src/database/classes.py

@@ -25,6 +25,11 @@ from src.model_textual import (
     SalienceModelOutput
 )
 
+
+class NoDocumentFoundException(Exception):
+    pass
+
+
 class ModelOutputs(BaseModel):
     visual_syntax: VisualSyntaxModelOutput
     contact: ContactModelOutput
@@ -52,7 +57,7 @@ class ModelOutputs(BaseModel):
             in cls.model_fields.items()
         }
         return cls(**kwargs)
-    
+
     @classmethod
     def from_annotations(
         cls,
@@ -70,17 +75,28 @@ class ModelOutputs(BaseModel):
     ) -> ModelOutputs:
         """Instantiate from annotation."""
         kwargs = {
-            "visual_syntax": VisualSyntaxModelOutput.from_choice(visual_syntax),
-            "contact": ContactModelOutput.from_choice(contact),
-            "angle": AngleModelOutput.from_choice(angle),
-            "point_of_view": PointOfViewModelOutput.from_choice(point_of_view),
-            "distance": DistanceModelOutput.from_choice(distance),
-            "modality_lighting": ModalityLightingModelOutput.from_choice(modality_lighting),
-            "modality_color": ModalityColorModelOutput.from_choice(modality_color),
-            "modality_depth": ModalityDepthModelOutput.from_choice(modality_depth),
-            "information_value": InformationValueModelOutput.from_choice(information_value),
-            "framing": FramingModelOutput.from_choice(framing),
-            "salience": SalienceModelOutput.from_choice(salience)
+            "visual_syntax": VisualSyntaxModelOutput
+            .from_choice(visual_syntax),
+            "contact": ContactModelOutput
+            .from_choice(contact),
+            "angle": AngleModelOutput
+            .from_choice(angle),
+            "point_of_view": PointOfViewModelOutput
+            .from_choice(point_of_view),
+            "distance": DistanceModelOutput
+            .from_choice(distance),
+            "modality_lighting": ModalityLightingModelOutput
+            .from_choice(modality_lighting),
+            "modality_color": ModalityColorModelOutput
+            .from_choice(modality_color),
+            "modality_depth": ModalityDepthModelOutput
+            .from_choice(modality_depth),
+            "information_value": InformationValueModelOutput
+            .from_choice(information_value),
+            "framing": FramingModelOutput
+            .from_choice(framing),
+            "salience": SalienceModelOutput
+            .from_choice(salience)
         }
         return cls(**kwargs)
 
@@ -115,7 +131,10 @@ class VisualCommunication(BaseModel):
 
     @field_validator("image", mode="before")
     @classmethod
-    def convert_to_image(cls, image: Image.Image | BytesIO | bytes) -> Image.Image:
+    def convert_to_image(
+        cls,
+        image: Image.Image | BytesIO | bytes
+    ) -> Image.Image:
         if isinstance(image, bytes):
             image = BytesIO(image)
         if isinstance(image, BytesIO):
@@ -124,7 +143,7 @@ class VisualCommunication(BaseModel):
 
     def __repr__(self) -> str:
         return f"{self.classname()}(name='{self.name}')"
-    
+
     def webencoded_image(self) -> str:
         """Convert image to be displayed on webpage."""
         # convert images to bytes string
@@ -138,6 +157,3 @@ class VisualCommunication(BaseModel):
         if not force and self.prediction is not None:
             logging.warning("set force=True to overwrite existing values.")
         self.prediction = ModelOutputs.from_random()
-
-class NoDocumentFoundException(Exception):
-    pass

src/database/utils.py

@@ -20,15 +20,17 @@ def total_annotated(
 ) -> int:
     """Get total number of annotated documents in database."""
     query = {
-        "annotation": { "$ne": None }
+        "annotation": {
+            "$ne": None
+        }
     }
     return collection.count_documents(filter=query)
 
