Update app.py

app.py

@@ -1,52 +1,81 @@
 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")
+# ----------------------------
+# Inisialisasi model
+# ----------------------------
 
-# Model tambahan general classifier (backup)
-general = pipeline("image-classification", model="google/vit-base-patch16-224")
+# Model A: AI detector wajah
+model_a = pipeline("image-classification", model="rodrigoberriel/real-or-fake-face")
 
-def detect_image(img):
-    try:
-        # Prediksi dengan AI detector
-        result1 = detector(img)
-        label1 = result1[0]['label']
-        conf1 = round(result1[0]['score'] * 100, 2)
+# Model B: AI detector general (misal: resnet-50)
+model_b = pipeline("image-classification", model="microsoft/resnet-50")
 
-        # Prediksi dengan model general (untuk cek ganda)
-        result2 = general(img)
-        label2 = result2[0]['label']
-        conf2 = round(result2[0]['score'] * 100, 2)
+# Model C: General image classifier
+model_c = pipeline("image-classification", model="google/vit-base-patch16-224")
 
-        # 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)"
+# ----------------------------
+# Fungsi prediksi dengan ensemble
+# ----------------------------
+def detect_image(image):
+    results = []
+    
+    # Model A
+    res_a = model_a(image)[0]
+    label_a, score_a = res_a['label'].lower(), res_a['score']
+    results.append((label_a, score_a))
+    
+    # Model B
+    res_b = model_b(image)[0]
+    label_b, score_b = res_b['label'].lower(), res_b['score']
+    # Simplifikasi: jika label mirip 'human' atau 'person' = human, lainnya = AI
+    if 'human' in label_b or 'person' in label_b:
+        label_b_final = 'human'
+    else:
+        label_b_final = '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']
+    if 'human' in label_c or 'person' in label_c:
+        label_c_final = 'human'
+    else:
+        label_c_final = 'ai'
+    results.append((label_c_final, score_c))
+    
+    # ----------------------------
+    # Voting + Threshold
+    # ----------------------------
+    votes = [r[0] for r in results]
+    scores = [r[1] for r in results]
+    
+    # Mayoritas voting
+    final_label = max(set(votes), key=votes.count)
+    
+    # Rata-rata confidence untuk final label
+    relevant_scores = [s for (l, s) in results if l == final_label]
+    avg_confidence = np.mean(relevant_scores) * 100  # ke persen
+    
+    # Tentukan hasil akhir
+    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)
 
-        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
+# ----------------------------
+# 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."
+    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 3 model + voting."
 )
 
-if __name__ == "__main__":
-    iface.launch()
+iface.launch()