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| """ | |
| AI Image Detector | |
| Minimal. Yes/No + Confidence. | |
| Uses Smogy detector (balanced precision/recall, fewer false positives) | |
| """ | |
| import os | |
| os.environ['HF_HOME'] = '/tmp/hf_cache' | |
| os.environ['TRANSFORMERS_CACHE'] = '/tmp/hf_cache' | |
| import streamlit as st | |
| from PIL import Image | |
| import io | |
| st.set_page_config( | |
| page_title="AI?", | |
| page_icon="🔍", | |
| layout="centered" | |
| ) | |
| st.markdown(""" | |
| <style> | |
| #MainMenu, footer, header {visibility: hidden;} | |
| .block-container {padding-top: 2rem; padding-bottom: 0; max-width: 500px;} | |
| </style> | |
| """, unsafe_allow_html=True) | |
| def load_detector(): | |
| """Load Smogy AI detector - better balanced, fewer false positives""" | |
| from transformers import pipeline | |
| return pipeline( | |
| "image-classification", | |
| model="Smogy/SMOGY-Ai-images-detector", | |
| device=-1 | |
| ) | |
| def predict(detector, image: Image.Image) -> tuple: | |
| """Returns (is_ai, ai_confidence, human_confidence)""" | |
| img = image.convert('RGB') | |
| # Resize if too large | |
| max_dim = 1024 | |
| if max(img.size) > max_dim: | |
| ratio = max_dim / max(img.size) | |
| img = img.resize((int(img.size[0] * ratio), int(img.size[1] * ratio)), Image.LANCZOS) | |
| results = detector(img) | |
| ai_score = 0.0 | |
| human_score = 0.0 | |
| for r in results: | |
| label = r['label'].lower() | |
| if 'ai' in label or 'artificial' in label or 'fake' in label: | |
| ai_score = max(ai_score, r['score']) | |
| elif 'human' in label or 'real' in label: | |
| human_score = max(human_score, r['score']) | |
| # Conservative: only flag AI if confidence > 70% | |
| threshold = 0.70 | |
| is_ai = ai_score > threshold and ai_score > human_score | |
| return is_ai, ai_score, human_score | |
| def main(): | |
| st.markdown("<h2 style='text-align:center; margin:0;'>🔍</h2>", unsafe_allow_html=True) | |
| uploaded = st.file_uploader("", type=['png', 'jpg', 'jpeg', 'webp'], label_visibility="collapsed") | |
| if uploaded: | |
| image = Image.open(io.BytesIO(uploaded.read())) | |
| st.image(image, use_container_width=True) | |
| with st.spinner(""): | |
| try: | |
| detector = load_detector() | |
| is_ai, ai_conf, human_conf = predict(detector, image) | |
| if is_ai: | |
| pct = int(ai_conf * 100) | |
| st.markdown(f""" | |
| <div style='text-align:center; padding:1.5rem 0;'> | |
| <div style='font-size:4rem; font-weight:bold; color:#e74c3c;'>AI</div> | |
| <div style='font-size:2rem; color:#666;'>{pct}%</div> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| else: | |
| pct = int(human_conf * 100) | |
| st.markdown(f""" | |
| <div style='text-align:center; padding:1.5rem 0;'> | |
| <div style='font-size:4rem; font-weight:bold; color:#27ae60;'>REAL</div> | |
| <div style='font-size:2rem; color:#666;'>{pct}%</div> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| except Exception as e: | |
| st.error(str(e)) | |
| if __name__ == "__main__": | |
| main() | |