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 HF PRIORITAS
# ----------------------------
def detect_image(image):
    output_lines = []

    # -------- HF AI-detector --------
    hf_score = 0
    hf_label = "N/A"
    hf_conf = 0
    if hf_detector:
        try:
            result = hf_detector(image)
            hf_label = result[0]['label']
            hf_conf = result[0]['score'] * 100
            # tentukan HF score AI relevan
            if any(x in hf_label.lower() for x in ["fake", "ai", "artificial"]):
                hf_score = hf_conf
        except:
            hf_score = 0

    # Jika HF confidence tinggi → langsung AI
    if hf_score > 60:
        final_result = "🤖 AI Detected"
        weighted_score = hf_score
        output_lines.append(f"### Hasil Deteksi:\n{final_result}")
        output_lines.append(f"HF AI-detector: {hf_label} ({hf_conf:.2f}%)")
        return "\n".join(output_lines)

    # -------- General model --------
    general_score = 0
    general_label = "N/A"
    general_conf = 0
    if general_model:
        try:
            result2 = general_model(image)
            general_label = result2[0]['label']
            general_conf = result2[0]['score'] * 100
            if any(x in general_label.lower() for x in ["anime","cartoon","illustration","maya"]):
                general_score = general_conf
        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 (general + local) --------
    weighted_score = general_score*0.6 + local_score*0.4

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

    # -------- Output --------
    output_lines.append(f"### Hasil Deteksi:\n{final_result}")
    output_lines.append(f"Weighted Skor: {weighted_score:.2f}")
    output_lines.append(f"HF AI-detector: {hf_label} ({hf_conf:.2f}%)")
    output_lines.append(f"General Model: {general_label} ({general_conf:.2f}%)")
    output_lines.append(f"Blur Score: {blur_score:.2f}")
    output_lines.append(f"Noise Score: {noise_score:.2f}")
    output_lines.append(f"Metadata Kamera: {'Ada' if exif_present else 'Tidak Ada'}")

    return "\n".join(output_lines)

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

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