fixed code import bug

docker-compose.local.yml

@@ -1,21 +1,20 @@
-version: '3.7'
 services:
-  # app:
-  #   image: visual_critical_discourse_analysis:dev
-  #   container_name: visual_critical_discourse_analysis
-  #   build:
-  #     context: .
-  #     dockerfile: Dockerfile
-  #   env_file:
-  #     - local.env
-  #   environment:
-  #     - ENV=DEV
-  #   ports:
-  #     - 8050:8050
-  #   networks:
-  #     - backend
-  #   depends_on:
-  #     - mongo
+  app:
+    image: visual_critical_discourse_analysis:dev
+    container_name: visual_critical_discourse_analysis
+    build:
+      context: .
+      dockerfile: ./web_ui/Dockerfile
+    env_file:
+      - local.env
+    environment:
+      - ENV=DEV
+    ports:
+      - 8050:8050
+    networks:
+      - backend
+    depends_on:
+      - mongo
   mongo:
     image: mongo:latest
     container_name: mongo

docker-compose.server.yml

@@ -1,11 +1,10 @@
-version: '3.7'
 services:
   app:
     image: visual_critical_discourse_analysis:dev
     container_name: visual_critical_discourse_analysis
     build:
       context: .
-      dockerfile: Dockerfile
+      dockerfile: ./web_ui/Dockerfile
     env_file:
       - server.env
     ports:

src/model_experiential/__init__.py

@@ -1 +0,0 @@
-from .classes import VisualSyntaxModelOutput

src/model_experiential/classes.py

@@ -1,89 +0,0 @@
-from pydantic import BaseModel, ValidationError
-from typing import List
-import random
-
-
-class OptionNotSetException(Exception):
-    pass
-
-
-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)
-
-    @classmethod
-    def from_choice(cls, option: str):
-        """Instantiate from choice."""
-        if option is None:
-            raise ValidationError()
-        assert isinstance(option, str), "option is not a string"
-        allowed_options_list = cls.list_fields()
-        assert option in allowed_options_list, \
-            f"{option} is not among allowed fields {allowed_options_list}"
-        kwargs = {field: 0 for field in cls.list_fields()}
-        kwargs[option] = 1
-        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 VisualSyntaxModelOutput(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 = VisualSyntaxModelOutput.from_random()
-    print(m)
-    print(repr(m))
-    print(m.highest_score_field())
-    print(m.highest_score_value())

src/model_experiential/output.py

@@ -1,20 +0,0 @@
-CLASS_NAMES = [
-    "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_interpersonal/__init__.py

@@ -1,9 +0,0 @@
-from .classes import (
-    ContactModelOutput,
-    AngleModelOutput,
-    PointOfViewModelOutput,
-    DistanceModelOutput,
-    ModalityLightingModelOutput,
-    ModalityColorModelOutput,
-    ModalityDepthModelOutput
-)

src/model_interpersonal/classes.py

@@ -1,137 +0,0 @@
-from pydantic import BaseModel, ValidationError
-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)
-
-    @classmethod
-    def from_choice(cls, option: str):
-        """Instantiate from choice."""
-        if option is None:
-            raise ValidationError()
-        assert isinstance(option, str)
-        allowed_options_list = cls.list_fields()
-        assert option in allowed_options_list, \
-            f"{option} is not among allowed fields {allowed_options_list}"
-        kwargs = {field: 0 for field in cls.list_fields()}
-        kwargs[option] = 1
-        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

@@ -1,36 +0,0 @@
-
-model_labels = {
-    "contact": [
-        "offer",
-        "demand"
-    ],
-    "angle": [
-        "high",
-        "eye-level",
-        "low"
-    ],
-    "point-of-view": [
-        "frontal",
-        "oblique"
-    ],
-    "distance": [
-        "long",
-        "medium",
-        "close"
-    ],
-    "modality lighting": [
-        "high",
-        "medium",
-        "low"
-    ],
-    "modality color": [
-        "high",
-        "medium",
-        "low"
-    ],
-    "modality depth": [
-        "high",
-        "medium",
-        "low"
-    ]
-}

src/model_textual/__init__.py

@@ -1,5 +0,0 @@
-from .classes import (
-    InformationValueModelOutput,
-    FramingModelOutput,
-    SalienceModelOutput
-)

