app.py

import os
import time
import numpy as np
from datetime import datetime, timezone
from supabase import create_client, Client

# ========================================
# 🗄 Supabase Setup
# ========================================
SUPABASE_URL = os.getenv("SUPABASE_URL")
SUPABASE_KEY = os.getenv("SUPABASE_KEY")

supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)

# ========================================
# 📊 Helper Functions
# ========================================

def fetch_user_activity(user_id):
    response = supabase.table("user_activity") \
        .select("*") \
        .eq("actor_user_id", user_id) \
        .execute()
    return response.data


def fetch_safe_content():
    # ================= POSTS =================
    posts = supabase.table("posts").select("id, caption").execute().data

    moderated_posts = supabase.table("content_moderation") \
        .select("post_id, result") \
        .eq("post_type", "post") \
        .execute().data

    safe_posts = {p["post_id"] for p in moderated_posts if p["result"] == "safe"}

    safe_post_ids = [
        p["id"] for p in posts
        if (p["id"] in safe_posts) or
           (p["id"] not in [m["post_id"] for m in moderated_posts])
    ]

    # ================= TRENDZ =================
    trendz = supabase.table("trendz").select("id, caption").execute().data

    moderated_trendz = supabase.table("content_moderation") \
        .select("post_id, result") \
        .eq("post_type", "trendz") \
        .execute().data

    safe_trendz = {t["post_id"] for t in moderated_trendz if t["result"] == "safe"}

    safe_trendz_ids = [
        t["id"] for t in trendz
        if (t["id"] in safe_trendz) or
           (t["id"] not in [m["post_id"] for m in moderated_trendz])
    ]

    return safe_post_ids, safe_trendz_ids, posts, trendz


def recommend(user_id, top_n=5):
    activities = fetch_user_activity(user_id)
    safe_post_ids, safe_trendz_ids, posts, trendz = fetch_safe_content()

    summaries = [a["content_summary"] for a in activities if a.get("content_summary")]

    # Extract keywords
    keywords = []
    for summary in summaries:
        keywords.extend(summary.lower().split())

    # ================= POSTS =================
    post_scores = {}
    for p in posts:
        if p["id"] in safe_post_ids:
            caption = (p.get("caption") or "").lower()
            score = sum(caption.count(word) for word in keywords)
            post_scores[p["id"]] = score

    # ================= TRENDZ =================
    trendz_scores = {}
    for t in trendz:
        if t["id"] in safe_trendz_ids:
            caption = (t.get("caption") or "").lower()
            score = sum(caption.count(word) for word in keywords)
            trendz_scores[t["id"]] = score

    # Sort
    recommended_posts = [
        pid for pid, _ in sorted(post_scores.items(), key=lambda x: x[1], reverse=True)[:top_n]
    ]

    recommended_trendz = [
        tid for tid, _ in sorted(trendz_scores.items(), key=lambda x: x[1], reverse=True)[:top_n]
    ]

    # ================= FALLBACK =================
    if not recommended_posts and safe_post_ids:
        recommended_posts = list(
            np.random.choice(safe_post_ids, min(top_n, len(safe_post_ids)), replace=False)
        )

    if not recommended_trendz and safe_trendz_ids:
        recommended_trendz = list(
            np.random.choice(safe_trendz_ids, min(top_n, len(safe_trendz_ids)), replace=False)
        )

    return recommended_posts, recommended_trendz


def store_recommendations(user_id, posts, trendz):
    now = datetime.now(timezone.utc).isoformat()

    supabase.table("user_recommendations").upsert({
        "user_id": user_id,
        "recommended_post_ids": posts,
        "recommended_trendz_ids": trendz,
        "generated_at": now
    }).execute()


# ========================================
# 🔁 BACKGROUND WORKER (EVERY 10 MIN)
# ========================================
def recommendation_worker():
    while True:
        try:
            print("\n🚀 Running recommendation job...")

            users = supabase.table("profiles") \
                .select("id") \
                .limit(20) \
                .execute().data

            for u in users:
                posts, trendz = recommend(u["id"])
                store_recommendations(u["id"], posts, trendz)

                print(f"✅ Updated user {u['id']}")

        except Exception as e:
            print("❌ Worker error:", e)

        print("⏳ Sleeping for 10 minutes...\n")
        time.sleep(600)


# ========================================
# ▶️ AUTO START
# ========================================
if __name__ == "__main__":
    print("🔥 Starting AI Recommendation Worker...")
    recommendation_worker()