Update app.py

app.py

@@ -1,67 +1,61 @@
 import gradio as gr
-from transformers import pipeline
 from PIL import Image
+from transformers import pipeline
 
-# === Load 3 model publik khusus AI vs Real ===
-model1 = pipeline("image-classification", model="prithivMLmods/AI-vs-Deepfake-vs-Real")
-model2 = pipeline("image-classification", model="dima806/ai_vs_real_image_detection")
-model3 = pipeline("image-classification", model="Hemg/AI-VS-REAL-IMAGE-DETECTION")
+# Load 3 model Hugging Face
+model1 = pipeline("image-classification", model="prithivMLmods/deepfake-vs-real-image-detection")
+model2 = pipeline("image-classification", model="dima806/ai-image-detector")
+model3 = pipeline("image-classification", model="Hemg/A-real-and-fake-image-detection")
 
 def detect_image(img: Image.Image):
     results = {}
 
-    # Model 1
+    # Prediksi masing-masing model
     res1 = model1(img)
-    results["Model 1 (prithivMLmods)"] = res1
-
-    # Model 2
     res2 = model2(img)
-    results["Model 2 (dima806)"] = res2
-
-    # Model 3
     res3 = model3(img)
-    results["Model 3 (Hemg)"] = res3
 
-    # --- Ensemble dengan threshold agar lebih stabil ---
-    ai_score, real_score = 0, 0
-    for res in [res1, res2, res3]:
-        for item in res:
-            label = item["label"].lower()
-            score = item["score"]
-            if "real" in label:
-                real_score += score
-            elif "fake" in label or "deepfake" in label or "artificial" in label:
-                ai_score += score
+    # Ambil skor Real/Artificial sesuai output
+    score1_real = next((r["score"] for r in res1 if "real" in r["label"].lower()), 0)
+    score1_fake = next((r["score"] for r in res1 if "fake" in r["label"].lower() or "artificial" in r["label"].lower()), 0)
+
+    score2_real = next((r["score"] for r in res2 if "real" in r["label"].lower()), 0)
+    score2_fake = next((r["score"] for r in res2 if "fake" in r["label"].lower()), 0)
+
+    score3_real = next((r["score"] for r in res3 if "real" in r["label"].lower()), 0)
+    score3_fake = next((r["score"] for r in res3 if "fake" in r["label"].lower()), 0)
 
-    # Normalisasi
-    total = ai_score + real_score + 1e-9
-    ai_percent = (ai_score / total) * 100
-    real_percent = (real_score / total) * 100
+    # Voting: jika 2 model yakin REAL (>0.5), maka final REAL
+    votes_real = sum([score1_real > 0.5, score2_real > 0.5, score3_real > 0.5])
 
-    # Keputusan akhir dengan threshold
-    if real_percent > ai_percent and real_percent > 55:  
-        verdict = f"✅ Foto Asli ({real_percent:.2f}%)"
-    elif ai_percent > real_percent and ai_percent > 55:
-        verdict = f"⚠️ AI Generated ({ai_percent:.2f}%)"
+    if votes_real >= 2:
+        final_label = "✅ Foto Asli"
     else:
-        verdict = f"⚠️ Tidak Pasti (Real {real_percent:.2f}%, AI {ai_percent:.2f}%)"
+        # Weighted ensemble (Hemg lebih berat)
+        weighted_real = (0.25 * score1_real) + (0.25 * score2_real) + (0.5 * score3_real)
+        weighted_fake = (0.25 * score1_fake) + (0.25 * score2_fake) + (0.5 * score3_fake)
 
-    # Format output
-    output = f"## 📊 Ringkasan Deteksi\n"
-    for name, res in results.items():
-        output += f"\n🔹 **{name}**: {res}\n"
+        if weighted_real >= weighted_fake:
+            final_label = "✅ Foto Asli"
+        else:
+            final_label = "❌ Foto AI / Hasil Buatan"
 
-    output += f"\n=== 🧠 ENSEMBLE HASIL AKHIR ===\n{verdict}\n"
+    # Tampilkan hasil detail
+    results["Model1"] = res1
+    results["Model2"] = res2
+    results["Model3"] = res3
+    results["Final"] = final_label
 
-    return output
+    return results
 
-iface = gr.Interface(
+# Interface Gradio
+demo = gr.Interface(
     fn=detect_image,
     inputs=gr.Image(type="pil"),
-    outputs="markdown",
-    title="Deteksi AI vs Foto Asli (Ensemble 3 Model Publik)",
-    description="Menggunakan 3 model publik Hugging Face (prithivMLmods, dima806, Hemg) dengan threshold untuk mendeteksi apakah gambar AI-generated atau asli."
+    outputs="json",
+    title="Deteksi Foto Asli vs AI",
+    description="Menggabungkan 3 model Hugging Face dengan hybrid voting + bobot."
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()