},
{file = "pydantic_core-2.16.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fe56851c3f1d6f5384b3051c536cc81b3a93a73faf931f404fef95217cf1e10d"},
{file = "pydantic_core-2.16.2-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:9d776d30cde7e541b8180103c3f294ef7c1862fd45d81738d156d00551005784"},
{file = "pydantic_core-2.16.2-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:72f7919af5de5ecfaf1eba47bf9a5d8aa089a3340277276e5636d16ee97614d7"},
{file = "pydantic_core-2.16.2-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:4bfcbde6e06c56b30668a0c872d75a7ef3025dc3c1823a13cf29a0e9b33f67e8"},
{file = "pydantic_core-2.16.2-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:ff7c97eb7a29aba230389a2661edf2e9e06ce616c7e35aa764879b6894a44b25"},
{file = "pydantic_core-2.16.2-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:9b5f13857da99325dcabe1cc4e9e6a3d7b2e2c726248ba5dd4be3e8e4a0b6d0e"},
{file = "pydantic_core-2.16.2-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:a7e41e3ada4cca5f22b478c08e973c930e5e6c7ba3588fb8e35f2398cdcc1545"},
{file = "pydantic_core-2.16.2-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:60eb8ceaa40a41540b9acae6ae7c1f0a67d233c40dc4359c256ad2ad85bdf5e5"},
{file = "pydantic_core-2.16.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7beec26729d496a12fd23cf8da9944ee338c8b8a17035a560b585c36fe81af20"},
{file = "pydantic_core-2.16.2-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:22c5f022799f3cd6741e24f0443ead92ef42be93ffda0d29b2597208c94c3753"},
{file = "pydantic_core-2.16.2-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:eca58e319f4fd6df004762419612122b2c7e7d95ffafc37e890252f869f3fb2a"},
{file = "pydantic_core-2.16.2-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:ed957db4c33bc99895f3a1672eca7e80e8cda8bd1e29a80536b4ec2153fa9804"},
{file = "pydantic_core-2.16.2-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:459c0d338cc55d099798618f714b21b7ece17eb1a87879f2da20a3ff4c7628e2"},
{file = "pydantic_core-2.16.2.tar.gz", hash = "sha256:0ba503850d8b8dcc18391f10de896ae51d37fe5fe43dbfb6a35c5c5cad271a06"},
]
[package.dependencies]
typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
[[package]]
name = "pymongo"
version = "4.6.1"
description = "Python driver for MongoDB <http://www.mongodb.org>"
optional = false
python-versions = ">=3.7"
files = [
{file = "pymongo-4.6.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:4344c30025210b9fa80ec257b0e0aab5aa1d5cca91daa70d82ab97b482cc038e"},
{file = "pymongo-4.6.1-cp310-cp310-manylinux1_i686.whl", hash = "sha256:1c5654bb8bb2bdb10e7a0bc3c193dd8b49a960b9eebc4381ff5a2043f4c3c441"},
{file = "pymongo-4.6.1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:eaf2f65190c506def2581219572b9c70b8250615dc918b3b7c218361a51ec42e"},
{file = "pymongo-4.6.1-cp310-cp310-manylinux2014_i686.whl", hash = "sha256:262356ea5fcb13d35fb2ab6009d3927bafb9504ef02339338634fffd8a9f1ae4"},
{file = "pymongo-4.6.1-cp310-cp310-manylinux2014_ppc64le.whl", hash = "sha256:2dd2f6960ee3c9360bed7fb3c678be0ca2d00f877068556785ec2eb6b73d2414"},
{file = "pymongo-4.6.1-cp310-cp310-manylinux2014_s390x.whl", hash = "sha256:ff925f1cca42e933376d09ddc254598f8c5fcd36efc5cac0118bb36c36217c41"},
{file = "pymongo-4.6.1-cp310-cp310-manylinux2014_x86_64.whl", hash = "sha256:3cadf7f4c8e94d8a77874b54a63c80af01f4d48c4b669c8b6867f86a07ba994f"},
{file = "pymongo-4.6.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:55dac73316e7e8c2616ba2e6f62b750918e9e0ae0b2053699d66ca27a7790105"},
{file = "pymongo-4.6.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:154b361dcb358ad377d5d40df41ee35f1cc14c8691b50511547c12404f89b5cb"},
{file = "pymongo-4.6.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2940aa20e9cc328e8ddeacea8b9a6f5ddafe0b087fedad928912e787c65b4909"},
{file = "pymongo-4.6.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:010bc9aa90fd06e5cc52c8fac2c2fd4ef1b5f990d9638548dde178005770a5e8"},
{file = "pymongo-4.6.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e470fa4bace5f50076c32f4b3cc182b31303b4fefb9b87f990144515d572820b"},
