fix the ruler offset

Author: kazmer97

lib/idp_common_pkg/idp_common/assessment/models.py

@@ -106,10 +106,15 @@ class BoundingBoxCoordinates(BaseModel):
 
     @classmethod
     def from_corners(
-        cls, x1: float, y1: float, x2: float, y2: float, scale: float = 1000.0
+        cls,
+        x1: float,
+        y1: float,
+        x2: float,
+        y2: float,
+        scale: float = 1000.0,
     ) -> "BoundingBoxCoordinates":
         """
-        Create from corner coordinates.
+        Create from corner coordinates in document space.
 
         Args:
             x1, y1: Top-left corner in 0-scale range
@@ -129,6 +134,12 @@ def from_corners(
         width = (x2 - x1) / scale
         height = (y2 - y1) / scale
 
+        # Clamp to valid range
+        left = min(max(left, 0.0), 1.0)
+        top = min(max(top, 0.0), 1.0)
+        width = min(width, 1.0 - left)
+        height = min(height, 1.0 - top)
+
         return cls(top=top, left=left, width=width, height=height)
 
 
@@ -201,7 +212,6 @@ class FieldAssessmentData(BaseModel):
     """
 
     confidence: float = Field(..., ge=0.0, le=1.0)
-    value: Any = Field(None, description="The extracted value")
     reasoning: str = Field(..., description="Confidence reasoning")
     confidence_threshold: float = Field(..., ge=0.0, le=1.0)
     geometry: list[Geometry] | None = Field(
@@ -213,7 +223,6 @@ class FieldAssessmentData(BaseModel):
     def from_llm_response(
         cls,
         confidence: float,
-        value: Any,
         reasoning: str,
         confidence_threshold: float,
         bbox_coords: list[float] | None = None,
@@ -227,18 +236,16 @@ def from_llm_response(
 
         return cls(
             confidence=confidence,
-            value=value,
             reasoning=reasoning,
             confidence_threshold=confidence_threshold,
             geometry=geometry,
         )
 
     def to_explainability_format(self) -> dict[str, Any]:
         """Convert to explainability_info format for frontend."""
-        result = {
+        result: dict[str, Any] = {
             "confidence": self.confidence,
-            "value": self.value,
-            "reasoning": self.reasoning,
+            "confidence_reason": self.reasoning,
             "confidence_threshold": self.confidence_threshold,
         }
 

lib/idp_common_pkg/idp_common/assessment/strands_models.py

@@ -40,7 +40,6 @@ def to_geometry(self) -> dict[str, Any]:
 class ConfidenceAssessment(BaseModel):
     """Confidence assessment for an attribute value."""
 
-    value: Any = Field(..., description="The extracted value")
     confidence: float = Field(..., ge=0.0, le=1.0, description="Confidence score 0-1")
     reasoning: str = Field(..., description="Explanation for the confidence score")
     bounding_box: BoundingBox | None = Field(

lib/idp_common_pkg/idp_common/assessment/strands_service.py

@@ -416,7 +416,6 @@ def _convert_to_assessment_result(
     # Create standardized field assessment data
     field_data = FieldAssessmentData.from_llm_response(
         confidence=assessment.confidence,
-        value=assessment.value,
         reasoning=assessment.reasoning,
         confidence_threshold=task.confidence_threshold,
         bbox_coords=(

lib/idp_common_pkg/idp_common/assessment/strands_tools.py

@@ -115,13 +115,12 @@ def view_image(input_data: ViewImageInput, agent: Agent) -> dict:
                 "color": "red",
             }
 
-            # Draw the bounding box on the image (which already has ruler)
+            # Draw the bounding box on the image (which has 30px margin for ruler)
             # Let drawing errors propagate - if we can't draw, something is wrong
             img_bytes = draw_bounding_boxes(
                 img_bytes,
                 [bbox_dict],
-                has_ruler=True,
-                ruler_width=30,
+                margin_offset=30,
             )
 
             logger.debug(

lib/idp_common_pkg/idp_common/config/models.py

@@ -140,7 +140,6 @@ def set_default_review_agent_model(self) -> Self:
 
         return self
 
-
     @model_validator(mode="after")
     def set_default_review_agent_model(self) -> Self:
         """Set review_agent_model to extraction model if not specified."""
@@ -206,7 +205,8 @@ class AssessmentConfig(BaseModel):
 
     enabled: bool = Field(default=True, description="Enable assessment")
     model: Optional[str] = Field(
-        default=None, description="Bedrock model ID for assessment"
+        default="us.anthropic.claude-haiku-4-5-20251001-v1:0",
+        description="Bedrock model ID for assessment",
     )
     system_prompt: str = Field(
         default="You are a document analysis assessment expert. Your role is to evaluate the confidence and accuracy of data extraction results by analyzing them against source documents.\n\nProvide accurate confidence scores for each assessment.",