 
 def get_visual_communication(
-        collection: Collection,
-        with_annotation: bool = False
-    ) -> VisualCommunication:
+    collection: Collection,
+    with_annotation: bool = False
+) -> VisualCommunication:
     """Get a random visual communication from the database."""
     query = {}
     if with_annotation:
@@ -36,8 +38,14 @@ def get_visual_communication(
     else:
         query["annotation"] = None
     data = collection.aggregate([
-        { "$match": query },   # find using filters
-        { "$sample": { "size": 1 } }    # get one random
+        {
+            "$match": query     # find using filters
+        },
+        {
+            "$sample": {
+                "size": 1   # get one random
+            }
+        }
     ])
     data = list(data)   # read data from cursor object
     if len(data) == 0:
@@ -58,7 +66,9 @@ def upsert_predictions(
         "name": vis_com_name
     }
     update = {
-        "$set": { "prediction": predictions.model_dump() }
+        "$set": {
+            "prediction": predictions.model_dump()
+        }
     }
     res = collection.update_one(
         filter=query,
@@ -79,7 +89,9 @@ def upsert_annotations(
         "name": vis_com_name
     }
     update = {
-        "$set": { "annotation": annotations.model_dump() }
+        "$set": {
+            "annotation": annotations.model_dump()
+        }
     }
     res = collection.update_one(
         filter=query,

src/main.py

@@ -1,9 +1,10 @@
 import logging
 from dotenv import load_dotenv
 from pathlib import Path
-import logging
 import os
 
+from src.web import app
+
 # prepare optional local setup
 env_path = Path(__file__).parent.parent / "local.env"
 load_dotenv(env_path)
@@ -38,8 +39,6 @@ datefmt = '%Y-%m-%d %H:%M:%S'
 logging.basicConfig(format=fmt, datefmt=datefmt, level=logging.INFO)
 
 logging.info("initialized app")
-
-from src.web import app
 server = app.server
 
 if __name__ == "__main__":
@@ -47,4 +46,4 @@ if __name__ == "__main__":
     os.environ["MONGO_HOST"] = "localhost"
     # run app
     app.run(debug=True)
-    logging.info("started app")
+    logging.info("started app")

src/model_experiential/__init__.py

@@ -1,24 +1 @@
 from .classes import VisualSyntaxModelOutput
-
-
-
-# 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

@@ -6,6 +6,7 @@ import random
 class OptionNotSetException(Exception):
     pass
 
+
 class ModelOutput(BaseModel):
 
     @classmethod
@@ -17,13 +18,13 @@ class ModelOutput(BaseModel):
     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."""
@@ -31,7 +32,8 @@ class ModelOutput(BaseModel):
             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}"
+        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)
@@ -39,15 +41,19 @@ class ModelOutput(BaseModel):
     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 += ", ".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()

src/model_interpersonal/__init__.py

@@ -6,4 +6,4 @@ from .classes import (
     ModalityLightingModelOutput,
     ModalityColorModelOutput,
     ModalityDepthModelOutput
-)
+)

src/model_interpersonal/classes.py

@@ -14,13 +14,13 @@ class ModelOutput(BaseModel):
     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."""
@@ -28,7 +28,8 @@ class ModelOutput(BaseModel):
             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}"
+        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)
@@ -36,15 +37,19 @@ class ModelOutput(BaseModel):
     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 += ", ".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()

src/model_textual/__init__.py

@@ -2,4 +2,4 @@ from .classes import (
     InformationValueModelOutput,
     FramingModelOutput,
     SalienceModelOutput
-)
+)

src/model_textual/classes.py

@@ -14,13 +14,13 @@ class ModelOutput(BaseModel):
     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."""
@@ -28,7 +28,8 @@ class ModelOutput(BaseModel):
             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}"
+        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)
@@ -36,15 +37,19 @@ class ModelOutput(BaseModel):
     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 += ", ".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()
@@ -84,4 +89,4 @@ if __name__ == '__main__':
     m = SalienceModelOutput.from_random()
     print(repr(m))
     print(m.highest_score_field())
-    print(m.highest_score_value())
+    print(m.highest_score_value())

src/web/app.py

@@ -30,6 +30,7 @@ BasicAuth(app, AUTH_DICT)
 # connect to database
 collection, db, client = connect()
 
