Update app.py

app.py

@@ -1,51 +1,69 @@
 import gradio as gr
 from transformers import pipeline
 from PIL import Image
+import numpy as np
 
-# Model utama untuk deteksi real vs AI
-detector = pipeline("image-classification", model="umm-maybe/AI-image-detector")
-
-# Model tambahan general classifier (backup)
-general = pipeline("image-classification", model="google/vit-base-patch16-224")
-
-def detect_image(img):
-    try:
-        # Prediksi dengan AI detector
-        result1 = detector(img)
-        label1 = result1[0]['label']
-        conf1 = round(result1[0]['score'] * 100, 2)
-
-        # Prediksi dengan model general (untuk cek ganda)
-        result2 = general(img)
-        label2 = result2[0]['label']
-        conf2 = round(result2[0]['score'] * 100, 2)
-
-        # Logika sederhana untuk memutuskan hasil
-        if "fake" in label1.lower() or "artificial" in label1.lower():
-            final = f"⚠️ Kemungkinan Besar AI Generated ({conf1}%)"
-        elif "real" in label1.lower():
-            final = f"✅ Kemungkinan Besar Foto Asli ({conf1}%)"
-        else:
-            final = f"⚠️ Tidak Pasti (cek manual)"
-
-        output = f"""
+# ----------------------------
+# Inisialisasi model publik
+# ----------------------------
+model1 = pipeline("image-classification", model="microsoft/resnet-50")
+model2 = pipeline("image-classification", model="google/vit-base-patch16-224")
+
+# ----------------------------
+# Fungsi deteksi dengan ensemble
+# ----------------------------
+def detect_image(image):
+    results = []
+
+    # Model 1
+    res1 = model1(image)[0]
+    label1, score1 = res1['label'].lower(), res1['score']
+    label1_final = 'human' if 'person' in label1 or 'human' in label1 else 'ai'
+    results.append((label1_final, score1))
+
+    # Model 2
+    res2 = model2(image)[0]
+    label2, score2 = res2['label'].lower(), res2['score']
+    label2_final = 'human' if 'person' in label2 or 'human' in label2 else 'ai'
+    results.append((label2_final, score2))
+
+    # ----------------------------
+    # Voting + threshold
+    # ----------------------------
+    votes = [r[0] for r in results]
+    final_label = max(set(votes), key=votes.count)
+
+    # Rata-rata confidence untuk label final
+    relevant_scores = [s for (l, s) in results if l == final_label]
+    avg_confidence = np.mean(relevant_scores) * 100
+
+    # Tentukan hasil akhir
+    if avg_confidence < 80:
+        final_result = "⚠️ Tidak Pasti (cek manual)"
+    elif final_label == 'human':
+        final_result = f"✅ Foto Asli ({avg_confidence:.2f}%)"
+    else:
+        final_result = f"🤖 AI Detected ({avg_confidence:.2f}%)"
+
+    # Detail per model
+    output = f"""
 ### Hasil Deteksi:
-{final}
+{final_result}
 
-**Model AI-detector:** {label1} ({conf1}%)
-**Model General (ViT):** {label2} ({conf2}%)
+**Model 1 (ResNet-50):** {label1} ({score1*100:.2f}%)
+**Model 2 (ViT):** {label2} ({score2*100:.2f}%)
 """
-        return output
-    except Exception as e:
-        return f"Terjadi error: {str(e)}"
+    return output
 
-# UI Gradio
+# ----------------------------
+# Gradio Interface
+# ----------------------------
 iface = gr.Interface(
     fn=detect_image,
     inputs=gr.Image(type="pil"),
     outputs="markdown",
-    title="AI vs Real Image Detector",
-    description="Upload foto untuk mendeteksi apakah gambar kemungkinan besar asli atau hasil AI."
+    title="AI vs Foto Asli Detector",
+    description="Unggah gambar, sistem akan mendeteksi apakah gambar kemungkinan besar asli atau dihasilkan AI menggunakan ensemble 2 model publik + voting."
 )
 
 if __name__ == "__main__":