Create reviewer_agent.py

agents/reviewer_agent.py

@@ -0,0 +1,134 @@
+from typing import Dict, Any
+
+
+class ReviewerAgent:
+    """
+    Reviewer Agent
+    - Collects outputs from Grammar, Style, and Clarity agents
+    - Combines their confidence, number of changes, and readability
+    - Selects the best final output
+    - Returns a full explanation + candidate comparison
+    """
+
+    def __init__(self):
+        pass
+
+    @staticmethod
+    def _length_score(text: str) -> float:
+        """
+        Simple readability heuristic based on text length.
+        Penalizes texts that are too short or too long.
+        """
+        words = text.split()
+        n = len(words)
+
+        if n == 0:
+            return 0.0
+
+        # Ideal range: 8–40 words
+        if n < 8:
+            return 0.4
+        if n > 40:
+            return 0.6
+
+        return 1.0
+
+    def review(
+        self,
+        original: str,
+        grammar_result: Dict[str, Any],
+        style_result: Dict[str, Any],
+        clarity_result: Dict[str, Any],
+    ) -> Dict[str, Any]:
+        """
+        Decide which version is best and explain the decision.
+        Returns:
+        {
+            "final_text": ...,
+            "candidates": [...],
+            "decision_explanation": "..."
+        }
+        """
+
+        candidates = []
+
+        # -----------------------
+        # 1) Original Input
+        # -----------------------
+        orig_score = 0.3
+        candidates.append({
+            "name": "original",
+            "text": original,
+            "score": round(orig_score, 3),
+            "notes": "Raw user input; no improvements applied."
+        })
+
+        # -----------------------
+        # 2) Grammar Agent Output
+        # -----------------------
+        g_text = grammar_result["corrected"]
+        g_changes = len(grammar_result.get("changes", []))
+        g_conf = grammar_result.get("confidence", 0.7)
+
+        g_score = (0.4 * g_conf) + (0.3 + min(g_changes, 5) * 0.05)
+        g_score *= self._length_score(g_text)
+
+        candidates.append({
+            "name": "grammar",
+            "text": g_text,
+            "score": round(g_score, 3),
+            "notes": f"Grammar agent fixed {g_changes} issue(s)."
+        })
+
+        # -----------------------
+        # 3) Style Agent Output
+        # -----------------------
+        s_text = style_result["styled"]
+        s_changes = len(style_result.get("changes", []))
+        s_conf = style_result.get("confidence", 0.75)
+
+        s_score = (0.4 * s_conf) + (0.35 + min(s_changes, 5) * 0.04)
+        s_score *= self._length_score(s_text)
+
+        candidates.append({
+            "name": "style",
+            "text": s_text,
+            "score": round(s_score, 3),
+            "notes": f"Style agent applied {s_changes} improvements."
+        })
+
+        # -----------------------
+        # 4) Clarity Agent Output
+        # -----------------------
+        c_text = clarity_result["clarified"]
+        c_changes = len(clarity_result.get("changes", []))
+        c_conf = clarity_result.get("confidence", 0.8)
+
+        c_score = (0.4 * c_conf) + (0.4 + min(c_changes, 5) * 0.03)
+        c_score *= self._length_score(c_text)
+
+        candidates.append({
+            "name": "clarity",
+            "text": c_text,
+            "score": round(c_score, 3),
+            "notes": f"Clarity agent rewrote {c_changes} part(s) for readability."
+        })
+
+        # -----------------------
+        # Decide the winner
+        # -----------------------
+        best = max(candidates, key=lambda c: c["score"])
+
+        explanation = (
+            f"The reviewer selected the final output from the '{best['name']}' agent "
+            f"because it achieved the highest overall quality score ({best['score']}). "
+            "Scores are based on: (1) the agent’s confidence, (2) number of improvements, "
+            "(3) readability via length heuristic. This ensures the final text is "
+            "clear, professional, and client-ready."
+        )
+
+        return {
+            "final_text": best["text"],
+            "candidates": candidates,
+            "decision_explanation": explanation
+        }