ThreatLevelD
Launch frontend demo: Implemented live MEC MVP demo with dynamic emotion processing
7b8b1dc
class FECController: | |
def __init__(self): | |
pass | |
def generate_prompt(self, final_uesp): | |
# Validate required keys | |
required_keys = [ | |
'emotion_family', | |
'primary_emotion_code', | |
'emotion_arc_trajectory', | |
'resonance_pattern' | |
] | |
for key in required_keys: | |
if key not in final_uesp: | |
raise KeyError(f"Missing required key: {key}") | |
# Map human-readable emotion label from code | |
emotion_code = final_uesp['primary_emotion_code'] | |
if emotion_code.startswith("VAR-"): | |
emotion_label = emotion_code.replace("VAR-", "").split('-')[0].title() | |
elif emotion_code.startswith("FAM-"): | |
emotion_label = emotion_code.replace("FAM-", "").title() | |
else: | |
emotion_label = "Unknown" | |
# Base fields | |
arc = final_uesp['emotion_arc_trajectory'] | |
resonance = final_uesp['resonance_pattern'] | |
# Optional fields with fallbacks | |
blend_states = final_uesp.get('blend_states', 'None detected') | |
response_strategy = final_uesp.get('response_strategy', 'RSM-DEFAULT') | |
tone_classification = final_uesp.get('tone_classification', 'TBD') | |
# Build Fusion Prompt | |
fusion_prompt = f""" | |
Contextual Emotional State: | |
- Primary Emotion: {emotion_label} | |
- Emotional Arc: {arc} | |
- Resonance: {resonance} | |
- Tone: {tone_classification} | |
- Blend States: {blend_states} | |
- Intervention Strategy: {response_strategy} | |
Empathic Objective: | |
Align your response tone and content with the above emotional context. Prioritize emotional authenticity, HEART-compliant care, and safety. Do not merely reflect emotion — respond in an emotionally aligned and supportive manner that honors the user’s lived experience. | |
HEART Compliance: Response must align with HEART™ ethical principles — transparency, alignment, contextuality, traceability, and emotional safety. | |
""".strip() | |
return fusion_prompt | |