src/model_textual/classes.py

@@ -1,92 +0,0 @@
-from pydantic import BaseModel, ValidationError
-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)
-
-    @classmethod
-    def from_choice(cls, option: str):
-        """Instantiate from choice."""
-        if option is None:
-            raise ValidationError()
-        assert isinstance(option, str)
-        allowed_options_list = cls.list_fields()
-        assert option in allowed_options_list, \
-            f"{option} is not among allowed fields {allowed_options_list}"
-        kwargs = {field: 0 for field in cls.list_fields()}
-        kwargs[option] = 1
-        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/model_textual/output.py

@@ -1,21 +0,0 @@
-
-model_labels = {
-    "information value": [
-        "given-new",
-        "ideal-real",
-        "central-marginal"
-    ],
-    "framing": [
-        "frame lines",
-        "empty space",
-        "colour contrast",
-        "form contrast"
-    ],
-    "salience": [
-        "size",
-        "colour",
-        "tone",
-        "form",
-        "positioning"
-    ]
-}

src/web/__init__.py

@@ -1,4 +0,0 @@
-from __future__ import annotations
-
-from src.web.app import app
-from src.web.app import server

src/web/layout/__init__.py

@@ -1 +0,0 @@
-from .layout import app_layout

src/web/layout/image.py

@@ -1,21 +0,0 @@
-import dash_mantine_components as dmc
-from dash import html
-from pathlib import Path
-from base64 import b64encode
-
-# read init img
-init_img_path = Path(__file__).parent / "init_img.png"
-with open(init_img_path.absolute(), "rb") as fh:
-    init_img_enc = b64encode(fh.read()).decode("utf-8")
-# generate init img string
-init_img_src = f"data:image/png;base64, {init_img_enc}"
-
-image_element = dmc.Center(
-    html.Img(
-        style={
-            "width": "100%",
-        },
-        id="image-container",
-        src=init_img_src
-    )
-)

web_ui/Dockerfile

@@ -0,0 +1,57 @@
+# build stage
+FROM python:3.12-slim-bookworm as BUILDER
+
+# set environment variables
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=off \
+    PIP_DISABLE_PIP_VERSION_CHECK=ON \
+    PIP_DEFAULT_TIMEOUT=100 \
+    DEBIAN_FRONTEND=noninteractive \
+    POETRY_HOME=/etc/poetry \
+    POETRY_VERSION=1.7.1 \
+    POETRY_VIRTUALENVS_IN_PROJECT=1 \
+    POETRY_VIRTUALENVS_CREATE=1 \
+    POETRY_NO_INTERACTION=1 \
+    POETRY_CACHE_DIR=/tmp/poetry_cache \
+    APP_HOME=/home/app
+
+# update system
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends \
+    build-essential \
+    curl \
+    && apt-get clean
+
+# install poetry
+RUN curl -sSL https://install.python-poetry.org | python3 -
+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 --without model
+
+# final stage
+FROM python:3.12-slim-bookworm
+
+# copy virtualenv made by poetry
+ENV APP_HOME=/home/app \
+    VIRTUAL_ENV=/home/app/.venv
+COPY --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV}
+ENV PATH="${VIRTUAL_ENV}/bin:${PATH}"
+
+# create home directory and app user
+RUN mkdir -p /home/app && \
+    addgroup --system app && \
+    adduser --system --group app
+
+# add code while changing ownership
+WORKDIR $APP_HOME
+COPY --chown=app:app ./core ./core
+COPY --chown=app:app ./web_ui/src ./src
+
+# change to the app user
+USER app
+
+ENTRYPOINT [ "gunicorn", "src.main:server", "-b", "0.0.0.0:8050" ]

web_ui/src/app/__init__.py

@@ -0,0 +1,3 @@
+from __future__ import annotations
+
+from .app import app

web_ui/src/app/app.py

@@ -245,3 +245,7 @@ def cycle_visual_communication_data(
         response[3] = annotation_values
         logging.info('finished getting visual communication: %s', vis_com_name)
     return tuple(response)
+
+
+if __name__ == '__main__':
+    app.run(debug=True)

