"""
Nexari Context Engine (UPDATED)
Author: Piyush
Improvements:
 - Robust emotion pipeline usage & error handling
 - Safer fallback when model not available
 - Returns compact psychological profile instruction
"""

from transformers import pipeline

print(">>> Context: Loading Emotion Analysis Model...")
# load with top_k=1 by default
try:
    emotion_classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=1)
except Exception as e:
    print(f"Context: Failed to load emotion model: {e}")
    emotion_classifier = None

def _safe_emotion_analysis(text):
    if not emotion_classifier:
        return ("neutral", 0.0)
    try:
        # pipeline may return list of dicts or list-of-lists depending on version
        res = emotion_classifier(text)
        if isinstance(res, list) and len(res) > 0:
            # res could be [{'label':..., 'score':...}] or [['label',score],...]
            first = res[0]
            if isinstance(first, dict):
                return (first.get('label', 'neutral'), float(first.get('score', 0.0)))
            elif isinstance(first, list) and len(first) > 0 and isinstance(first[0], dict):
                return (first[0].get('label', 'neutral'), float(first[0].get('score', 0.0)))
        return ("neutral", 0.0)
    except Exception as e:
        print(f"Emotion analysis error: {e}")
        return ("neutral", 0.0)

def get_smart_context(user_text):
    """
    Analyzes the user's 'Vibe' and returns a short persona instruction.
    """
    try:
        label, confidence = _safe_emotion_analysis(user_text)
        top_emotion = label.lower()
        confidence = float(confidence)

        word_count = len(user_text.split()) if user_text else 0

        if word_count < 4:
            conversation_mode = "Ping-Pong Mode (Fast)"
        elif word_count < 20:
            conversation_mode = "Standard Chat Mode (Balanced)"
        else:
            conversation_mode = "Deep Dive Mode (Detailed)"

        if top_emotion == "joy":
            emotional_context = "User: Positive/Energetic. Vibe: Celebrate — be upbeat but concise."
        elif top_emotion == "sadness":
            emotional_context = "User: Low Energy. Vibe: Supportive — patient and gentle."
        elif top_emotion == "anger":
            emotional_context = "User: Frustrated. Vibe: De-escalate — calm, solution-first."
        elif top_emotion == "fear":
            emotional_context = "User: Anxious. Vibe: Reassure and clarify."
        elif top_emotion == "surprise":
            emotional_context = "User: Curious/Alert. Vibe: Engage and explain."
        else:
            emotional_context = "User: Neutral/Professional. Vibe: Helpful and efficient."

        return (
            f"\n[PSYCHOLOGICAL PROFILE]\n"
            f"1. Interaction Mode: {conversation_mode}\n"
            f"2. {emotional_context}\n"
            f"3. Directive: Mirror user's energy; keep follow-ups short and on-topic.\n"
        )
    except Exception as e:
        print(f"Context Error: {e}")
        return ""