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Update app.py
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app.py
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import cv2
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import torch
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import requests
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from PIL import Image
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from fastapi import FastAPI
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from supabase import create_client
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from transformers import AutoImageProcessor, AutoModelForImageClassification
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import os
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app = FastAPI()
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# ===============================
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#
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# ===============================
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API_TOKEN = os.getenv("API_TOKEN")
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def verify_key(x_api_key: str):
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if API_TOKEN and x_api_key != API_TOKEN:
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raise HTTPException(status_code=403, detail="Unauthorized")
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# ===============================
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# Supabase
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# ===============================
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SUPABASE_URL = os.getenv("SUPABASE_URL")
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SUPABASE_KEY = os.getenv("SUPABASE_KEY")
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supabase = create_client(SUPABASE_URL, SUPABASE_KEY)
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# ===============================
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# Load Model (
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# ===============================
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model_name = "AdamCodd/vit-base-nsfw-detector"
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model = AutoModelForImageClassification.from_pretrained(model_name)
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# ===============================
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# Image
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# ===============================
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def check_image(url):
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try:
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probs = torch.softmax(outputs.logits, dim=1)
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return "explicit" if probs[0][1] > 0.5 else "safe"
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return "safe"
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# ===============================
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# Video
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# ===============================
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def check_video(url, frame_sample_rate=30):
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cap.release()
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return "safe"
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except:
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return "safe"
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# ===============================
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#
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# ===============================
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"type": "image",
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"url": post["image_url"],
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"result": result
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})
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# Videos
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vid_posts = supabase.table("trendz").select("*").execute().data
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for post in vid_posts:
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result = check_video(post["video_url"])
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results.append({
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"id": post["id"],
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"type": "video",
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"url": post["video_url"],
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"result": result
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})
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return {
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"status": "
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"
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"data": results
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}
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import cv2
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import torch
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import requests
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import threading
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import time
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from PIL import Image
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from fastapi import FastAPI
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from supabase import create_client
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from transformers import AutoImageProcessor, AutoModelForImageClassification
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import os
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app = FastAPI()
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# ===============================
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# π Supabase Setup
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# ===============================
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SUPABASE_URL = os.getenv("SUPABASE_URL")
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SUPABASE_KEY = os.getenv("SUPABASE_KEY")
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supabase = create_client(SUPABASE_URL, SUPABASE_KEY)
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# ===============================
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# π€ Load NSFW Model (once)
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# ===============================
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model_name = "AdamCodd/vit-base-nsfw-detector"
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model = AutoModelForImageClassification.from_pretrained(model_name)
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# ===============================
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# πΌ Image Check
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# ===============================
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def check_image(url):
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try:
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probs = torch.softmax(outputs.logits, dim=1)
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return "explicit" if probs[0][1] > 0.5 else "safe"
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except Exception as e:
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print("Image error:", e)
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return "safe"
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# ===============================
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# π₯ Video Check
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# ===============================
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def check_video(url, frame_sample_rate=30):
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try:
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cap.release()
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return "safe"
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except Exception as e:
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print("Video error:", e)
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return "safe"
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# ===============================
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# π BACKGROUND WORKER (EVERY 5 MIN)
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# ===============================
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def moderation_worker():
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while True:
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try:
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print("π Running moderation job...")
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# ===============================
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# πΌ Process Images (Batch)
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# ===============================
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img_posts = supabase.table("posts") \
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.select("*") \
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.limit(10) \
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.execute().data
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for post in img_posts:
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existing = supabase.table("content_moderation") \
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.select("id") \
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.eq("post_id", post["id"]) \
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.eq("post_type", "image") \
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.execute()
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if len(existing.data) == 0:
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result = check_image(post["image_url"])
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supabase.table("content_moderation").insert({
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"post_id": post["id"],
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"post_type": "image",
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"file_url": post["image_url"],
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"result": result
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}).execute()
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print(f"β
IMAGE {post['id']} β {result}")
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# ===============================
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# π₯ Process Videos (Batch)
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# ===============================
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vid_posts = supabase.table("trendz") \
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.select("*") \
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.limit(5) \
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.execute().data
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for post in vid_posts:
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existing = supabase.table("content_moderation") \
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.select("id") \
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.eq("post_id", post["id"]) \
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.eq("post_type", "video") \
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.execute()
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if len(existing.data) == 0:
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result = check_video(post["video_url"])
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supabase.table("content_moderation").insert({
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"post_id": post["id"],
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"post_type": "video",
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"file_url": post["video_url"],
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"result": result
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}).execute()
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print(f"π₯ VIDEO {post['id']} β {result}")
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except Exception as e:
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print("β Worker error:", e)
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# β± Run every 5 minutes
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time.sleep(300)
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# ===============================
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# π START WORKER ON STARTUP
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# ===============================
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@app.on_event("startup")
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def start_worker():
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thread = threading.Thread(target=moderation_worker, daemon=True)
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thread.start()
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# ===============================
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# π HEALTH CHECK ENDPOINT
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# ===============================
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@app.get("/")
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def health():
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return {
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"status": "running",
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"message": "NSFW moderation running every 5 minutes"
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}
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