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

# ----------------------------
# Model Hugging Face AI detector (public)
# ----------------------------
try:
    hf_detector = pipeline("image-classification", model="umm-maybe/AI-image-detector")
except Exception as e:
    hf_detector = None
    print("Hugging Face model gagal dimuat, hanya analisis lokal yang dipakai:", e)

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

def calculate_noise(image):
    img_gray = np.array(image.convert("L"), dtype=np.float32)
    h, w = img_gray.shape
    mean = np.mean(img_gray)
    noise_std = np.std(img_gray - mean)
    return noise_std

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

# ----------------------------
# Deteksi hybrid AI vs Foto Asli
# ----------------------------
def detect_image(image):
    scores = []

    # ---- HUGGING FACE ----
    if hf_detector:
        try:
            result = hf_detector(image)
            label = result[0]['label'].lower()
            conf = result[0]['score'] * 100
            if "fake" in label or "ai" in label or "artificial" in label:
                scores.append(conf)  # AI score
            else:
                scores.append(0)  # Foto asli = 0 AI score
        except:
            scores.append(0)
    else:
        scores.append(0)

    # ---- ANALISIS LOKAL ----
    blur_score = calculate_blur(image)
    noise_score = calculate_noise(image)
    exif_present = has_camera_exif(image)

    # Blur & noise heuristics
    # Foto asli: blur tinggi (>100) atau noise > threshold
    # AI: blur rendah atau noise sangat rendah
    local_ai_score = 0
    if blur_score < 100 or noise_score < 10:
        local_ai_score += 50  # menambah skor AI
    if not exif_present:
        local_ai_score += 10  # sedikit penalti jika metadata hilang

    scores.append(local_ai_score)

    # ---- Gabungkan skor ----
    avg_score = sum(scores) / len(scores)

    # ---- Tentukan hasil final ----
    if avg_score > 50:
        final_result = "🤖 AI Detected"
    else:
        final_result = "✅ Foto Asli"

    output = f"""
### Hasil Deteksi:
{final_result}

**Skor rata-rata AI:** {avg_score:.2f}
**Blur Score:** {blur_score:.2f}
**Noise Score:** {noise_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 (Hybrid, Gratis)",
    description="Unggah gambar, sistem hybrid akan mendeteksi apakah gambar kemungkinan besar asli atau dihasilkan AI."
)

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