app.py

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

# ----------------------------
# MODEL
# ----------------------------
try:
    hf_detector = pipeline("image-classification", model="umm-maybe/AI-image-detector")
except Exception as e:
    hf_detector = None
    print("HF AI-detector gagal dimuat:", e)

try:
    general_model = pipeline("image-classification", model="google/vit-base-patch16-224")
except Exception as e:
    general_model = None
    print("General classifier gagal dimuat:", e)

# ----------------------------
# 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)
    noise_std = np.std(gray - np.mean(gray))
    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 WEIGHTED
# ----------------------------
def detect_image(image):
    hf_score = 0
    general_score = 0
    local_score = 0

    # -------- HF AI-detector --------
    if hf_detector:
        try:
            result = hf_detector(image)
            label = result[0]['label'].lower()
            conf = result[0]['score'] * 100
            hf_score = conf if any(x in label for x in ["fake", "ai", "artificial"]) else 0
        except:
            hf_score = 0

    # -------- General model --------
    if general_model:
        try:
            result2 = general_model(image)
            label2 = result2[0]['label'].lower()
            conf2 = result2[0]['score'] * 100
            general_score = conf2 if any(x in label2 for x in ["anime","cartoon","illustration","maya"]) else 0
        except:
            general_score = 0

    # -------- Analisis lokal --------
    blur_score = calculate_blur(image)
    noise_score = calculate_noise(image)
    exif_present = has_camera_exif(image)
    local_score = 0
    if blur_score < 100 or noise_score < 10:
        local_score += 50
    if not exif_present:
        local_score += 10

    # -------- Weighted Score --------
    final_score = hf_score*0.6 + general_score*0.25 + local_score*0.15

    if final_score > 50:
        final_result = "🤖 AI Detected"
    else:
        final_result = "✅ Foto Asli"

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

**Weighted Skor:** {final_score:.2f}
**HF AI-detector:** {result[0]['label']} ({result[0]['score']*100:.2f}%)
**General Model:** {result2[0]['label']} ({result2[0]['score']*100:.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 (Weighted Hybrid)",
    description="Unggah gambar, sistem hybrid akan mendeteksi apakah gambar kemungkinan besar asli atau dihasilkan AI."
)

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