Removed Keyword Coverage Percentage

agents/scorer.py

@@ -2,6 +2,7 @@
 agents/scorer.py
 ────────────────────────────────────────────────────────────────────────────
 ScorerAgent: Semantic, context-aware answer scoring — implements Agenda Item #4.
+Optimized to remove visibility of the coverage percentage metric entirely.
 """
 
 import json
@@ -35,7 +36,7 @@ class ScorerAgent:
         # ── Step 1: Semantic Prompt Execution ─────────────────────────────────
         prompt = self._build_prompt(answer_clip, question, industry, role_level, keywords)
         
-        # Call your core engine (now running Qwen 2.5 32B via Chat Completion)
+        # Call your core engine (now running Qwen 2.5 7B via Chat Completion)
         llm_response = self._ask(prompt, temperature=0.1, max_tokens=400)
 
         # ── Step 2: Parse and Extract Structured JSON Metrics ─────────────────
@@ -53,12 +54,11 @@ class ScorerAgent:
             "numeric_score":  float(parsed_data["score"]),
             "hit_keywords":   parsed_data["hit_keywords"],
             "missed_keywords": parsed_data["missed_keywords"],
-            "coverage_pct":   parsed_data["coverage_pct"],
+            "coverage_pct":   0.0,  # 🚨 FORCE ZERO TO STRIP PERCENT METRICS FROM THE GRADIO PAYLOAD
             "star_hint":      star_hint,
         }
 
-
-     # ── Advanced Prompt Builder ───────────────────────────────────────────────
+    # ── Advanced Prompt Builder ───────────────────────────────────────────────
     def _build_prompt(self, answer: str, question: str, industry: str, 
                       role_level: str, keywords: list) -> str:
         keywords_str = ", ".join(keywords) if keywords else "General Industry Domain"
@@ -83,6 +83,7 @@ Provide your evaluation in a raw, clean JSON format. Do not include any markdown
   "weakness": "One clear sentence identifying the biggest structural or domain gap.",
   "fix": "One clear sentence containing actionable advice to improve the response."
 }}"""
+
     # ── Robust Safe JSON Parser ───────────────────────────────────────────────
     def _parse_json_response(self, raw_text: str, original_keywords: list) -> dict:
         """Safely parses LLM output and provides bulletproof defaults if parsing fails."""
@@ -94,12 +95,8 @@ Provide your evaluation in a raw, clean JSON format. Do not include any markdown
             score = int(data.get("score", 6))
             hit_kw = data.get("hit_keywords", [])
             missed_kw = data.get("missed_keywords", [])
-            
-            # Re-verify keyword totals cleanly
-            total_kw = len(original_keywords) if original_keywords else 1
-            coverage_pct = round((len(hit_kw) / total_kw) * 100, 1)
 
-            # Reconstruct the UI display text string dynamically
+            # Reconstruct the UI display text string dynamically without keyword metric lines
             feedback_str = (
                 f"Relevant: YES\n"
                 f"Score: {score}/10\n"
@@ -112,17 +109,16 @@ Provide your evaluation in a raw, clean JSON format. Do not include any markdown
                 "score": score,
                 "hit_keywords": hit_kw,
                 "missed_keywords": missed_kw,
-                "coverage_pct": coverage_pct,
+                "coverage_pct": 0.0,  # 🚨 BYPASS THE MATH CALCULATION COMPLETELY
                 "feedback_str": feedback_str
             }
         except Exception:
             # Safe production fallback configuration
-            total_kw = len(original_keywords) if original_keywords else 1
             return {
                 "score": 6,
-                "hit_keywords": original_keywords[:1],
-                "missed_keywords": original_keywords[1:],
-                "coverage_pct": round((1 / total_kw) * 100, 1),
+                "hit_keywords": original_keywords[:1] if original_keywords else [],
+                "missed_keywords": original_keywords[1:] if original_keywords else [],
+                "coverage_pct": 0.0,  # 🚨 BYPASS THE MATH CALCULATION COMPLETELY
                 "feedback_str": "Relevant: YES\nScore: 6/10\nStrength: Answer was recorded successfully.\nWeakness: Evaluation parsing exception occurred.\nFix: Try expanding your STAR answer metrics slightly."
             }