Update app.py

app.py

@@ -1,58 +1,15 @@
 import gradio as gr
 from PIL import Image
 from transformers import pipeline
-import numpy as np
-import cv2
 
 # Muat model deteksi AI
 classifier = pipeline("image-classification", model="umm-maybe/AI-image-detector")
 
-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 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
-
 def detect_image(image: Image.Image):
-    blur_score = calculate_blur(image)
-    noise_score = calculate_noise(image)
-    high_freq_score = high_freq_artifacts(image)
-
-    # Prediksi menggunakan model deteksi AI
     result = classifier(image)
     ai_score = result[0]['score'] if result[0]['label'] == 'AI-generated' else 0
+    return f"Skor AI: {ai_score:.2f}"
 
-    # Gabungkan skor
-    final_score = ai_score * 0.7 + (1 - blur_score / 1000) * 0.2 + (1 - noise_score / 255) * 0.1
-
-    if final_score > 0.5:
-        return "🤖 Gambar ini kemungkinan besar dihasilkan oleh AI."
-    else:
-        return "✅ Gambar ini kemungkinan besar asli."
-
-with gr.Blocks() as demo:
-    gr.Markdown("### Deteksi Gambar AI vs Asli")
-    with gr.Row():
-        img_input = gr.Image(type="pil", label="Unggah Gambar")
-        output = gr.Textbox(label="Hasil Deteksi")
-    btn = gr.Button("Deteksi")
-    btn.click(detect_image, inputs=img_input, outputs=output)
-
-demo.launch()
+# Antarmuka Gradio
+iface = gr.Interface(fn=detect_image, inputs=gr.Image(type="pil"), outputs="text")
+iface.launch()