brian/visual_critical_discourse_analysis
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docker_webserver #10

Merged
brian merged 22 commits from docker_webserver into main 2024-02-21 20:07:07 +01:00
Conversation 0 Commits 22 Files Changed 26
+975 -76
26 changed files with 975 additions and 76 deletions
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.gitignore
+1 -1
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@@ -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"},
]
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name = "pillow"
version = "10.2.0"
description = "Python Imaging Library (Fork)"
optional = false
python-versions = ">=3.8"
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[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]]
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version = "2.6.1"
description = "Data validation using Python type hints"
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pydantic-core = "2.16.2"
typing-extensions = ">=4.6.1"
[package.extras]
email = ["email-validator (>=2.0.0)"]
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description = ""
optional = false
python-versions = ">=3.8"
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{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
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@@ -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
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@@ -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
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@@ -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
+6 -11
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([
generate_header(),
generate_body(
experiential_labels,
interpersonal_labels,
textual_labels
),
]),
dmc.Container(
[
generate_header(),
generate_body(),
], fluid=True
),
],
)
src/web/body.py
+119 -47
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,
),
dmc.Col(
# radio buttons part
label_container,
span=5,
children = [
label_container,
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
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tests/test_image_download.py
+27
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@@ -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
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@@ -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}")