+
 # define callbacks
 @app.callback(
     Output("alert-element", "is_open"),
@@ -44,6 +45,7 @@ def show_alert(
     logging.info(f"updated alert message: {msg}")
     return True, msg
 
+
 @app.callback(
     Output("alert-message", "data"),
     Output("vis-com-name", "data"),
@@ -73,7 +75,7 @@ def cycle_visual_communication_data(
         annotation_values
     ]
     # check if next-button clicked
-    if n_clicks  == 0:
+    if n_clicks == 0:
         logging.info("stopping early: next-button has not yet been clicked")
         return response
     # check if visual communication name is set
@@ -102,13 +104,13 @@ def cycle_visual_communication_data(
                 in annotation_values
             ]
             annotations = {
-                key: value 
-                for key, value 
+                key: value
+                for key, value
                 in zip(annotation_keys, annotation_values)
             }
             # instantiate ModelOutputs object
             annotations = ModelOutputs.from_annotations(**annotations)
-            # save data to 
+            # save data to database
             upsert_annotations(
                 collection=collection,
                 vis_com_name=vis_com_name,
@@ -137,7 +139,7 @@ def cycle_visual_communication_data(
             # reset annotations
             annotation_values = [None for elem in annotation_values]
     except NoDocumentFoundException:
-        msg = f"no unannotated data in database"
+        msg = "no unannotated data in database"
         logging.warning(msg)
         response[0] = msg
         return tuple(response)

src/web/layout/__init__.py

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

src/web/layout/alerts.py

@@ -1,4 +1,4 @@
-from dash import html, dcc
+from dash import html
 import dash_bootstrap_components as dbc
 
 

src/web/layout/body.py

@@ -1,6 +1,4 @@
 import dash_mantine_components as dmc
-from dash import dcc, html
-from typing import List
 
 from .image import image_element
 from .inputs import inputs_element
@@ -17,4 +15,4 @@ body_element = dmc.Container(
             ],
         )
     ],
-)
+)

src/web/layout/inputs.py

@@ -20,4 +20,4 @@ inputs_element = dmc.SimpleGrid(
         labels_element,
         next_button
     ]
-)
+)

src/web/layout/labels.py

@@ -20,6 +20,7 @@ from src.model_textual import (
     SalienceModelOutput
 )
 
+
 def generate_option_labels(model) -> List[str]:
     """Generate presentable list of attributes from an OutputModel."""
     labels = [
@@ -28,35 +29,51 @@ def generate_option_labels(model) -> List[str]:
     ]
     return labels
 
+
 def generate_visual_syntax_options_map():
     """Generate map of titles and options for visual syntax labels."""
     options_map = {}
     # add experiential labels
-    options_map["visual syntax"] = generate_option_labels(VisualSyntaxModelOutput)
+    options_map["visual syntax"] = generate_option_labels(
+        VisualSyntaxModelOutput
+    )
     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"] = generate_option_labels(ContactModelOutput)
     options_map["angle"] = generate_option_labels(AngleModelOutput)
-    options_map["point of view"] = generate_option_labels(PointOfViewModelOutput)
+    options_map["point of view"] = generate_option_labels(
+        PointOfViewModelOutput
+    )
     options_map["distance"] = generate_option_labels(DistanceModelOutput)
-    options_map["modality lighting"] = generate_option_labels(ModalityLightingModelOutput)
-    options_map["modality color"] = generate_option_labels(ModalityColorModelOutput)
-    options_map["modality depth"] = generate_option_labels(ModalityDepthModelOutput)
+    options_map["modality lighting"] = generate_option_labels(
+        ModalityLightingModelOutput
+    )
+    options_map["modality color"] = generate_option_labels(
+        ModalityColorModelOutput
+    )
+    options_map["modality depth"] = 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"] = generate_option_labels(InformationValueModelOutput)
+    options_map["information value"] = generate_option_labels(
+        InformationValueModelOutput
+    )
     options_map["framing"] = generate_option_labels(FramingModelOutput)
     options_map["salience"] = generate_option_labels(SalienceModelOutput)
     return options_map
 
