Derive candidate fields from vision crop OCR

app/logic/vision_analysis.py

@@ -4,6 +4,7 @@ from pathlib import Path
 from typing import Any
 import hashlib
 import tempfile
+import re
 
 try:
     import fitz  # PyMuPDF
@@ -619,6 +620,82 @@ def classify_and_crop_unmatched_regions(
         "classified_regions": classified,
     }
 
+
+def build_vision_candidate_fields(classification: dict[str, Any]) -> list[dict[str, Any]]:
+    """
+    Convert crop OCR/classification results into lightweight structured field candidates.
+    """
+    fields: list[dict[str, Any]] = []
+    regions = classification.get("classified_regions") or []
+
+    money_re = re.compile(r"(?<!\d)(?:\$?\s*)\d+\.\d{2}[A-Z]?(?!\d)")
+    time_re = re.compile(r"\b\d{1,2}:\d{2}\s*(?:AM|PM)?\b", re.IGNORECASE)
+    item_count_re = re.compile(r"\b\d+\s+ITEMS?\b", re.IGNORECASE)
+
+    for idx, region in enumerate(regions):
+        text = str(region.get("ocr_text") or "").strip()
+        if not text:
+            continue
+
+        conf = region.get("ocr_confidence")
+        base = {
+            "source": "vision_crop_ocr",
+            "source_region_index": idx,
+            "source_bbox": region.get("bbox"),
+            "source_crop_path": region.get("crop_path"),
+            "ocr_confidence": conf,
+            "geometry_class": region.get("geometry_class"),
+        }
+
+        lower = text.lower()
+
+        if "cvs" in lower or "pharmacy" in lower:
+            fields.append({
+                **base,
+                "candidate_type": "merchant_or_header",
+                "value": text,
+                "confidence": 0.75 if (conf or 0) >= 70 else 0.45,
+            })
+
+        if time_re.search(text):
+            fields.append({
+                **base,
+                "candidate_type": "transaction_time",
+                "value": time_re.search(text).group(0),
+                "raw_text": text,
+                "confidence": 0.80 if (conf or 0) >= 70 else 0.50,
+            })
+
+        if item_count_re.search(text):
+            fields.append({
+                **base,
+                "candidate_type": "item_count",
+                "value": item_count_re.search(text).group(0).upper(),
+                "raw_text": text,
+                "confidence": 0.65 if (conf or 0) >= 50 else 0.40,
+            })
+
+        money_matches = money_re.findall(text)
+        if money_matches:
+            fields.append({
+                **base,
+                "candidate_type": "money_amounts",
+                "value": money_matches,
+                "raw_text": text,
+                "confidence": 0.65 if (conf or 0) >= 50 else 0.35,
+            })
+
+        # Capture low-value symbol/noise so later filtering can learn from it.
+        if len(text) <= 3 and not money_matches and not time_re.search(text):
+            fields.append({
+                **base,
+                "candidate_type": "symbol_or_noise",
+                "value": text,
+                "confidence": 0.20,
+            })
+
+    return fields
+
 def build_vision_assisted_layout(source_layout: dict[str, Any] | None, vision_result: dict[str, Any]) -> dict[str, Any]:
     """
     Convert vision analysis into normal layout_json.
@@ -638,6 +715,7 @@ def build_vision_assisted_layout(source_layout: dict[str, Any] | None, vision_re
         layout,
         region_score,
     )
+    candidate_fields = build_vision_candidate_fields(region_classification)
 
     layout["vision_assisted"] = True
     layout["vision_assisted_status"] = normalized_vision.get("status", "unknown")
@@ -646,6 +724,7 @@ def build_vision_assisted_layout(source_layout: dict[str, Any] | None, vision_re
     layout["vision_coordinate_normalization"] = normalized_vision.get("coordinate_normalization")
     layout["vision_region_score"] = region_score
     layout["vision_region_classification"] = region_classification
+    layout["vision_candidate_fields"] = candidate_fields
     layout["layout_sync_source"] = "vision_assisted"
     layout["layout_needs_review"] = True
     return layout