Update app.py

app.py

@@ -1,14 +1,46 @@
 from transformers import pipeline
+import gradio as gr
+from PIL import Image, ExifTags
 
-# Menggunakan model publik dan gratis untuk klasifikasi gambar.
-# Model 'orpatashnik/image-real-fake' diganti karena tidak dapat diakses.
-classifier = pipeline("image-classification", model="google/vit-base-patch16-224")
-
-# Ini adalah contoh penggunaan, Anda bisa mengadaptasi sesuai kebutuhan.
-# Contoh: Melakukan klasifikasi pada file gambar.
-# from PIL import Image
-# import requests
-# url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cat-dog.jpg"
-# image = Image.open(requests.get(url, stream=True).raw)
-# result = classifier(image)
-# print(result)
+# Muat model Hugging Face khusus deteksi AI
+classifier = pipeline("image-classification", model="elacour/ai-image-detection")
+
+def detect_image(image):
+    # --- Cek metadata (EXIF) ---
+    exif = {}
+    try:
+        raw_exif = image._getexif()
+        if raw_exif:
+            exif = {ExifTags.TAGS.get(k, k): v for k, v in raw_exif.items()}
+    except:
+        pass
+    
+    if not exif:
+        exif_result = "⚠️ Tidak ada metadata kamera → kemungkinan besar AI atau editan"
+    else:
+        exif_result = "✅ Metadata kamera terdeteksi"
+
+    # --- Prediksi model ---
+    results = classifier(image)
+    label = results[0]["label"]
+    score = results[0]["score"] * 100
+
+    if "fake" in label.lower() or "ai" in label.lower():
+        verdict = "🚨 Kemungkinan besar Hasil AI"
+    else:
+        verdict = "📷 Kemungkinan besar Foto Asli"
+
+    # --- Gabungan hasil ---
+    final_result = f"{verdict}\n\nLabel Model: {label}\nConfidence: {score:.2f}%\n\nCek Metadata: {exif_result}"
+    return final_result
+
+# Buat UI Gradio
+iface = gr.Interface(
+    fn=detect_image,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Hybrid AI Image Detector",
+    description="Upload foto untuk mendeteksi apakah gambar hasil kamera asli atau hasil AI."
+)
+
+iface.launch()