{file = "pymongo-4.6.1-cp310-cp310-win32.whl", hash = "sha256:da08ea09eefa6b960c2dd9a68ec47949235485c623621eb1d6c02b46765322ac"},
{file = "pymongo-4.6.1-cp310-cp310-win_amd64.whl", hash = "sha256:13d613c866f9f07d51180f9a7da54ef491d130f169e999c27e7633abe8619ec9"},
{file = "pymongo-4.6.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:6a0ae7a48a6ef82ceb98a366948874834b86c84e288dbd55600c1abfc3ac1d88"},
{file = "pymongo-4.6.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5bd94c503271e79917b27c6e77f7c5474da6930b3fb9e70a12e68c2dff386b9a"},
{file = "pymongo-4.6.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2d4ccac3053b84a09251da8f5350bb684cbbf8c8c01eda6b5418417d0a8ab198"},
{file = "pymongo-4.6.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:349093675a2d3759e4fb42b596afffa2b2518c890492563d7905fac503b20daa"},
{file = "pymongo-4.6.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:88beb444fb438385e53dc9110852910ec2a22f0eab7dd489e827038fdc19ed8d"},
{file = "pymongo-4.6.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d8e62d06e90f60ea2a3d463ae51401475568b995bafaffd81767d208d84d7bb1"},
{file = "pymongo-4.6.1-cp311-cp311-win32.whl", hash = "sha256:5556e306713e2522e460287615d26c0af0fe5ed9d4f431dad35c6624c5d277e9"},
{file = "pymongo-4.6.1-cp311-cp311-win_amd64.whl", hash = "sha256:b10d8cda9fc2fcdcfa4a000aa10413a2bf8b575852cd07cb8a595ed09689ca98"},
{file = "pymongo-4.6.1-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:b435b13bb8e36be11b75f7384a34eefe487fe87a6267172964628e2b14ecf0a7"},
{file = "pymongo-4.6.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e438417ce1dc5b758742e12661d800482200b042d03512a8f31f6aaa9137ad40"},
{file = "pymongo-4.6.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8b47ebd89e69fbf33d1c2df79759d7162fc80c7652dacfec136dae1c9b3afac7"},
{file = "pymongo-4.6.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:bbed8cccebe1169d45cedf00461b2842652d476d2897fd1c42cf41b635d88746"},
{file = "pymongo-4.6.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c30a9e06041fbd7a7590693ec5e407aa8737ad91912a1e70176aff92e5c99d20"},
{file = "pymongo-4.6.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b8729dbf25eb32ad0dc0b9bd5e6a0d0b7e5c2dc8ec06ad171088e1896b522a74"},
{file = "pymongo-4.6.1-cp312-cp312-win32.whl", hash = "sha256:3177f783ae7e08aaf7b2802e0df4e4b13903520e8380915e6337cdc7a6ff01d8"},
{file = "pymongo-4.6.1-cp312-cp312-win_amd64.whl", hash = "sha256:00c199e1c593e2c8b033136d7a08f0c376452bac8a896c923fcd6f419e07bdd2"},
{file = "pymongo-4.6.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:6dcc95f4bb9ed793714b43f4f23a7b0c57e4ef47414162297d6f650213512c19"},
{file = "pymongo-4.6.1-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:13552ca505366df74e3e2f0a4f27c363928f3dff0eef9f281eb81af7f29bc3c5"},
{file = "pymongo-4.6.1-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:77e0df59b1a4994ad30c6d746992ae887f9756a43fc25dec2db515d94cf0222d"},
{file = "pymongo-4.6.1-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:3a7f02a58a0c2912734105e05dedbee4f7507e6f1bd132ebad520be0b11d46fd"},
{file = "pymongo-4.6.1-cp37-cp37m-manylinux2014_i686.whl", hash = "sha256:026a24a36394dc8930cbcb1d19d5eb35205ef3c838a7e619e04bd170713972e7"},
{file = "pymongo-4.6.1-cp37-cp37m-manylinux2014_ppc64le.whl", hash = "sha256:3b287e814a01deddb59b88549c1e0c87cefacd798d4afc0c8bd6042d1c3d48aa"},
{file = "pymongo-4.6.1-cp37-cp37m-manylinux2014_s390x.whl", hash = "sha256:9a710c184ba845afb05a6f876edac8f27783ba70e52d5eaf939f121fc13b2f59"},
{file = "pymongo-4.6.1-cp37-cp37m-manylinux2014_x86_64.whl", hash = "sha256:30b2c9caf3e55c2e323565d1f3b7e7881ab87db16997dc0cbca7c52885ed2347"},
{file = "pymongo-4.6.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ff62ba8ff70f01ab4fe0ae36b2cb0b5d1f42e73dfc81ddf0758cd9f77331ad25"},
{file = "pymongo-4.6.1-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:547dc5d7f834b1deefda51aedb11a7af9c51c45e689e44e14aa85d44147c7657"},
{file = "pymongo-4.6.1-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1de3c6faf948f3edd4e738abdb4b76572b4f4fdfc1fed4dad02427e70c5a6219"},
