Upload agents/utils.py with huggingface_hub

agents/utils.py

@@ -50,10 +50,11 @@ def compute_failure_prob(n_ran: int, n_total: int, n_insufficient: int = 0) -> f
     return max(0.0, 1.0 - n_effective / max(n_total, 1))
 
 
-def run_agent_tests(tests, img, agent_name):
+def run_agent_tests(tests, img, agent_name, modality_adjustments=None):
     """
     Shared test runner for all signal-processing agents.
     Handles: running tests, tagging insufficient-data, computing confidence properly.
+    Applies modality-aware score reweighting when modality_adjustments is provided.
     """
     findings, scores = [], []
     n_insufficient = 0
@@ -65,15 +66,29 @@ def run_agent_tests(tests, img, agent_name):
             
             sc = r.get("score", 0)
             note = r.get("note", "")
+            test_name = r.get("test", fn.__name__)
             
             # P7: Detect insufficient-data results — tag as not_applicable
             is_insufficient = (sc == 0.0 and any(kw in note.lower() for kw in 
-                ["insufficient", "too small", "no data", "not available", "few ", "no "]))
+                ["insufficient", "too small", "no data", "not available", "few ", "no ",
+                 "not meaningful", "cannot read", "parse error"]))
             
             if is_insufficient:
                 r["not_applicable"] = True
                 n_insufficient += 1
             
+            # Apply modality adjustment if present
+            if modality_adjustments and test_name in modality_adjustments:
+                multiplier = modality_adjustments[test_name]
+                original_score = sc
+                sc = sc * multiplier
+                r["score"] = sc
+                r["modality_adjusted"] = True
+                r["original_score"] = original_score
+                r["adjustment_multiplier"] = multiplier
+                if multiplier < 0.5:
+                    r["note"] = f"[Modality-suppressed ×{multiplier}] {note}"
+            
             scores.append(sc)
         except Exception as e:
             findings.append({"test": fn.__name__, "error": str(e), "score": 0})