Update app.py

app.py

@@ -2,9 +2,9 @@ import gradio as gr
 from transformers import pipeline
 from PIL import Image
 
-# Dua model publik gratis
+# Model publik (tidak perlu token)
 model1 = pipeline("image-classification", model="umm-maybe/ai-image-detector")
-model2 = pipeline("image-classification", model="fal-ai/imagenet-real-or-fake")
+model2 = pipeline("image-classification", model="google/vit-base-patch16-224")
 
 def detect_ai(image):
     img = image.convert("RGB").resize((224, 224))
@@ -15,29 +15,25 @@ def detect_ai(image):
     label1, conf1 = res1['label'], res1['score']
     label2, conf2 = res2['label'], res2['score']
 
-    # Voting sederhana
-    labels = [label1.lower(), label2.lower()]
-    ai_votes = sum(1 for l in labels if "fake" in l or "ai" in l)
-    real_votes = len(labels) - ai_votes
-
-    if ai_votes > real_votes:
+    # Voting sederhana: kalau model1 bilang FAKE, lebih dipercaya
+    if "fake" in label1.lower() or "ai" in label1.lower():
         verdict = "🚨 Kemungkinan besar AI Generated"
-    elif real_votes > ai_votes:
+    elif "real" in label1.lower():
         verdict = "✅ Kemungkinan besar Foto Asli"
     else:
-        verdict = "⚠️ Tidak Pasti (butuh cek manual)"
+        verdict = "⚠️ Tidak Pasti (cek manual)"
 
     return f"""{verdict}
 
-Model 1: {label1} ({conf1*100:.2f}%)
-Model 2: {label2} ({conf2*100:.2f}%)"""
+Model AI-detector: {label1} ({conf1*100:.2f}%)
+Model General (ViT): {label2} ({conf2*100:.2f}%)"""
 
 demo = gr.Interface(
     fn=detect_ai,
     inputs=gr.Image(type="pil"),
     outputs="text",
-    title="Deteksi Foto AI vs Asli (Ensemble)",
-    description="Menggunakan dua model gratis sekaligus agar hasil lebih akurat."
+    title="Deteksi Foto AI vs Asli (Gratis)",
+    description="Menggunakan model publik gratis Hugging Face."
 )
 
 demo.launch()