web_ui/src/app/layout/__init__.py

@@ -0,0 +1,3 @@
+from __future__ import annotations
+
+from .layout import app_layout

web_ui/src/app/layout/body.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import dash_mantine_components as dmc
 
 from .image import image_element
@@ -11,8 +13,8 @@ body_element = dmc.Container(
             grow=True,
             children=[
                 dmc.Col([image_element], span=5),
-                dmc.Col([inputs_element], span=7)
+                dmc.Col([inputs_element], span=7),
             ],
-        )
+        ),
     ],
 )

web_ui/src/app/layout/image.py

@@ -0,0 +1,24 @@
+from __future__ import annotations
+
+from base64 import b64encode
+from pathlib import Path
+
+import dash_mantine_components as dmc
+from dash import html
+
+# read init img
+init_img_path = Path(__file__).parent / 'init_img.png'
+with open(init_img_path.absolute(), 'rb') as fh:
+    init_img_enc = b64encode(fh.read()).decode('utf-8')
+# generate init img string
+init_img_src = f"data:image/png;base64, {init_img_enc}"
+
+image_element = dmc.Center(
+    html.Img(
+        style={
+            'width': '100%',
+        },
+        id='image-container',
+        src=init_img_src,
+    ),
+)

web_ui/src/app/layout/inputs.py

@@ -1,23 +1,25 @@
+from __future__ import annotations
+
 import dash_mantine_components as dmc
 
 from .labels import labels_element
 
 next_button = dmc.Button(
-    "next".title(),
-    id="next-button",
+    'next'.title(),
+    id='next-button',
     n_clicks=0,
     fullWidth=True,
-    color="lime",
-    radius="sm",
-    size="md",
+    color='lime',
+    radius='sm',
+    size='md',
     style={
-        "height": "50px"
-    }
+        'height': '50px',
+    },
 )
 
 inputs_element = dmc.SimpleGrid(
     children=[
         labels_element,
-        next_button
-    ]
+        next_button,
+    ],
 )

web_ui/src/app/layout/layout.py

@@ -1,22 +1,24 @@
+from __future__ import annotations
+
 import dash_mantine_components as dmc
 
-from .stores import stores_element
 from .alerts import alerts_element
-from .header import header_element
 from .body import body_element
+from .header import header_element
+from .stores import stores_element
 
 
 app_layout = dmc.MantineProvider(
     theme={
-        "fontFamily": '"Inter", sans-serif',
-        "components": {
-            "NavLink": {
-                "styles": {
-                    "label": {
-                        "color": "#c2c7d0"
-                    }
-                }
-            }
+        'fontFamily': '"Inter", sans-serif',
+        'components': {
+            'NavLink': {
+                'styles': {
+                    'label': {
+                        'color': '#c2c7d0',
+                    },
+                },
+            },
         },
     },
     children=[
@@ -27,8 +29,8 @@ app_layout = dmc.MantineProvider(
                 header_element,
                 body_element,
             ],
-            fluid=True
+            fluid=True,
         ),
 
-    ]
+    ],
 )

web_ui/src/main.py

@@ -6,10 +6,10 @@ from pathlib import Path
 
 from dotenv import load_dotenv
 
-from src.web import app
+from .app import app
 
 # prepare optional local setup
-env_path = Path(__file__).parent.parent / 'local.env'
+env_path = Path(__file__).parent.parent.parent / 'local.env'
 load_dotenv(env_path)
 
 # ensure env vars set
@@ -31,18 +31,19 @@ for env_var in necesasary_var_list:
     )
 
 # setup logging stream handler
-fmt = (
+FMT = (
     '%(asctime)s | '
     '%(levelname)s | '
     '%(filename)s | '
     '%(funcName)s | '
     '%(message)s'
 )
-datefmt = '%Y-%m-%d %H:%M:%S'
-logging.basicConfig(format=fmt, datefmt=datefmt, level=logging.INFO)
+DATEFMT = '%Y-%m-%d %H:%M:%S'
+logging.basicConfig(format=FMT, datefmt=DATEFMT, level=logging.INFO)
 
 logging.info('initialized app')
 server = app.server
+server.config.update(SECRET_KEY=os.urandom(24))
 
 if __name__ == '__main__':
     # prepare local env vars