optimized prompt

app.py

@@ -381,6 +381,67 @@ def _generate_plan(items: List[Challenge], keyword: str, min_prize: float, days_
         return "", "\n".join(logs)
 
 
+def _generate_plan_fixed(
+    ranked: List[tuple[Challenge, float, str]],
+    keyword: str,
+    days_ahead: int,
+    debug: bool = False,
+) -> Tuple[str, str]:
+    """Generate a plan using the already-ranked top picks without changing order.
+
+    The LLM is only used to phrase reasons and the action plan. It must not re-rank.
+    """
+    top = ranked[:3]
+    data = [
+        {
+            "title": c.title,
+            "prize": c.prize,
+            "deadline": c.deadline,
+            "tags": c.tags[:6],
+            "score": round(s, 4),
+            "reason": (r or "").strip()[:300],
+        }
+        for c, s, r in top
+    ]
+
+    logs: List[str] = []
+    api_key = os.getenv("OPENAI_API_KEY")
+    base_url = os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1")
+    if not api_key:
+        logs.append("Missing OPENAI_API_KEY")
+        return "", "\n".join(logs)
+
+    try:
+        client = OpenAI(api_key=api_key, base_url=base_url)
+        prompt = (
+            "You are a concise challenge scout. You are given pre-ranked top picks (in order).\n"
+            "Do NOT change the order or add/remove items.\n"
+            "Output exactly:\n"
+            "- Top 3 picks (title + short reason).\n"
+            "- Quick plan of action (3 bullets).\n"
+            f"Constraints: query='{keyword}', within {days_ahead} days.\n"
+            f"Ranked data: {json.dumps(data)}"
+        )
+        if debug:
+            logs.append(f"PLAN(FIXED) prompt: {prompt[:1200]}")
+        resp = client.chat.completions.create(
+            model=os.getenv("OPENAI_MODEL", "gpt-4o-mini"),
+            messages=[
+                {"role": "system", "content": "Be terse. Do not re-rank or add items."},
+                {"role": "user", "content": prompt},
+            ],
+            temperature=0.3,
+            timeout=20,
+        )
+        text = (resp.choices[0].message.content or "").strip()
+        if debug:
+            logs.append(f"PLAN(FIXED) output: {text[:800]}")
+        return text, "\n".join(logs)
+    except Exception as e:
+        if debug:
+            logs.append(f"PLAN(FIXED) error: {e}")
+        return "", "\n".join(logs)
+
 def _score_items(items: List[Challenge], keyword: str, debug: bool = False) -> Tuple[List[tuple[Challenge, float, str]], str]:
     """Score challenges using OpenAI API and return (challenge, score, reason) tuples"""
     api_key = os.getenv("OPENAI_API_KEY")
@@ -598,7 +659,7 @@ def run_query(requirements: str):
     if not filtered and items:
         filtered = items
         status = f"{status} (no matches; showing unfiltered)"
-    plan_text, _ = _generate_plan(filtered, keyword, min_prize, days_ahead, debug=False) if filtered else ("", "")
+    plan_text, _ = _generate_plan_fixed(scored, keyword, days_ahead, debug=False) if ok and scored else ("", "")
 
     # Build a map from id to (score, reason) for star display
     id_to_score_reason: dict[str, tuple[float, str]] = {}