added image upload to database test

.gitignore

@@ -121,7 +121,7 @@ celerybeat.pid
 *.sage.py
 
 # Environments
-.env
+*.env
 .venv
 env/
 venv/

docker-compose.local.yml

@@ -1,3 +1,4 @@
+version: '3.7'
 services:
   app:
     image: visual_critical_discourse_analysis:dev
@@ -5,11 +6,42 @@ services:
     build:
       context: .
       dockerfile: Dockerfile
+    env_file:
+      - mongodb.env
+    environment:
+      - MONGO_HOST=mongo
     ports:
       - 8050:8050
     networks:
       - backend
+    depends_on:
+      - mongo
+  mongo:
+    image: mongo:latest
+    container_name: mongo
+    env_file:
+      - mongodb.env
+    ports:
+      - "27017:27017"
+    networks:
+      - backend
+  mongo-express:
+    image: mongo-express
+    ports:
+      - 8081:8081
+    environment:
+      ME_CONFIG_MONGODB_ADMINUSERNAME: root
+      ME_CONFIG_MONGODB_ADMINPASSWORD: vSH7I7RxsDvb
+      ME_CONFIG_MONGODB_PORT: 27017
+      ME_CONFIG_BASICAUTH_USERNAME: admin
+      ME_CONFIG_BASICAUTH_PASSWORD: q
+    links:
+      - mongo
+    networks:
+      - backend
+    depends_on:
+      - mongo
 
 networks:
   backend:
-    external: false
+    driver: bridge

poetry.lock

@@ -268,6 +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"
@@ -669,6 +689,109 @@ files = [
 [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"
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+    {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"
@@ -839,4 +962,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.12"
-content-hash = "e4892a5e8db437b5c79b40f259ba6b512d1c0b18677b4f13163fee518276a28d"
+content-hash = "e4aacea5a98281d935411e0d96152d1d24680f6c1e5288e9a1be913a0536b78e"

pyproject.toml

@@ -18,6 +18,7 @@ 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/database/__init__.py

@@ -0,0 +1,5 @@
+from .classes import (
+    ModelOutputs,
+    VisualCommunication
+)
+from .database import connect

src/database/classes.py

@@ -0,0 +1,69 @@
+from __future__ import annotations
+from pydantic import BaseModel, field_validator
+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: bytes
+    annotation: ModelOutputs | None = None
+    prediction: ModelOutputs | None = None
+    
+    @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)
+        return VisualCommunication(name=name, image=image)
+    
+    @field_validator("image", mode="before")
+    @classmethod
+    def convert_to_bytes(cls, raw: Image.Image | BytesIO | bytes) -> bytes:
+        if isinstance(raw, Image.Image):
+            raw = raw.tobytes()
+        if isinstance(raw, BytesIO):
+            raw = raw.read()
+        return raw
+    
+    def __repr__(self) -> str:
+        return f"{self.classname()}(name='{self.name}')"
+
+    @property
+    def image(self) -> Image.Image:
+        return Image.open(BytesIO(self.image))

src/database/database.py

@@ -0,0 +1,22 @@
+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")]
+    return collection, db, client
+
+

src/model_experiential/__init__.py

@@ -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

@@ -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

@@ -1,5 +1,4 @@
-
+CLASS_NAMES = [
-model_labels = [
     "non transactional action",
     "non transactional reaction",
     "unidirectional transactional action",

src/model_interpersonal/__init__.py

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

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())

src/model_interpersonal/output.py

@@ -1,8 +1,8 @@
 
 model_labels = {
     "contact": [
-        "contact offer",
+        "offer",
-        "contact demand"
+        "demand"
     ],
     "angle": [
         "high",

src/model_textual/__init__.py

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

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())

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([
+        dmc.Container(
+            [
                 generate_header(),
-            generate_body(
+                generate_body(),
-                experiential_labels,
+            ], fluid=True
-                interpersonal_labels,
-                textual_labels
         ),
-        ]),
     ],
 )
         