+
 # prepare experiential container
 experiential_map = generate_visual_syntax_options_map()
 experiential_container = dmc.Col(
@@ -118,4 +135,4 @@ labels_element = dmc.Grid(
         interpersonal_container,
         textual_container,
     ]
-)
+)

src/web/layout/layout.py

@@ -1,4 +1,3 @@
-from dash import dcc
 import dash_mantine_components as dmc
 
 from .stores import stores_element

src/web/layout/stores.py

@@ -5,12 +5,14 @@ import os
 storage_type = "session"
 if "ENV" in os.environ and os.getenv("ENV") == "DEV":
     storage_type = "memory"
-    logging.info(f"ENV=DEV -> dcc.Stores changed to storage_type={storage_type}")
-    
+    logging.info(
+        "ENV=DEV -> dcc.Stores changed to storage_type=%s",
+        storage_type
+    )
 
 stores_element = html.Div(
     children=[
         dcc.Store(id="alert-message", storage_type=storage_type, data=""),
         dcc.Store(id="vis-com-name", storage_type=storage_type, data=""),
     ]
-)
+)

tests/test_generate_random_prediction.py

@@ -10,8 +10,12 @@ if __name__ == "__main__":
     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]
+    vis_com_list = [
+        VisualCommunication.from_file(path)
+        for path
+        in img_path_list
+    ]
     # generate random predictions
     [vis_com.generate_random_prediction() for vis_com in vis_com_list]
     for vis_com in vis_com_list:
-        print(vis_com)
+        print(vis_com)

tests/test_image_upload.py

@@ -12,7 +12,11 @@ if __name__ == "__main__":
     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]
+    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

tests/test_model_outputs_from_annotation.py

@@ -3,11 +3,12 @@ from src.database import ModelOutputs
 
 if __name__ == "__main__":
     # instantiate data object
-    annotation = {
-        
-    }
-    vis_com_list = [ModelOutputs.from_annotation(path) for path in img_path_list]
+    vis_com_list = [
+        ModelOutputs.from_random()
+        for i
+        in range(3)
+    ]
     # generate random predictions
     [vis_com.generate_random_prediction() for vis_com in vis_com_list]
     for vis_com in vis_com_list:
-        print(vis_com)
+        print(vis_com)

tests/test_prediction_upload.py

@@ -25,7 +25,11 @@ if __name__ == "__main__":
     img_dir = test_dir / "imgs"
     img_path_list = [path for path in img_dir.glob("*.jpeg") if path.is_file()]
     # instantiate data object
-    vis_com_list = [VisualCommunication.from_file(path) for path in img_path_list]
+    vis_com_list = [
+        VisualCommunication.from_file(path)
+        for path
+        in img_path_list
+    ]
     # generate random predictions
     [vis_com.generate_random_prediction() for vis_com in vis_com_list]
     # prepare env vars

tests/test_total_annotated.py

@@ -17,4 +17,4 @@ if __name__ == "__main__":
     collection, db, client = connect()
     # get visual communication
     num_docs = total_annotated(collection)
-    print(f"number of annotated  documents in database: {num_docs}")
+    print(f"number of annotated  documents in database: {num_docs}")

tests/test_total_documents.py

@@ -17,4 +17,4 @@ if __name__ == "__main__":
     collection, db, client = connect()
     # get visual communication
     num_docs = total_documents(collection)
-    print(f"total number of documents in database: {num_docs}")
+    print(f"total number of documents in database: {num_docs}")