app_p/handler/recommendation_handler.py

import json
from pathlib import Path
import os
from langchain_google_genai import ChatGoogleGenerativeAI

# --- Data Loading ---
try:
    print("Loading personas for RecommendationHandler module...")
    # ABSOLUTE_PATH_TO_CONFIG = '/Users/jts-ai-sumana/rabbitlife_gemini/config/personas.json'
        # with open(ABSOLUTE_PATH_TO_CONFIG, "r", encoding="utf-8") as f:
    PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent
    CONFIG_PATH = PROJECT_ROOT / "config" / "personas.json"
    with open(CONFIG_PATH, "r", encoding="utf-8") as f:
        PERSONAS = json.load(f)
    print(f"✅ Personas config loaded successfully from: {CONFIG_PATH}")
except Exception as e:
    PERSONAS = {}
    print(f"‼️ ERROR: Could not load personas.json: {e}")

# --- LLM Initialization ---
try:
    INTEREST_LLM = ChatGoogleGenerativeAI(
        model="gemini-2.5-flash", 
        google_api_key=os.getenv("GOOGLE_API_KEY"),
        temperature=0, 
    )
    print("✅ LLM for interest classification initialized.")
except Exception as e:
    INTEREST_LLM = None
    print(f"‼️ ERROR initializing LLM for RecommendationHandler: {e}")


#  RECOMMENDATION ###
def generate_recommendation_from_profile(
    age: int,
    gender: str, # M, F
    salary: int,
    original_interest: str
) -> dict:
    if not PERSONAS or not INTEREST_LLM:
        return {"error": "Module not initialized correctly."}

    # print(f"\n--- Generating Recommendation Profile ---")
    # print(f"Input Data: Age={age}, Gender='{gender}', Salary={salary}, Interest='{original_interest}'")

    # 1. หา Persona ที่ตรงกับอายุ
    matched_persona = next((details for _, details in PERSONAS.items() if details.get("age_min", -1) <= age <= details.get("age_max", -1)), None)
    if not matched_persona:
        return {"error": f"No matching persona found for age {age}."}
    # print(f"-> Matched Persona: '{matched_persona.get('persona_name', 'N/A')}'")
    
    # 2. หา Tier ที่ตรงกับเงินเดือน
    products_from_tier = []
    salary_tiers = matched_persona.get('recommendations_by_salary', [])
    for tier in salary_tiers:
        if tier.get("salary_min", 0) <= salary <= tier.get("salary_max", float('inf')):
            products_from_tier = tier.get("products", [])
            # print(f"-> Matched Salary Tier: '{tier.get('tier_name')}'")
            break
    if not products_from_tier and salary_tiers:
        products_from_tier = salary_tiers[0].get("products", [])

    # 3. รวบรวม product จาก Tier และเพศ
    all_tier_products = [p for p in products_from_tier if p.get('gender') == 'all' or p.get('gender') == gender]
    
    # 4. กรองตาม Interest - LLM
    interest_prompt = f"""Analyze the user's interest and classify it into ONE of these categories:
    'ประกันคุ้มครองชีวิต', 'ประกันเพื่อการลงทุน', 'ประกันสุขภาพ', 'ประกันอุบัติเหตุ', 'ทั่วไป'.
    User Interest: "{original_interest}"
    Category:"""
    
    response = INTEREST_LLM.invoke(interest_prompt)    
    target_insurance_type = response.content.strip()
    # print(f"-> LLM classified interest as: '{target_insurance_type}'")

    products_to_recommend = all_tier_products
    searched_outside_tier = False

    valid_types = ['ประกันเพื่อการลงทุน', 'ประกันสุขภาพ', 'ประกันอุบัติเหตุ', 'ประกันคุ้มครองชีวิต']
    
    if target_insurance_type in valid_types:
        # print(f"-> Filtering for interest: '{target_insurance_type}' across all tiers...")
        
        all_persona_products = []
        for tier_fallback in matched_persona.get('recommendations_by_salary', []):
            all_persona_products.extend(tier_fallback.get('products', []))
        # print(all_persona_products)
        
        gender_filtered_products = [p for p in all_persona_products if p.get('gender') == 'all' or p.get('gender') == gender]
        final_filtered_products = [p for p in gender_filtered_products if p.get('insurance_type') == target_insurance_type]

        if final_filtered_products:
            # print(f"-> Found {len(final_filtered_products)} matching products.")
            products_to_recommend = final_filtered_products
        else:
            print(f"-> CRITICAL: No product of type '{target_insurance_type}' found anywhere in this persona.")
            return {"error": f"ขออภัยค่ะ เราไม่พบผลิตภัณฑ์ประเภท '{target_insurance_type}' ที่เหมาะสมกับโปรไฟล์ของคุณในขณะนี้ค่ะ"}


    # 5. ตรวจสอบและเพิ่มสัญญาหลักถ้าจำเป็น
    has_basic_plan = any(p.get('plan_type') == 'Basic' for p in products_to_recommend)
    auto_added_main_plan = False 
    if not has_basic_plan and any(p.get('plan_type') == 'Rider' for p in products_to_recommend):
        # print("-> Filtered list only has Riders. Adding a default main plan.")
        default_main_plans = [p for p in all_tier_products if p.get('plan_type') == 'Basic']
        if default_main_plans:
            products_to_recommend.insert(0, default_main_plans[0])
            auto_added_main_plan = True


    # 6. จัดกลุ่มและเตรียมข้อมูลสำหรับส่งคืน
    main_plans_pre = [p for p in products_to_recommend if p.get('plan_type') == 'Basic']
    main_plans = [dict(t) for t in {tuple(d.items()) for d in main_plans_pre}]
    riders_pre = [p for p in products_to_recommend if p.get('plan_type') == 'Rider']
    riders = [dict(t) for t in {tuple(d.items()) for d in riders_pre}]

    print(main_plans)

    main_plans_str = "ไม่มีแผนประกันหลักที่แนะนำ"
    if main_plans:
        main_plans_str = "\n".join([f"• **{p.get('product_name')}**: {p.get('product_description')}" for p in main_plans])
    riders_str = "ไม่มีสัญญาเพิ่มเติมที่แนะนำ"
    if riders:
        riders_str = "\n".join([f"• **{p.get('product_name')}**: {p.get('product_description')}" for p in riders])

    return {
        "age": age, "gender": gender, "salary": f"{salary:,}",
        "persona_name": matched_persona['persona_name'],
        "persona_description": matched_persona['description'],
        "original_interest": original_interest,
        "main_plans_str": main_plans_str.strip(),
        "riders_str": riders_str.strip(),
        "auto_added_main_plan": auto_added_main_plan,
        "searched_outside_tier": searched_outside_tier,
        "interest_category": target_insurance_type if target_insurance_type != 'ทั่วไป' else 'ประกัน'
    }