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import torch
import requests
import time
from PIL import Image
from supabase import create_client
from transformers import AutoImageProcessor, AutoModelForImageClassification
import os
# ===============================
# 🗄 Supabase Setup
# ===============================
SUPABASE_URL = os.getenv("SUPABASE_URL")
SUPABASE_KEY = os.getenv("SUPABASE_KEY")
supabase = create_client(SUPABASE_URL, SUPABASE_KEY)
# ===============================
# 🤖 Load NSFW Model (once)
# ===============================
print("🔄 Loading NSFW model...")
model_name = "AdamCodd/vit-base-nsfw-detector"
processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)
print("✅ Model loaded")
# ===============================
# 🖼 Image Check
# ===============================
def check_image(url):
try:
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
probs = torch.softmax(outputs.logits, dim=1)
return "explicit" if probs[0][1] > 0.5 else "safe"
except Exception as e:
print("❌ Image error:", e)
return "safe"
# ===============================
# 🎥 Video Check
# ===============================
def check_video(url, frame_sample_rate=30):
try:
cap = cv2.VideoCapture(url)
frame_count = 0
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
if frame_count % frame_sample_rate == 0:
img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
inputs = processor(images=img, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
probs = torch.softmax(outputs.logits, dim=1)
if probs[0][1] > 0.5:
cap.release()
return "explicit"
frame_count += 1
cap.release()
return "safe"
except Exception as e:
print("❌ Video error:", e)
return "safe"
# ===============================
# 🔁 MAIN WORKER LOOP
# ===============================
def moderation_worker():
while True:
try:
print("\n🚀 Running moderation job...")
# ===============================
# 🖼 Process Images
# ===============================
img_posts = supabase.table("posts") \
.select("*") \
.limit(10) \
.execute().data
for post in img_posts:
existing = supabase.table("content_moderation") \
.select("id") \
.eq("post_id", post["id"]) \
.eq("post_type", "image") \
.execute()
if len(existing.data) == 0:
result = check_image(post["image_url"])
supabase.table("content_moderation").insert({
"post_id": post["id"],
"post_type": "image",
"file_url": post["image_url"],
"result": result
}).execute()
print(f"🖼 IMAGE {post['id']} → {result}")
# ===============================
# 🎥 Process Videos
# ===============================
vid_posts = supabase.table("trendz") \
.select("*") \
.limit(5) \
.execute().data
for post in vid_posts:
existing = supabase.table("content_moderation") \
.select("id") \
.eq("post_id", post["id"]) \
.eq("post_type", "video") \
.execute()
if len(existing.data) == 0:
result = check_video(post["video_url"])
supabase.table("content_moderation").insert({
"post_id": post["id"],
"post_type": "video",
"file_url": post["video_url"],
"result": result
}).execute()
print(f"🎥 VIDEO {post['id']} → {result}")
except Exception as e:
print("❌ Worker error:", e)
# ⏱ WAIT 5 MINUTES
print("⏳ Sleeping for 5 minutes...\n")
time.sleep(300)
# ===============================
# ▶️ AUTO START
# ===============================
if __name__ == "__main__":
print("🔥 Starting NSFW moderation worker...")
moderation_worker() |