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

# ----------------------------
# ANALISIS LOKAL
# ----------------------------
def calculate_blur(image):
    gray = np.array(image.convert("L"))
    return cv2.Laplacian(gray, cv2.CV_64F).var()

def calculate_noise(image):
    gray = np.array(image.convert("L"), dtype=np.float32)
    return np.std(gray - np.mean(gray))

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

# -------- FFT ANALYSIS --------
def high_freq_artifacts(image):
    gray = np.array(image.convert("L"), dtype=np.float32)
    f = np.fft.fft2(gray)
    fshift = np.fft.fftshift(f)
    magnitude_spectrum = np.abs(fshift)
    rows, cols = gray.shape
    crow, ccol = rows // 2 , cols // 2
    r = min(crow, ccol) // 4
    mask = np.zeros((rows, cols))
    mask[crow-r:crow+r, ccol-r:ccol+r] = 1
    low_energy = np.sum(magnitude_spectrum * mask)
    total_energy = np.sum(magnitude_spectrum)
    high_ratio = (total_energy - low_energy) / total_energy * 100
    return high_ratio

# ----------------------------
# DETEKSI AI HYBRID ADAPTIF
# ----------------------------
def detect_image(image: Image.Image):
    blur_score = calculate_blur(image)
    noise_score = calculate_noise(image)
    exif_present = has_camera_exif(image)
    high_freq_score = high_freq_artifacts(image)

    # ---------- Scoring adaptif ----------
    # Normalisasi high_freq_score ke 0-10
    hf_norm = min(high_freq_score / 10, 10)
    # Noise invers (AI biasanya noise rendah)
    noise_norm = max(0, 12 - noise_score)
    # Weighted score
    weighted_score = 0.5 * hf_norm + 0.5 * noise_norm

    # Kondisi tegas: threshold > 5 → AI
    is_ai = False
    if not exif_present and weighted_score > 5 and blur_score < 150:
        is_ai = True

    final_result = "🤖 AI Detected" if is_ai else "✅ Foto Asli"

    output_lines = [
        f"### Hasil Deteksi:\n{final_result}",
        f"Blur Score: {blur_score:.2f}",
        f"Noise Score: {noise_score:.2f}",
        f"High-Freq Artifacts Score: {high_freq_score:.2f}",
        f"Weighted Score: {weighted_score:.2f}",
        f"Metadata Kamera: {'Ada' if exif_present else 'Tidak Ada'}"
    ]

    return "\n".join(output_lines)

# ----------------------------
# GRADIO INTERFACE
# ----------------------------
with gr.Blocks(title="Hybrid AI Realistic Detector (Adaptif & Tegas)") as demo:
    gr.Markdown("Unggah gambar, sistem akan mendeteksi apakah gambar kemungkinan besar asli atau dihasilkan AI.")
    with gr.Row():
        img_input = gr.Image(type="pil", label="Unggah Gambar")
        output_md = gr.Markdown(label="Hasil Deteksi")
    detect_btn = gr.Button("Deteksi")
    detect_btn.click(fn=detect_image, inputs=img_input, outputs=output_md)

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