{file = "pymongo-4.6.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a2831e05ce0a4df10c4ac5399ef50b9a621f90894c2a4d2945dc5658765514ed"},
{file = "pymongo-4.6.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:144a31391a39a390efce0c5ebcaf4bf112114af4384c90163f402cec5ede476b"},
{file = "pymongo-4.6.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:33bb16a07d3cc4e0aea37b242097cd5f7a156312012455c2fa8ca396953b11c4"},
{file = "pymongo-4.6.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:b7b1a83ce514700276a46af3d9e481ec381f05b64939effc9065afe18456a6b9"},
{file = "pymongo-4.6.1-cp37-cp37m-win32.whl", hash = "sha256:3071ec998cc3d7b4944377e5f1217c2c44b811fae16f9a495c7a1ce9b42fb038"},
{file = "pymongo-4.6.1-cp37-cp37m-win_amd64.whl", hash = "sha256:2346450a075625c4d6166b40a013b605a38b6b6168ce2232b192a37fb200d588"},
{file = "pymongo-4.6.1-cp38-cp38-macosx_11_0_universal2.whl", hash = "sha256:061598cbc6abe2f382ab64c9caa83faa2f4c51256f732cdd890bcc6e63bfb67e"},
{file = "pymongo-4.6.1-cp38-cp38-manylinux1_i686.whl", hash = "sha256:d483793a384c550c2d12cb794ede294d303b42beff75f3b3081f57196660edaf"},
{file = "pymongo-4.6.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:f9756f1d25454ba6a3c2f1ef8b7ddec23e5cdeae3dc3c3377243ae37a383db00"},
{file = "pymongo-4.6.1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:1ed23b0e2dac6f84f44c8494fbceefe6eb5c35db5c1099f56ab78fc0d94ab3af"},
{file = "pymongo-4.6.1-cp38-cp38-manylinux2014_i686.whl", hash = "sha256:3d18a9b9b858ee140c15c5bfcb3e66e47e2a70a03272c2e72adda2482f76a6ad"},
{file = "pymongo-4.6.1-cp38-cp38-manylinux2014_ppc64le.whl", hash = "sha256:c258dbacfff1224f13576147df16ce3c02024a0d792fd0323ac01bed5d3c545d"},
{file = "pymongo-4.6.1-cp38-cp38-manylinux2014_s390x.whl", hash = "sha256:f7acc03a4f1154ba2643edeb13658d08598fe6e490c3dd96a241b94f09801626"},
{file = "pymongo-4.6.1-cp38-cp38-manylinux2014_x86_64.whl", hash = "sha256:76013fef1c9cd1cd00d55efde516c154aa169f2bf059b197c263a255ba8a9ddf"},
{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]]
name = "python-dotenv"
version = "1.0.1"
@@ -583,13 +912,13 @@ files = [
[[package]]
name = "urllib3"
version = "2.2.0"
version = "2.2.1"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
python-versions = ">=3.8"
files = [
{file = "urllib3-2.2.0-py3-none-any.whl", hash = "sha256:ce3711610ddce217e6d113a2732fafad960a03fd0318c91faa79481e35c11224"},
{file = "urllib3-2.2.0.tar.gz", hash = "sha256:051d961ad0c62a94e50ecf1af379c3aba230c66c710493493560c0c223c49f20"},
{file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
{file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
]
[package.extras]
@@ -633,4 +962,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
[metadata]
lock-version = "2.0"
python-versions = "^3.12"
content-hash = "b80bd2598b5e94ab048aff2a8615067d938b520c5e4e131547e4cbc1a8f664d5"
content-hash = "e4aacea5a98281d935411e0d96152d1d24680f6c1e5288e9a1be913a0536b78e"
pyproject.toml
+7 -1
View File
@@ -1,9 +1,12 @@
[tool.poetry]
name = "visual-critical-discourse-analysis"
name = "visual_critical_discourse_analysis"
version = "0.1.0"
description = ""
authors = ["Brian Bjarke Jensen <[email protected]>"]
readme = "README.md"
packages = [
{ include = "src" },
]
[tool.poetry.dependencies]
python = "^3.12"
@@ -13,6 +16,9 @@ python-dotenv = "^1.0.1"
dash = "^2.15.0"
dash-bootstrap-components = "^1.5.0"
dash-mantine-components = "^0.12.1"
pydantic = "^2.6.1"
pillow = "^10.2.0"
pymongo = "^4.6.1"
[build-system]
src/__init__.py
View File
src/database/__init__.py
+5
View File
@@ -0,0 +1,5 @@
from .classes import (
ModelOutputs,
VisualCommunication
)
from .database import connect
src/database/classes.py
+75
View File
@@ -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}')"
src/database/database.py
+21
View File
@@ -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
src/main.py
+2 -1
View File
@@ -6,7 +6,7 @@ from pathlib import Path
import logging
import os
from web import app
from src.web import server
# load default values
config = ConfigParser()
@@ -93,6 +93,7 @@ def initialise_app() -> None:
