added from_annotations classmethod and fixed bug when generating webencoded image

src/database/classes.py

@@ -4,11 +4,12 @@ from PIL import Image
 from io import BytesIO
 from pathlib import Path
 from base64 import b64encode
-import random
 import logging
 from typing import List
 
-from src.model_experiential import ExperientialModelOutput
+from src.model_experiential import (
+    VisualSyntaxModelOutput
+)
 from src.model_interpersonal import (
     ContactModelOutput,
     AngleModelOutput,
@@ -25,7 +26,7 @@ from src.model_textual import (
 )
 
 class ModelOutputs(BaseModel):
-    experiential: ExperientialModelOutput
+    visual_syntax: VisualSyntaxModelOutput
     contact: ContactModelOutput
     angle: AngleModelOutput
     point_of_view: PointOfViewModelOutput
@@ -52,6 +53,37 @@ class ModelOutputs(BaseModel):
         }
         return cls(**kwargs)
     
+    @classmethod
+    def from_annotations(
+        cls,
+        visual_syntax: str,
+        contact: str,
+        angle: str,
+        point_of_view: str,
+        distance: str,
+        modality_lighting: str,
+        modality_color: str,
+        modality_depth: str,
+        information_value: str,
+        framing: str,
+        salience: str
+    ) -> 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)
+        }
+        return cls(**kwargs)
+
 
 class VisualCommunication(BaseModel):
     name: str
@@ -99,7 +131,7 @@ class VisualCommunication(BaseModel):
         buffer = BytesIO()
         self.image.save(buffer, format="png")
         img_enc = b64encode(buffer.getvalue()).decode("utf-8")
-        return img_enc
+        return f"data:image/png;base64, {img_enc}"
 
     def generate_random_prediction(self, force: bool = False) -> None:
         """Generate random prediction values."""