Update app.py

app.py

@@ -1,39 +1,107 @@
 import gradio as gr
 from transformers import pipeline
-from PIL import Image
+from PIL import Image, ExifTags
+import numpy as np
+import cv2
 
-# Model utama untuk deteksi real vs AI
+# Model utama AI-detector
 detector = pipeline("image-classification", model="umm-maybe/AI-image-detector")
 
-# Model tambahan general classifier (backup)
+# Model backup (general classifier)
 general = pipeline("image-classification", model="google/vit-base-patch16-224")
 
+def analyze_image(img_pil):
+    img = np.array(img_pil)
+
+    # Blur detection (variance of Laplacian)
+    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    blur_score = cv2.Laplacian(gray, cv2.CV_64F).var()
+
+    # Noise estimation (std deviation)
+    noise_score = np.std(gray)
+
+    # Edge consistency
+    edges = cv2.Canny(gray, 100, 200)
+    edge_score = np.std(edges)
+
+    # JPEG artifacts (kasar: std dev dari DCT block)
+    dct = cv2.dct(np.float32(gray) / 255.0)
+    jpeg_artifact = np.std(dct)
+
+    # Metadata kamera
+    metadata = "Tidak Ada"
+    try:
+        exif_data = img_pil._getexif()
+        if exif_data:
+            metadata = "Ada"
+    except:
+        pass
+
+    return blur_score, noise_score, edge_score, jpeg_artifact, metadata
+
 def detect_image(img):
     try:
-        # Prediksi dengan AI detector
+        # Prediksi AI-detector
         result1 = detector(img)
         label1 = result1[0]['label']
         conf1 = round(result1[0]['score'] * 100, 2)
 
-        # Prediksi dengan model general (untuk cek ganda)
+        # Prediksi model general
         result2 = general(img)
         label2 = result2[0]['label']
         conf2 = round(result2[0]['score'] * 100, 2)
 
-        # Logika sederhana untuk memutuskan hasil
+        # Analisis teknis
+        blur, noise, edge, jpeg_art, metadata = analyze_image(img)
+
+        # --- Voting System ---
+        ai_score = 0
+        real_score = 0
+
+        # Dari model AI-detector
         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}%)"
+            ai_score += conf1
+        elif "real" in label1.lower() or "human" in label1.lower():
+            real_score += conf1
+
+        # Dari metadata
+        if metadata == "Ada":
+            real_score += 30  # bobot ekstra untuk foto asli
         else:
-            final = f"⚠️ Tidak Pasti (cek manual)"
+            ai_score += 20
+
+        # Dari noise & blur
+        if noise > 20 and blur > 50:
+            real_score += 20
+        else:
+            ai_score += 10
+
+        # Hasil akhir
+        total = ai_score + real_score + 1e-6
+        ai_pct = round((ai_score / total) * 100, 2)
+        real_pct = round((real_score / total) * 100, 2)
+
+        if ai_pct > real_pct:
+            final = f"⚠️ Kemungkinan Besar AI Generated ({ai_pct}%)"
+        else:
+            final = f"✅ Kemungkinan Besar Foto Asli ({real_pct}%)"
 
         output = f"""
 ### Hasil Deteksi:
 {final}
 
-**Model AI-detector:** {label1} ({conf1}%)
+**Persentase AI:** {ai_pct}%  
+**Persentase Asli:** {real_pct}%
+
+**Model AI-detector:** {label1} ({conf1}%)  
 **Model General (ViT):** {label2} ({conf2}%)
+
+**Analisis Kamera & Teknis:**  
+- Blur Score: {round(blur,2)}  
+- Noise Score: {round(noise,2)}  
+- Edge Consistency (STD): {round(edge,2)}  
+- JPEG Artifact Level: {round(jpeg_art,2)}  
+- Metadata Kamera: {metadata}
 """
         return output
     except Exception as e:
@@ -44,8 +112,8 @@ 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."
+    title="AI vs Real Image Detector (Hybrid)",
+    description="Deteksi AI vs Foto Asli dengan kombinasi model + analisis teknis (blur, noise, edge, metadata)."
 )
 
 if __name__ == "__main__":