Update app.py

app.py

@@ -1,89 +1,91 @@
 import gradio as gr
 from transformers import pipeline
-from PIL import Image, ExifTags, ImageStat, ImageFilter
+from PIL import Image
+from PIL.ExifTags import TAGS
 import numpy as np
 
-# Model utama untuk deteksi AI
+# Model utama deteksi AI
 detector = pipeline("image-classification", model="umm-maybe/AI-image-detector")
-
-# Model tambahan general classifier (backup)
+# Model general classifier (backup)
 general = pipeline("image-classification", model="google/vit-base-patch16-224")
 
-def analyze_noise(img):
-    gray = img.convert("L")
-    arr = np.array(gray)
-    return np.std(arr)  # Standar deviasi → noise
-
-def analyze_blur(img):
-    gray = img.convert("L")
-    arr = np.array(gray)
-    lap = cv2.Laplacian(arr, cv2.CV_64F).var()
-    return lap  # Variansi Laplacian → blur
-
-def metadata_score(img):
+def get_exif_metadata(img):
+    """Cek metadata kamera untuk membantu deteksi foto asli"""
     try:
-        exif = img._getexif()
-        if exif is None:
-            return 1  # Tidak ada metadata → kemungkinan AI
-        for tag, value in exif.items():
-            decoded = ExifTags.TAGS.get(tag, tag)
-            if decoded == "Make" or decoded == "Model":
-                return 0  # Ada kamera → kemungkinan asli
-        return 1
+        exif_data = img._getexif()
+        if exif_data is None:
+            return False
+        for tag_id, value in exif_data.items():
+            tag = TAGS.get(tag_id, tag_id)
+            if tag in ["Make", "Model", "Software"]:
+                return True
+        return False
     except:
-        return 1
+        return False
 
-def detect_image(img: Image.Image):
+def detect_image(img):
     try:
-        # Prediksi AI-detector
+        # Prediksi dengan AI-detector
         result1 = detector(img)
         label1 = result1[0]['label']
-        conf1 = result1[0]['score']  # 0-1
+        conf1 = result1[0]['score'] * 100
 
-        # Prediksi general model
+        # Prediksi dengan model general
         result2 = general(img)
         label2 = result2[0]['label']
-        conf2 = result2[0]['score']
+        conf2 = result2[0]['score'] * 100
 
-        # Analisis blur & noise
-        noise = analyze_noise(img)
-        try:
-            import cv2
-            blur = analyze_blur(img)
-        except:
-            blur = 0
+        # Cek metadata kamera
+        has_metadata = get_exif_metadata(img)
 
-        # Metadata
-        meta = metadata_score(img)
+        # Weighted hybrid scoring
+        # Skema: AI-detector 50%, Metadata 30%, general model 20%
+        ai_score = conf1 if "artificial" in label1.lower() or "fake" in label1.lower() else 0
+        human_score = conf1 if "human" in label1.lower() or "real" in label1.lower() else 0
 
-        # Weighted skor akhir (lebih sensitif terhadap AI photorealistic)
-        ai_score = conf1 * 0.6 + meta * 0.2 + (1 - min(noise/100,1)) * 0.1 + (1 - min(blur/1000,1)) * 0.1
-        ai_score = min(max(ai_score, 0), 1)  # Clamp 0-1
-        human_score = 1 - ai_score
+        if has_metadata:
+            human_score += 30  # boost jika metadata ada
+        ai_score = min(ai_score, 100)
+        human_score = min(human_score, 100)
 
-        ai_percent = round(ai_score * 100, 2)
-        human_percent = round(human_score * 100, 2)
+        # Normalisasi agar total = 100%
+        total = ai_score + human_score
+        if total == 0:
+            ai_percent = 50
+            human_percent = 50
+        else:
+            ai_percent = round((ai_score / total) * 100, 2)
+            human_percent = round((human_score / total) * 100, 2)
 
+        # Tentukan hasil akhir
+        if ai_percent == 100:
+            final_text = "🖼️ Gambar ini 100% AI"
+        elif human_percent == 100:
+            final_text = "🖼️ Gambar ini asli 100%"
+        else:
+            final_text = f"🖼️ Gambar ini {ai_percent}% AI / {human_percent}% Asli"
+
+        # Tambahkan info model
         output = f"""
 ### Hasil Deteksi:
-🖼️ Gambar ini {ai_percent}% AI / {human_percent}% Asli
+{final_text}
 
-**Model AI-detector:** {label1} ({round(conf1*100,2)}%)  
-**Model General (ViT):** {label2} ({round(conf2*100,2)}%)  
-**Blur Score:** {round(blur,2)}  
-**Noise Score:** {round(noise,2)}  
-**Metadata Kamera:** {"Ada" if meta==0 else "Tidak Ada"}
+**Model AI-detector:** {label1} ({conf1:.2f}%)
+**Model General (ViT):** {label2} ({conf2:.2f}%)
+**Metadata Kamera Ada:** {"Ya" if has_metadata else "Tidak"}
 """
         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="markdown",
-    title="Hybrid AI vs Foto Asli Detector",
-    description="Upload foto untuk mendeteksi persentase AI vs foto asli. Lebih sensitif terhadap AI photorealistic."
+    title="Hybrid AI vs Real Image Detector",
+    description="Upload gambar, sistem akan mendeteksi persentase AI vs foto asli, sensitif untuk AI photorealistic tapi tetap akurat untuk foto asli."
 )
 
 if __name__ == "__main__":