Update app.py

app.py

@@ -1,54 +1,55 @@
 import gradio as gr
 from transformers import pipeline
-
-# Daftar model publik yang relatif stabil
-model_ids = [
-    "umm-maybe/AI-image-detector",
-    "falconsai/nsfw_image_detection",   # meski nsfw, output-nya bermanfaat untuk fitur visual
-    "microsoft/resnet-50"               # model general untuk referensi real-world image
-]
-
-# Load semua model
-detectors = [pipeline("image-classification", model=m) for m in model_ids]
-
-def detect_image(image):
-    combined_scores = {}
-    results_text = []
-
-    # Jalankan semua model
-    for i, detector in enumerate(detectors):
-        preds = detector(image)
-        results_text.append(f"Model {i+1} ({model_ids[i]}):")
-        for p in preds:
-            label = p["label"]
-            score = float(p["score"])
-            results_text.append(f"  {label}: {round(score*100, 2)}%")
-
-            # Gabungkan skor
-            if label not in combined_scores:
-                combined_scores[label] = []
-            combined_scores[label].append(score)
-
-    # Hitung rata-rata dari semua model
-    final_scores = {label: sum(scores)/len(scores) for label, scores in combined_scores.items()}
-
-    # Cari label dengan skor tertinggi
-    best_label = max(final_scores, key=final_scores.get)
-    best_score = round(final_scores[best_label]*100, 2)
-
-    results_text.append("\n=== ENSEMBLE HASIL AKHIR ===")
-    results_text.append(f"{best_label} ({best_score}%)")
-
-    return "\n".join(results_text)
-
-# Gradio interface
-iface = gr.Interface(
+from PIL import Image
+import numpy as np
+
+# Model 1: Hugging Face AI Image Detector
+detector_hf = pipeline("image-classification", model="falconsai/nsfw_image_detection")
+
+# Model 2: ViT (ImageNet classification)
+detector_vit = pipeline("image-classification", model="google/vit-base-patch16-224")
+
+# Model 3: ResNet50
+detector_resnet = pipeline("image-classification", model="microsoft/resnet-50")
+
+def detect_image(img):
+    results = {}
+
+    # Model 1
+    try:
+        out1 = detector_hf(img)
+        results["HF AI-Detector"] = out1
+    except Exception as e:
+        results["HF AI-Detector"] = str(e)
+
+    # Model 2
+    try:
+        out2 = detector_vit(img)
+        results["ViT Model"] = out2
+    except Exception as e:
+        results["ViT Model"] = str(e)
+
+    # Model 3
+    try:
+        out3 = detector_resnet(img)
+        results["ResNet50"] = out3
+    except Exception as e:
+        results["ResNet50"] = str(e)
+
+    # Gabungkan skor sederhana
+    summary = "📊 Ringkasan Deteksi:\n"
+    for k, v in results.items():
+        summary += f"\n🔹 {k}: {v}\n"
+
+    return summary
+
+demo = gr.Interface(
     fn=detect_image,
     inputs=gr.Image(type="pil"),
     outputs="text",
-    title="AI vs Real Image Detector (3-Model Ensemble)",
-    description="Menggabungkan 3 model publik untuk meningkatkan akurasi deteksi gambar AI vs asli."
+    title="🖼️ Multi-Model AI Image Detector",
+    description="Menggunakan 3 model (HF Detector, ViT, ResNet) untuk mendeteksi apakah gambar AI-generated atau asli."
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()