diff --git a/.gitignore b/.gitignore index 33e7851..30ffc03 100644 --- a/.gitignore +++ b/.gitignore @@ -121,7 +121,7 @@ celerybeat.pid *.sage.py # Environments -.env +*.env .venv env/ venv/ diff --git a/Dockerfile b/Dockerfile index 8f6a6ca..5671d4d 100644 --- a/Dockerfile +++ b/Dockerfile @@ -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" ] \ No newline at end of file +ENTRYPOINT [ "gunicorn", "src.main:server", "-b", "0.0.0.0:8050" ] \ No newline at end of file diff --git a/docker-compose.local.yml b/docker-compose.local.yml index 7f01e12..247052c 100644 --- a/docker-compose.local.yml +++ b/docker-compose.local.yml @@ -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 \ No newline at end of file + driver: bridge \ No newline at end of file diff --git a/docker-compose.server.yml b/docker-compose.server.yml new file mode 100644 index 0000000..73a4d10 --- /dev/null +++ b/docker-compose.server.yml @@ -0,0 +1,12 @@ +version: '3.7' +services: + 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"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" diff --git a/pyproject.toml b/pyproject.toml index eb5a355..f71a170 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -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 "] 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] diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/database/__init__.py b/src/database/__init__.py new file mode 100644 index 0000000..4083b77 --- /dev/null +++ b/src/database/__init__.py @@ -0,0 +1,5 @@ +from .classes import ( + ModelOutputs, + VisualCommunication +) +from .database import connect \ No newline at end of file diff --git a/src/database/classes.py b/src/database/classes.py new file mode 100644 index 0000000..39aad3a --- /dev/null +++ b/src/database/classes.py @@ -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}')" diff --git a/src/database/database.py b/src/database/database.py new file mode 100644 index 0000000..692eb70 --- /dev/null +++ b/src/database/database.py @@ -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 diff --git a/src/main.py b/src/main.py index d362393..7b0b5ea 100644 --- a/src/main.py +++ b/src/main.py @@ -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") \ No newline at end of file diff --git a/src/model_experiential/__init__.py b/src/model_experiential/__init__.py index b0d4f72..9a2791c 100644 --- a/src/model_experiential/__init__.py +++ b/src/model_experiential/__init__.py @@ -1 +1,24 @@ -from .output import model_labels \ No newline at end of file +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" +# ] \ No newline at end of file diff --git a/src/model_experiential/classes.py b/src/model_experiential/classes.py new file mode 100644 index 0000000..29f550e --- /dev/null +++ b/src/model_experiential/classes.py @@ -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()) diff --git a/src/model_experiential/output.py b/src/model_experiential/output.py index bddaf06..188234f 100644 --- a/src/model_experiential/output.py +++ b/src/model_experiential/output.py @@ -1,5 +1,4 @@ - -model_labels = [ +CLASS_NAMES = [ "non transactional action", "non transactional reaction", "unidirectional transactional action", diff --git a/src/model_interpersonal/__init__.py b/src/model_interpersonal/__init__.py index b0d4f72..74211a9 100644 --- a/src/model_interpersonal/__init__.py +++ b/src/model_interpersonal/__init__.py @@ -1 +1,9 @@ -from .output import model_labels \ No newline at end of file +from .classes import ( + ContactModelOutput, + AngleModelOutput, + PointOfViewModelOutput, + DistanceModelOutput, + ModalityLightingModelOutput, + ModalityColorModelOutput, + ModalityDepthModelOutput +) \ No newline at end of file diff --git a/src/model_interpersonal/classes.py b/src/model_interpersonal/classes.py new file mode 100644 index 0000000..d57717d --- /dev/null +++ b/src/model_interpersonal/classes.py @@ -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()) diff --git a/src/model_interpersonal/output.py b/src/model_interpersonal/output.py index a95e916..14c84a9 100644 --- a/src/model_interpersonal/output.py +++ b/src/model_interpersonal/output.py @@ -1,8 +1,8 @@ model_labels = { "contact": [ - "contact offer", - "contact demand" + "offer", + "demand" ], "angle": [ "high", diff --git a/src/model_textual/__init__.py b/src/model_textual/__init__.py index b0d4f72..9e7b87c 100644 --- a/src/model_textual/__init__.py +++ b/src/model_textual/__init__.py @@ -1 +1,5 @@ -from .output import model_labels \ No newline at end of file +from .classes import ( + InformationValueModelOutput, + FramingModelOutput, + SalienceModelOutput +) \ No newline at end of file diff --git a/src/model_textual/classes.py b/src/model_textual/classes.py new file mode 100644 index 0000000..a9bc4d1 --- /dev/null +++ b/src/model_textual/classes.py @@ -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()) \ No newline at end of file diff --git a/src/web/app.py b/src/web/app.py index 807b8b8..eac840a 100644 --- a/src/web/app.py +++ b/src/web/app.py @@ -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 + ), ], ) \ No newline at end of file diff --git a/src/web/body.py b/src/web/body.py index dbab304..1e5e1c9 100644 --- a/src/web/body.py +++ b/src/web/body.py @@ -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 \ No newline at end of file diff --git a/tests/imgs/18-12-14-ESP-01a.jpeg b/tests/imgs/18-12-14-ESP-01a.jpeg new file mode 100644 index 0000000..ebaa5ee Binary files /dev/null and b/tests/imgs/18-12-14-ESP-01a.jpeg differ diff --git a/tests/imgs/18-12-14-ESP-01b.jpeg b/tests/imgs/18-12-14-ESP-01b.jpeg new file mode 100644 index 0000000..060f79a Binary files /dev/null and b/tests/imgs/18-12-14-ESP-01b.jpeg differ diff --git a/tests/imgs/18-12-14-ESP-01c.jpeg b/tests/imgs/18-12-14-ESP-01c.jpeg new file mode 100644 index 0000000..a2d79a7 Binary files /dev/null and b/tests/imgs/18-12-14-ESP-01c.jpeg differ diff --git a/tests/test_image_download.py b/tests/test_image_download.py new file mode 100644 index 0000000..2e2b860 --- /dev/null +++ b/tests/test_image_download.py @@ -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) diff --git a/tests/test_image_upload.py b/tests/test_image_upload.py new file mode 100644 index 0000000..f4231f9 --- /dev/null +++ b/tests/test_image_upload.py @@ -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}")