lib/idp_common_pkg/idp_common/utils/grid_overlay.py

@@ -147,8 +147,7 @@ def add_ruler_edges(
 def draw_bounding_boxes(
     image_data: bytes,
     bboxes: list[dict],
-    has_ruler: bool = False,
-    ruler_width: int = 30,
+    margin_offset: int = 0,
     box_color: str = "red",
     box_width: int = 3,
     label_font_size: int = 12,
@@ -160,12 +159,11 @@ def draw_bounding_boxes(
     Args:
         image_data: Raw image bytes
         bboxes: List of bounding box dictionaries, each containing:
-            - 'bbox': [x1, y1, x2, y2] in 0-1000 normalized scale
+            - 'bbox': [x1, y1, x2, y2] in 0-1000 normalized scale (document space)
             - 'label': Optional label text for the box
             - 'color': Optional color override for this box
             - 'page': Optional page number (for multi-page docs)
-        has_ruler: If True, account for ruler margins in coordinate calculation
-        ruler_width: Width of ruler margin (only used if has_ruler=True)
+        margin_offset: Pixel offset for top-left margin (e.g., if image has decorative margins)
         box_color: Default color for bounding boxes
         box_width: Line width for bounding boxes
         label_font_size: Font size for box labels
@@ -191,17 +189,11 @@ def draw_bounding_boxes(
     image = Image.open(io.BytesIO(image_data)).convert("RGBA")
     width, height = image.size
 
-    # If image has ruler edges, calculate the actual document area
-    if has_ruler:
-        doc_width = width - ruler_width
-        doc_height = height - ruler_width
-        offset_x = ruler_width
-        offset_y = ruler_width
-    else:
-        doc_width = width
-        doc_height = height
-        offset_x = 0
-        offset_y = 0
+    # Calculate document area (excluding any margin offset)
+    doc_width = width - margin_offset
+    doc_height = height - margin_offset
+    offset_x = margin_offset
+    offset_y = margin_offset
 
     # Create overlay for semi-transparent boxes
     overlay = Image.new("RGBA", (width, height), (0, 0, 0, 0))
@@ -331,12 +323,11 @@ def add_ruler_and_draw_boxes(
         label_interval=label_interval,
     )
 
-    # Then draw bounding boxes (accounting for ruler offset)
+    # Then draw bounding boxes (accounting for margin offset from ruler)
     result = draw_bounding_boxes(
         image_with_ruler,
         bboxes,
-        has_ruler=True,
-        ruler_width=ruler_width,
+        margin_offset=ruler_width,
         box_color=box_color,
         box_width=box_width,
     )

src/ui/src/components/common/confidence-alerts-utils.js

@@ -297,6 +297,7 @@ export const getFieldConfidenceInfo = (fieldName, explainabilityInfo, path = [],
           // nosemgrep: javascript.lang.security.audit.prototype-pollution.prototype-pollution-loop - Controlled data source, input validation performed upstream
           currentExplainabilityData = currentExplainabilityData[index];
         } else {
+          console.log('getFieldConfidenceInfo: Array index out of bounds or invalid', { pathSegment, index, arrayLength: currentExplainabilityData.length });
           return { hasConfidenceInfo: false };
         }
       } else {
@@ -305,16 +306,19 @@ export const getFieldConfidenceInfo = (fieldName, explainabilityInfo, path = [],
         currentExplainabilityData = currentExplainabilityData[pathSegment];
       }
     } else {
+      console.log('getFieldConfidenceInfo: Path traversal failed', { pathSegment, currentExplainabilityData });
       return { hasConfidenceInfo: false };
     }
   }
 
   // Now look for the field in the current explainability data location
   if (!currentExplainabilityData || typeof currentExplainabilityData !== 'object') {
+    console.log('getFieldConfidenceInfo: currentExplainabilityData not valid after path traversal', { fieldName, path, currentExplainabilityData });
     return { hasConfidenceInfo: false };
   }
 
   const fieldData = currentExplainabilityData[fieldName];
+  console.log('getFieldConfidenceInfo: Looking up field', { fieldName, path, fieldData, currentKeys: Object.keys(currentExplainabilityData) });
   if (!fieldData || typeof fieldData !== 'object') {
     return { hasConfidenceInfo: false };
   }

src/ui/src/components/document-viewer/JSONViewer.jsx

@@ -133,6 +133,18 @@ const FormEditorView = ({ jsonData, onChange, isReadOnly, sectionData }) => {
       ? { hasConfidenceInfo: false } // Array indices don't have confidence, their contents do
       : getFieldConfidenceInfo(cleanKey, explainabilityInfo, filteredParentPath);
 
+    // Debug logging for nested field confidence (groups and list items)
+    if (!isArrayIndex && filteredParentPath.length > 0) {
+      console.log('Nested field confidence lookup:', {
+        fieldName: cleanKey,
+        parentPath,
+        filteredParentPath,
+        hasExplainabilityInfo: !!explainabilityInfo,
+        explainabilityInfoKeys: explainabilityInfo ? Object.keys(Array.isArray(explainabilityInfo) ? explainabilityInfo[0] || {} : explainabilityInfo) : [],
+        confidenceInfo,
+      });
+    }
+
     // Check if this field should be highlighted due to low confidence (legacy alert-based highlighting)
     const confidenceThresholdAlerts = sectionData?.ConfidenceThresholdAlerts || [];
     const highlightInfo = getFieldHighlightInfo(key, value, confidenceThresholdAlerts);