import gradio as gr
from PIL import Image, ImageStat, ExifTags
import numpy as np
import cv2

# ----------------------------
# Fungsi hitung sharpness / blur
# ----------------------------
def calculate_blur(image):
    image_cv = np.array(image.convert("L"))  # konversi ke grayscale
    laplacian_var = cv2.Laplacian(image_cv, cv2.CV_64F).var()
    return laplacian_var

# ----------------------------
# Fungsi deteksi metadata
# ----------------------------
def has_camera_exif(image):
    try:
        exif = image._getexif()
        if exif:
            for tag, value in exif.items():
                decoded = ExifTags.TAGS.get(tag, tag)
                if decoded in ["Make", "Model"]:
                    return True
    except:
        return False
    return False

# ----------------------------
# Fungsi prediksi AI vs Real
# ----------------------------
def detect_image(image):
    blur_score = calculate_blur(image)
    exif_present = has_camera_exif(image)
    
    # Threshold empiris (bisa disesuaikan)
    # blur rendah + metadata → Foto Asli
    # blur tinggi + tidak ada metadata → AI
    if exif_present or blur_score > 100:
        final_result = f"✅ Foto Asli"
    else:
        final_result = f"🤖 AI Detected"
    
    output = f"""
### Hasil Deteksi:
{final_result}

**Blur Score:** {blur_score:.2f}
**Metadata Kamera:** {'Ada' if exif_present else 'Tidak Ada'}
"""
    return output

# ----------------------------
# Gradio Interface
# ----------------------------
iface = gr.Interface(
    fn=detect_image,
    inputs=gr.Image(type="pil"),
    outputs="markdown",
    title="AI vs Foto Asli Detector (Gratis)",
    description="Unggah gambar, sistem akan mendeteksi apakah gambar kemungkinan besar asli atau dihasilkan AI berdasarkan analisis blur dan metadata."
)

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