import gradio as gr
from transformers import pipeline
from PIL import Image, ExifTags
import numpy as np
import cv2
import io

# Load HuggingFace pipeline
detector = pipeline("image-classification", model="falconsai/nsfw_image_detection")  
# ganti model ke yang support binary classification real vs ai kalau mau training

def analyze_image(image):
    # Konversi ke format OpenCV
    img_bytes = io.BytesIO()
    image.save(img_bytes, format="PNG")
    img_bytes = np.frombuffer(img_bytes.getvalue(), dtype=np.uint8)
    cv_img = cv2.imdecode(img_bytes, cv2.IMREAD_COLOR)

    # Blur score (laplacian variance)
    gray = cv2.cvtColor(cv_img, cv2.COLOR_BGR2GRAY)
    blur_score = cv2.Laplacian(gray, cv2.CV_64F).var()

    # Noise score (std dev)
    noise_score = np.std(gray)

    # Metadata Kamera
    meta = image.getexif()
    meta_info = "Ada" if meta else "Tidak Ada"

    # Prediksi AI vs Asli (pakai HF pipeline dummy dulu)
    preds = detector(image)
    label = preds[0]["label"].lower()
    score = preds[0]["score"]

    # Normalisasi ke persentase AI vs Asli
    if "artificial" in label or "fake" in label or "ai" in label:
        ai_prob = score * 100
    else:
        ai_prob = (1 - score) * 100
    real_prob = 100 - ai_prob

    # Koreksi dengan metadata → jika metadata kamera ada, naikkan skor real
    if meta_info == "Ada":
        real_prob += 15
        ai_prob -= 15

    # Koreksi blur/noise → foto asli biasanya lebih natural
    if blur_score > 500 and noise_score > 20:
        real_prob += 5

    # Clamp ke 0-100
    ai_prob = max(0, min(100, ai_prob))
    real_prob = max(0, min(100, real_prob))

    # Output
    hasil = f"""🖼️ Hasil Deteksi:
    {ai_prob:.2f}% AI / {real_prob:.2f}% Asli

    Blur Score: {blur_score:.2f}
    Noise Score: {noise_score:.2f}
    Metadata Kamera: {meta_info}
    """

    return hasil

demo = gr.Interface(
    fn=analyze_image,
    inputs=gr.Image(type="pil"),
    outputs="text",
    title="AI vs Real Image Detector",
    description="Deteksi apakah gambar AI-generated atau asli. Gratis, tanpa API berbayar."
)

if __name__ == "__main__":
    demo.launch()