Update app.py

app.py

@@ -2,25 +2,42 @@ import gradio as gr
 from transformers import pipeline
 from PIL import Image
 
-# Gunakan model PUBLIC yang terbuka
-classifier = pipeline("image-classification", model="umm-maybe/ai-image-detector")
+# Dua model publik gratis
+model1 = pipeline("image-classification", model="umm-maybe/ai-image-detector")
+model2 = pipeline("image-classification", model="fal-ai/imagenet-real-or-fake")
 
 def detect_ai(image):
-    results = classifier(image)
-    label = results[0]['label']
-    confidence = results[0]['score'] * 100
-    
-    if label.lower() in ["fake", "ai-generated"]:
-        return f"🚨 Kemungkinan besar AI Generated\nLabel: {label}\nConfidence: {confidence:.2f}%"
+    img = image.convert("RGB").resize((224, 224))
+
+    res1 = model1(img)[0]
+    res2 = model2(img)[0]
+
+    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:
+        verdict = "🚨 Kemungkinan besar AI Generated"
+    elif real_votes > ai_votes:
+        verdict = "✅ Kemungkinan besar Foto Asli"
     else:
-        return f"✅ Kemungkinan besar Foto Asli\nLabel: {label}\nConfidence: {confidence:.2f}%"
+        verdict = "⚠️ Tidak Pasti (butuh cek manual)"
+
+    return f"""{verdict}
+
+Model 1: {label1} ({conf1*100:.2f}%)
+Model 2: {label2} ({conf2*100:.2f}%)"""
 
 demo = gr.Interface(
     fn=detect_ai,
     inputs=gr.Image(type="pil"),
     outputs="text",
-    title="Deteksi Foto AI vs Asli",
-    description="Upload foto untuk mengetahui apakah ini foto asli atau hasil AI-generated."
+    title="Deteksi Foto AI vs Asli (Ensemble)",
+    description="Menggunakan dua model gratis sekaligus agar hasil lebih akurat."
 )
 
 demo.launch()