ruff format fixes

data_store/repositories/annotation_repository.py

@@ -60,17 +60,27 @@ class AnnotationRepository(MinioAdapter, AnnotationRepositoryInterface):
             annotation = Annotation(
                 angle=AngleEnum(data["angle"]) if data["angle"] is not None else None,
                 angle_uncertainty=bool(data["angle_uncertainty"]),
-                colour=ColourEnum(data["colour"]) if data["colour"] is not None else None,
+                colour=ColourEnum(data["colour"])
+                if data["colour"] is not None
+                else None,
                 colour_uncertainty=bool(data["colour_uncertainty"]),
-                contact=ContactEnum(data["contact"]) if data["contact"] is not None else None,
+                contact=ContactEnum(data["contact"])
+                if data["contact"] is not None
+                else None,
                 contact_uncertainty=bool(data["contact_uncertainty"]),
                 depth=DepthEnum(data["depth"]) if data["depth"] is not None else None,
                 depth_uncertainty=bool(data["depth_uncertainty"]),
-                distance=DistanceEnum(data["distance"]) if data["distance"] is not None else None,
+                distance=DistanceEnum(data["distance"])
+                if data["distance"] is not None
+                else None,
                 distance_uncertainty=bool(data["distance_uncertainty"]),
-                lighting=LightingEnum(data["lighting"]) if data["lighting"] is not None else None,
+                lighting=LightingEnum(data["lighting"])
+                if data["lighting"] is not None
+                else None,
                 lighting_uncertainty=bool(data["lighting_uncertainty"]),
-                point_of_view=PointOfViewEnum(data["point_of_view"]) if data["point_of_view"] is not None else None,
+                point_of_view=PointOfViewEnum(data["point_of_view"])
+                if data["point_of_view"] is not None
+                else None,
                 point_of_view_uncertainty=bool(data["point_of_view_uncertainty"]),
             )
         except (KeyError, ValueError) as e:

scripts/query_ai_model.py

@@ -58,7 +58,7 @@ def query_model_with_image(
     prompt: str,
     allowed_answers: list[str],
     image: Image.Image,
-    model: str = "llava:13b"
+    model: str = "llava:13b",
 ) -> str:
     """
     Query an Ollama instance with a prompt, image, and constrained answers.
@@ -77,9 +77,9 @@ def query_model_with_image(
     # Read environment variables
     ollama_endpoint = str(os.getenv("OLLAMA_ENDPOINT"))
     # Handle RGBA images by converting to RGB
-    if image.mode == 'RGBA':
+    if image.mode == "RGBA":
         # Convert RGBA to RGB by compositing against white background
-        rgb_image = Image.new('RGB', image.size, (255, 255, 255))
+        rgb_image = Image.new("RGB", image.size, (255, 255, 255))
         rgb_image.paste(image, mask=image.split()[-1])  # Use alpha as mask
         image = rgb_image
     # Convert PIL Image to base64
@@ -97,14 +97,12 @@ def query_model_with_image(
         "model": model,
         "prompt": prompt_with_options,
         "images": [image_b64],
-        "stream": False
+        "stream": False,
     }
     # Make request to Ollama
     # N.B. the generate endpoint does not retain context between calls
     response = requests.post(
-        f"{ollama_endpoint}/api/generate",
-        json=payload,
-        timeout=60
+        f"{ollama_endpoint}/api/generate", json=payload, timeout=60
     )
     response.raise_for_status()
     result = response.json()
@@ -197,11 +195,15 @@ def calculate_model_prompt_accuracy(
             expected_answer = getattr(job.annotation, category.lower())
             # Score the response
             score = score_response(response, expected_answer)
-            print(f"Job {job_id}: Response '{response}' - Expected answer '{expected_answer}' - Score: {score}")
+            print(
+                f"Job {job_id}: Response '{response}' - Expected answer '{expected_answer}' - Score: {score}"
+            )
             total_score += score
     # Calculate accuracy
     jobs_evaluated = total_jobs - jobs_skipped
-    print(f"Total Jobs: {total_jobs}, Jobs Skipped: {jobs_skipped}, Jobs Evaluated: {jobs_evaluated}")
+    print(
+        f"Total Jobs: {total_jobs}, Jobs Skipped: {jobs_skipped}, Jobs Evaluated: {jobs_evaluated}"
+    )
     if jobs_evaluated == 0:
         raise ValueError("No jobs were evaluated. All jobs were skipped.")
     accuracy = total_score / jobs_evaluated
@@ -237,17 +239,11 @@ def refine_prompt(
         "Respond with only the refined prompt."
     )
     # Prepare request payload
-    payload = {
-        "model": model,
-        "prompt": refinement_instruction,
-        "stream": False
-    }
+    payload = {"model": model, "prompt": refinement_instruction, "stream": False}
     # Make request to Ollama
     # N.B. the generate endpoint does not retain context between calls
     response = requests.post(
-        f"{ollama_endpoint}/api/generate",
-        json=payload,
-        timeout=60
+        f"{ollama_endpoint}/api/generate", json=payload, timeout=60
     )
     response.raise_for_status()
     result = response.json()
@@ -290,9 +286,13 @@ if __name__ == "__main__":
             print(f"Prompt accuracy for category '{category}': {accuracy:.2%}")
             # Stop if accuracy is 100%
             if accuracy == 1.0:
-                print(f"Achieved 100% accuracy for category '{category}'. Stopping refinement.")
+                print(
+                    f"Achieved 100% accuracy for category '{category}'. Stopping refinement."
+                )
                 break
-        print(f"Final prompt accuracy map for category '{category}': {prompt_accuracy_map}")
+        print(
+            f"Final prompt accuracy map for category '{category}': {prompt_accuracy_map}"
+        )
         category_events[category] = prompt_accuracy_map
     # save results to json file
     with open("prompt_accuracy_results.json", "w", encoding="utf-8") as f: