File size: 3,795 Bytes
e2b61fb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | import os
import json
import random
# Tentukan path file JSON secara global agar bisa dibaca dan ditulis ulang
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
JSON_PATH = os.path.join(BASE_DIR, "trained_mdp_policy.json")
def load_policy():
with open(JSON_PATH, "r") as file:
return json.load(file)
def save_policy(policy_data):
with open(JSON_PATH, "w") as file:
json.dump(policy_data, file, indent=2)
# Load policy ke dalam memori aplikasi
policy = load_policy()
def generate_state_id(stress: str, prod_hours: float, sleep: float, screen: float, physical: float) -> str:
if prod_hours < 35.0: state_prod = "LOW_PROD"
elif 35.0 <= prod_hours <= 45.0: state_prod = "NORMAL_PROD"
else: state_prod = "HIGH_PROD"
if sleep < 5.8: state_sleep = "LACK_SLEEP"
elif 5.8 <= sleep <= 7.5: state_sleep = "ENOUGH_SLEEP"
else: state_sleep = "GOOD_SLEEP"
if screen < 3.0: state_screen = "LOW_SCREEN"
elif 3.0 <= screen <= 5.0: state_screen = "MEDIUM_SCREEN"
else: state_screen = "HIGH_SCREEN"
if physical < 2.0: state_phys = "LOW_ACT"
elif 2.0 <= physical <= 4.0: state_phys = "MEDIUM_ACT"
else: state_phys = "HIGH_ACT"
return f"STRESS_{stress.upper()}_{state_prod}_{state_sleep}_{state_screen}_{state_phys}"
def get_stress_management_recommendation(stress: str, prod_hours: float, sleep: float, screen: float, physical: float):
global policy
user_state = generate_state_id(stress, prod_hours, sleep, screen, physical)
# Logika Cell 4 dari kode Syifa
if user_state in policy and len(policy[user_state]) > 0:
actions_dict = policy[user_state]
sorted_actions = sorted(actions_dict.items(), key=lambda x: x[1], reverse=True)
top_5_actions = sorted_actions[:5]
else:
# Fallback jika state tidak ditemukan
top_5_actions = [('travelling', 10.0), ('watching sports', 9.0), ('social media engagement', 8.0), ('pilates', 7.0), ('meditation', 6.0)]
action_names = [item[0] for item in top_5_actions]
action_scores = [item[1] for item in top_5_actions]
total_score = sum(action_scores)
probabilities = [score / total_score for score in action_scores]
# Ambil 1 rekomendasi utama secara acak berdasarkan bobot probabilitas
chosen_recommendation = random.choices(action_names, weights=probabilities, k=1)[0]
# Format output top 5 agar rapi di API
formatted_top_5 = [{"activity": name, "score": round(score, 4)} for name, score in top_5_actions]
return {
"state_id": user_state,
"chosen_recommendation": chosen_recommendation,
"top_5_candidates": formatted_top_5
}
def process_user_feedback(stress: str, prod_hours: float, sleep: float, screen: float, physical: float, action_given: str, feedback_status: str):
global policy
user_state = generate_state_id(stress, prod_hours, sleep, screen, physical)
# Reload data terbaru dari file sebelum memodifikasi (mencegah desinkronisasi)
policy = load_policy()
if user_state in policy and action_given in policy[user_state]:
current_score = policy[user_state][action_given]
# Logika Cell 6 penyesuaian skor berdasarkan feedback status
if feedback_status.lower() == "negative":
new_score = current_score * 0.70
else:
new_score = current_score * 1.10
policy[user_state][action_given] = new_score
# Simpan kembali perubahan ke file JSON
save_policy(policy)
return {"status": "success", "message": f"Skor untuk '{action_given}' pada state '{user_state}' berhasil diperbarui menjadi {round(new_score, 4)}."}
return {"status": "ignored", "message": "State atau tindakan tidak ditemukan dalam policy untuk diperbarui."} |