if __name__ == "__main__":
from src.web import app
# initialise_app()
app.run(debug=True)
logging.info("started app")
src/model_experiential/__init__.py
+24 -1
View File
@@ -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"
# ]
src/model_experiential/classes.py
+68
View File
@@ -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())
src/model_experiential/output.py
+1 -2
View File
@@ -1,5 +1,4 @@
model_labels = [
CLASS_NAMES = [
"non transactional action",
"non transactional reaction",
"unidirectional transactional action",
src/model_interpersonal/__init__.py
+9 -1
View File
@@ -1 +1,9 @@
from .output import model_labels
from .classes import (
ContactModelOutput,
AngleModelOutput,
PointOfViewModelOutput,
DistanceModelOutput,
ModalityLightingModelOutput,
ModalityColorModelOutput,
ModalityDepthModelOutput
)
src/model_interpersonal/classes.py
+120
View File
@@ -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())
src/model_interpersonal/output.py
+2 -2
View File
@@ -1,8 +1,8 @@
model_labels = {
"contact": [
"contact offer",
"contact demand"
"offer",
"demand"
],
"angle": [
"high",
src/model_textual/__init__.py
+5 -1
View File
@@ -1 +1,5 @@
from .output import model_labels
from .classes import (
InformationValueModelOutput,
FramingModelOutput,
SalienceModelOutput
)
src/model_textual/classes.py
+75
View File
@@ -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())
src/web/app.py
+4 -9
View File
@@ -4,9 +4,6 @@ import dash_mantine_components as dmc
from .header import generate_header
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])
@@ -20,14 +17,12 @@ app.layout = dmc.MantineProvider(
},
},
children=[
dmc.Container([
dmc.Container(
[
generate_header(),
generate_body(
experiential_labels,
interpersonal_labels,
textual_labels
generate_body(),
], fluid=True
),
]),
],
)
src/web/body.py
+117 -45
View File
@@ -1,71 +1,143 @@
import dash_mantine_components as dmc
from dash import dcc, html
from typing import List
def generate_body(
experiential_labels,
interpersonal_labels,
textual_labels
):
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
)
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(
width=400,
height=400,
width=600,
height=600,
withPlaceholder=True,
placeholder=[dmc.Loader(color="gray", size="md")],
)
experiential_labels_container = dmc.Container(
# prepare experiential container
experiential_map = generate_experiential_options_map()
experiential_container = dmc.Col(
children=[
html.H4("experiential labels".title()),
dcc.RadioItems(options=list(experiential_labels)),
]
dmc.Container([
html.H4(list(experiential_map.keys())[0]),
html.B("visual syntax".title()),
dcc.RadioItems(options=list(experiential_map.values())[0]),
])
], span=4
)
interpersonal_labels_container = dmc.Container(
children=[]
)
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(
# prepare interpersonal container
interpersonal_map = generate_interpersonal_options_map()
interpersonal_container = dmc.Col(
children=[
experiential_labels_container,
dmc.Divider(),
interpersonal_labels_container,
dmc.Divider(),
textual_labels_container,
dmc.Divider(),
html.Button(
"confirm",
id="submit-button"
html.H4("interpersonal".title()),
], span=4
)
]
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(
dmc.Grid(
children=[
dmc.Col(
dmc.Center(
image_container,
span=5,
),
dmc.Col(
dmc.Divider(orientation="vertical"),
span=1,
span=5,
),
dmc.Col(
# radio buttons part
children = [
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
)
), fluid=True
)
return body_container
tests/imgs/18-12-14-ESP-01a.jpeg
BIN
View File
Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 26 KiB

tests/imgs/18-12-14-ESP-01b.jpeg
BIN
View File
Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 29 KiB

tests/imgs/18-12-14-ESP-01c.jpeg
BIN
View File
Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 37 KiB

tests/test_image_download.py
+27
View File
@@ -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)
tests/test_image_upload.py
+33
View File
@@ -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}")