Update core/ai_engine.py

core/ai_engine.py

@@ -90,9 +90,7 @@ Classify the task across these dimensions:
 
 6. time_estimate (integer): Realistic minutes to complete.
 
-7. deadline_date (string or null): Extract YYYY-MM-DD if user mentions any deadline.
-   Today is {today}. Examples: "by Friday" → next Friday's date, "due March 1" → "2026-03-01".
-   Return null if no deadline is mentioned.
+7. deadline_date (string or null): If user says "by Friday", "due March 1", "before end of month", "deadline X" — extract as YYYY-MM-DD. Today is {TODAY}. Return null if no deadline mentioned.
 
 8. clarifications_needed (array of strings):
    If NOT confident about a dimension, add a short specific question.
@@ -101,8 +99,7 @@ Classify the task across these dimensions:
 STRICT RULES:
 - Return ONLY valid JSON. No markdown, no explanation.
 - Never guess if uncertain — ask a clarification question instead.
-- Always return all 8 fields.
-- ALWAYS extract deadline_date if user says "by X", "due X", "before X", "deadline X".
+- Always return all 8 fields including deadline_date (null if none).
 
 Example: {"title": "Finish project proposal", "life_area": "Work", "urgency": "Urgent", "importance": "Move the Needle", "state_of_mind": "Flow", "time_estimate": 90, "deadline_date": null, "clarifications_needed": []}"""
 
@@ -120,12 +117,11 @@ def parse_task_with_groq(raw_text: str, user_context: dict = None,
     if life_areas:
         context_hint += f"\nUser's life areas: {', '.join(life_areas)}"
 
-    today_str = date.today().isoformat()
-    system_prompt = TASK_CAPTURE_PROMPT.replace("{today}", today_str) + context_hint
+    _prompt = TASK_CAPTURE_PROMPT.replace("{TODAY}", str(date.today())) + context_hint
     response = _groq().chat.completions.create(
         model=GROQ_MODEL,
         messages=[
-            {"role": "system", "content": system_prompt},
+            {"role": "system", "content": _prompt},
             {"role": "user",   "content": f"Parse this task: {raw_text}"}
         ],
         max_tokens=512,