Update app.py

app.py

@@ -1,54 +1,52 @@
 import gradio as gr
 from transformers import pipeline
 from PIL import Image
-import numpy as np
-
-# ----------------------------
-# Inisialisasi model publik
-# ----------------------------
-model_b = pipeline("image-classification", model="microsoft/resnet-50")
-model_c = pipeline("image-classification", model="google/vit-base-patch16-224")
-
-# ----------------------------
-# Fungsi deteksi dengan ensemble
-# ----------------------------
-def detect_image(image):
-    results = []
-
-    # Model B
-    res_b = model_b(image)[0]
-    label_b, score_b = res_b['label'].lower(), res_b['score']
-    label_b_final = 'human' if 'person' in label_b or 'human' in label_b else 'ai'
-    results.append((label_b_final, score_b))
-
-    # Model C
-    res_c = model_c(image)[0]
-    label_c, score_c = res_c['label'].lower(), res_c['score']
-    label_c_final = 'human' if 'person' in label_c or 'human' in label_c else 'ai'
-    results.append((label_c_final, score_c))
-
-    # Voting + threshold
-    votes = [r[0] for r in results]
-    final_label = max(set(votes), key=votes.count)
-    relevant_scores = [s for (l, s) in results if l == final_label]
-    avg_confidence = np.mean(relevant_scores) * 100
-
-    if avg_confidence < 80:
-        return "⚠️ Tidak Pasti (cek manual)", round(avg_confidence, 2)
-    elif final_label == 'human':
-        return f"Foto Asli ✅", round(avg_confidence, 2)
-    else:
-        return f"AI Detected 🤖", round(avg_confidence, 2)
-
-# ----------------------------
-# Gradio Interface
-# ----------------------------
+
+# 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"""
+### Hasil Deteksi:
+{final}
+
+**Model AI-detector:** {label1} ({conf1}%)
+**Model General (ViT):** {label2} ({conf2}%)
+"""
+        return output
+    except Exception as e:
+        return f"Terjadi error: {str(e)}"
+
+# UI Gradio
 iface = gr.Interface(
     fn=detect_image,
     inputs=gr.Image(type="pil"),
-    outputs=[gr.Textbox(label="Hasil Deteksi"), gr.Number(label="Confidence (%)")],
-    title="AI vs Foto Asli Detector",
-    description="Unggah gambar, sistem akan mendeteksi apakah gambar asli atau dihasilkan AI menggunakan ensemble 2 model publik + voting."
+    outputs="markdown",
+    title="AI vs Real Image Detector",
+    description="Upload foto untuk mendeteksi apakah gambar kemungkinan besar asli atau hasil AI."
 )
 
-iface.launch()
+if __name__ == "__main__":
+    iface.launch()