Update app.py

app.py

@@ -1,31 +1,29 @@
 import gradio as gr
 from transformers import pipeline
+from PIL import Image
 
-# gunakan model deteksi AI vs Real
-pipe = pipeline("image-classification", model="umm-maybe/AI-image-detector")
+# Gunakan model khusus deteksi AI-generated photo
+detector = pipeline("image-classification", model="microsoft/ai-image-detector")
 
-def detect_ai(image):
-    results = pipe(image)
+def detect(image):
+    results = detector(image)
     results = sorted(results, key=lambda x: x['score'], reverse=True)
     label = results[0]['label']
-    confidence = results[0]['score']
+    confidence = results[0]['score'] * 100
 
-    if "ai" in label.lower() or "fake" in label.lower():
-        status = "⚠️ Kemungkinan besar AI Generated"
+    if "fake" in label.lower() or "ai" in label.lower():
+        return f"⚠️ Kemungkinan besar Gambar AI\n\nLabel: {label}\nConfidence: {confidence:.2f}%"
     else:
-        status = "✅ Kemungkinan besar Foto Asli"
+        return f"✅ Kemungkinan besar Foto Asli\n\nLabel: {label}\nConfidence: {confidence:.2f}%"
 
-    return f"{status}\n\nLabel: {label}\nConfidence: {confidence*100:.2f}%"
+# UI dengan Gradio
+app = gr.Interface(
+    fn=detect,
+    inputs=gr.Image(type="pil", label="Upload Foto"),
+    outputs="text",
+    title="AI vs Foto Detector",
+    description="Unggah foto untuk mendeteksi apakah ini foto asli atau hasil AI-generated."
+)
 
-with gr.Blocks() as demo:
-    gr.Markdown("## 🖼️ AI Image Detector")
-    with gr.Row():
-        with gr.Column():
-            image_input = gr.Image(type="pil", label="Upload Gambar")
-            btn = gr.Button("Deteksi")
-        with gr.Column():
-            output_text = gr.Textbox(label="Hasil Deteksi", lines=5)
-
-    btn.click(detect_ai, inputs=image_input, outputs=output_text)
-
-demo.launch()
+if __name__ == "__main__":
+    app.launch()