Update app.py

app.py

@@ -1,11 +1,12 @@
 import gradio as gr
-from PIL import Image, ExifTags
+from PIL import Image
+from transformers import pipeline
 import numpy as np
 import cv2
 
-# ----------------------------
-# ANALISIS LOKAL
-# ----------------------------
+# 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()
@@ -14,19 +15,6 @@ 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)
@@ -42,48 +30,29 @@ def high_freq_artifacts(image):
     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 ----------
-    hf_norm = min(high_freq_score / 10, 10)
-    noise_norm = max(0, 12 - noise_score)
-    weighted_score = 0.5 * hf_norm + 0.5 * noise_norm
-
-    # Threshold tegas
-    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"
+    # Prediksi menggunakan model deteksi AI
+    result = classifier(image)
+    ai_score = result[0]['score'] if result[0]['label'] == 'AI-generated' else 0
 
-    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'}"
-    ]
+    # Gabungkan skor
+    final_score = ai_score * 0.7 + (1 - blur_score / 1000) * 0.2 + (1 - noise_score / 255) * 0.1
 
-    return "\n".join(output_lines)
+    if final_score > 0.5:
+        return "🤖 Gambar ini kemungkinan besar dihasilkan oleh AI."
+    else:
+        return "✅ Gambar ini kemungkinan besar asli."
 
-# ----------------------------
-# GRADIO INTERFACE
-# ----------------------------
-with gr.Blocks(title="Hybrid AI Realistic Detector (Jejak Digital)") as demo:
-    gr.Markdown("Unggah gambar, sistem akan mendeteksi apakah gambar kemungkinan besar asli atau dihasilkan AI.")
+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_md = gr.Markdown(label="Hasil Deteksi")
-    detect_btn = gr.Button("Deteksi")
-    detect_btn.click(fn=detect_image, inputs=img_input, outputs=output_md)
+        output = gr.Textbox(label="Hasil Deteksi")
+    btn = gr.Button("Deteksi")
+    btn.click(detect_image, inputs=img_input, outputs=output)
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()