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(
+from src.model_experiential import ExperientialModelOutput
-    experiential_labels,
+from src.model_interpersonal import (
-    interpersonal_labels,
+    ContactModelOutput,
-    textual_labels
+    AngleModelOutput,
-):
+    PointOfViewModelOutput,
+    DistanceModelOutput,
+    ModalityLightingModelOutput,
+    ModalityColorModelOutput,
+    ModalityDepthModelOutput
+)
+from src.model_textual import (
+    InformationValueModelOutput,
+    FramingModelOutput,
+    SalienceModelOutput
+)
+
+def generate_option_labels(model) -> List[str]:
+    """Generate presentable list of attributes from an OutputModel."""
+    labels = [
+        label.replace('_', ' ').title()
+        for label in model.list_fields()
+    ]
+    return labels
+
+def generate_experiential_options_map():
+    """Generate map of titles and options for experiential labels."""
+    options_map = {}
+    # add experiential labels
+    options_map["experiential".title()] = generate_option_labels(ExperientialModelOutput)
+    return options_map
+
+def generate_interpersonal_options_map():
+    """Generate map of titles and options for interpersonal labels."""
+    options_map = {}
+    # add interpersonal labels
+    options_map["contact".title()] = generate_option_labels(ContactModelOutput)
+    options_map["angle".title()] = generate_option_labels(AngleModelOutput)
+    options_map["point of view".title()] = generate_option_labels(PointOfViewModelOutput)
+    options_map["distance".title()] = generate_option_labels(DistanceModelOutput)
+    options_map["modality lighting".title()] = generate_option_labels(ModalityLightingModelOutput)
+    options_map["modality color".title()] = generate_option_labels(ModalityColorModelOutput)
+    options_map["modality depth".title()] = generate_option_labels(ModalityDepthModelOutput)
+    return options_map
+
+def generate_textual_options_map():
+    """Generate map of titles and options for textual labels."""
+    options_map = {}
+    # add textual labels
+    options_map["information value".title()] = generate_option_labels(InformationValueModelOutput)
+    options_map["framing".title()] = generate_option_labels(FramingModelOutput)
+    options_map["salience".title()] = generate_option_labels(SalienceModelOutput)
+    return options_map
+
+def generate_body():
     image_container = dmc.Image(
-        width=400,
+        width=600,
-        height=400,
+        height=600,
         withPlaceholder=True,
         placeholder=[dmc.Loader(color="gray", size="md")],
     )
-
+    # prepare experiential container
-    experiential_labels_container = dmc.Container(
+    experiential_map = generate_experiential_options_map()
+    experiential_container = dmc.Col(
         children=[
-            html.H4("experiential labels".title()),
+            dmc.Container([
-            dcc.RadioItems(options=list(experiential_labels)),
+                html.H4(list(experiential_map.keys())[0]),
-        ]
+                html.B("visual syntax".title()),
+                dcc.RadioItems(options=list(experiential_map.values())[0]),
+            ])
+        ], span=4
     )
-
+    # prepare interpersonal container
-    interpersonal_labels_container = dmc.Container(
+    interpersonal_map = generate_interpersonal_options_map()
-        children=[]
+    interpersonal_container = dmc.Col(
-    )
-    for category, options in interpersonal_labels.items():
-        interpersonal_labels_container.children.append(html.H4(category.title()))
-        interpersonal_labels_container.children.append(dcc.RadioItems(options))
-
-    textual_labels_container = dmc.Container(
-        children=[]
-    )
-    for category, options in textual_labels.items():
-        textual_labels_container.children.append(html.H4(category.title()))
-        textual_labels_container.children.append(dcc.RadioItems(options))
-
-    label_container = dmc.Container(
         children=[
-            experiential_labels_container,
+            html.H4("interpersonal".title()),
-            dmc.Divider(),
+        ], span=4
-            interpersonal_labels_container,
-            dmc.Divider(),
-            textual_labels_container,
-            dmc.Divider(),
-            html.Button(
-                "confirm",
-                id="submit-button"
     )
-        ]
+    for title, options in interpersonal_map.items():
+        interpersonal_container.children.append(
+            dmc.Container([
+                html.B(title),
+                dcc.RadioItems(options)
+            ])
         )
-
+    # prepare textual container
+    textual_map = generate_textual_options_map()
+    textual_container = dmc.Col(
+        children=[
+            html.H4("textual".title()),
+        ], span=4
+    )
+    for title, options in textual_map.items():
+        textual_container.children.append(
+            dmc.Container([
+                html.B(title),
+                dcc.RadioItems(options)
+            ])
+        )
+    # prepare labels container
+    label_container = dmc.Grid(
+        children=[
+            experiential_container,
+            interpersonal_container,
+            textual_container,
+        ],
+    )
+    # build the full body container
     body_container = dmc.Container(
         dmc.Grid(
             children=[
                 dmc.Col(
+                    dmc.Center(
                     image_container,
-                    span=5,
                     ),
-                dmc.Col(
+                    span=5,
-                    dmc.Divider(orientation="vertical"),
-                    span=1,
                 ),
                 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/test_image_upload.py

@@ -0,0 +1,26 @@
+from pathlib import Path
+from dotenv import load_dotenv
+from pymongo import MongoClient
+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))
+    # upload images to database
+    env_path = test_dir.parent / "mongodb.env"
+    assert env_path.exists()
+    load_dotenv(env_path)
+    collection, db, client = connect()
+    print(client.server_info())
+    for vis_com in vis_com_list:
+        result = collection.insert_one(vis_com.model_dump_json())
+        print(f"inserted document: {result}")