Update app.py

app.py

@@ -5,26 +5,33 @@ import numpy as np
 import cv2
 
 # ----------------------------
-# Model Hugging Face AI detector (public)
+# MODEL
 # ----------------------------
+
+# Model AI-detector (Hugging Face, public)
 try:
     hf_detector = pipeline("image-classification", model="umm-maybe/AI-image-detector")
 except Exception as e:
     hf_detector = None
-    print("Hugging Face model gagal dimuat, hanya analisis lokal yang dipakai:", e)
+    print("HF AI-detector gagal dimuat:", e)
+
+# Model general classifier (backup)
+try:
+    general_model = pipeline("image-classification", model="google/vit-base-patch16-224")
+except Exception as e:
+    general_model = None
+    print("General classifier gagal dimuat:", e)
 
 # ----------------------------
-# Analisis gambar lokal
+# ANALISIS LOKAL
 # ----------------------------
 def calculate_blur(image):
     gray = np.array(image.convert("L"))
     return cv2.Laplacian(gray, cv2.CV_64F).var()
 
 def calculate_noise(image):
-    img_gray = np.array(image.convert("L"), dtype=np.float32)
-    h, w = img_gray.shape
-    mean = np.mean(img_gray)
-    noise_std = np.std(img_gray - mean)
+    gray = np.array(image.convert("L"), dtype=np.float32)
+    noise_std = np.std(gray - np.mean(gray))
     return noise_std
 
 def has_camera_exif(image):
@@ -40,47 +47,58 @@ def has_camera_exif(image):
     return False
 
 # ----------------------------
-# Deteksi hybrid AI vs Foto Asli
+# DETEKSI HYBRID
 # ----------------------------
 def detect_image(image):
     scores = []
 
-    # ---- HUGGING FACE ----
+    # -------- HF AI-detector --------
     if hf_detector:
         try:
             result = hf_detector(image)
             label = result[0]['label'].lower()
             conf = result[0]['score'] * 100
             if "fake" in label or "ai" in label or "artificial" in label:
-                scores.append(conf)  # AI score
+                scores.append(conf)  # skor AI
+            else:
+                scores.append(0)
+        except:
+            scores.append(0)
+    else:
+        scores.append(0)
+
+    # -------- General model --------
+    if general_model:
+        try:
+            result2 = general_model(image)
+            label2 = result2[0]['label'].lower()
+            conf2 = result2[0]['score'] * 100
+            # Logika sederhana: label aneh / tidak realistis dianggap AI
+            if any(x in label2 for x in ["abaya", "cartoon", "anime", "illustration"]):
+                scores.append(conf2)
             else:
-                scores.append(0)  # Foto asli = 0 AI score
+                scores.append(0)
         except:
             scores.append(0)
     else:
         scores.append(0)
 
-    # ---- ANALISIS LOKAL ----
+    # -------- Analisis lokal --------
     blur_score = calculate_blur(image)
     noise_score = calculate_noise(image)
     exif_present = has_camera_exif(image)
 
-    # Blur & noise heuristics
-    # Foto asli: blur tinggi (>100) atau noise > threshold
-    # AI: blur rendah atau noise sangat rendah
     local_ai_score = 0
     if blur_score < 100 or noise_score < 10:
-        local_ai_score += 50  # menambah skor AI
+        local_ai_score += 50
     if not exif_present:
-        local_ai_score += 10  # sedikit penalti jika metadata hilang
-
+        local_ai_score += 10
     scores.append(local_ai_score)
 
-    # ---- Gabungkan skor ----
+    # -------- Rata-rata skor & threshold --------
     avg_score = sum(scores) / len(scores)
 
-    # ---- Tentukan hasil final ----
-    if avg_score > 50:
+    if avg_score > 60:
         final_result = "🤖 AI Detected"
     else:
         final_result = "✅ Foto Asli"
@@ -90,6 +108,8 @@ def detect_image(image):
 {final_result}
 
 **Skor rata-rata AI:** {avg_score:.2f}
+**Model AI-detector:** {result[0]['label']} ({result[0]['score']*100:.2f}%)
+**Model General:** {result2[0]['label']} ({result2[0]['score']*100:.2f}%)
 **Blur Score:** {blur_score:.2f}
 **Noise Score:** {noise_score:.2f}
 **Metadata Kamera:** {'Ada' if exif_present else 'Tidak Ada'}
@@ -103,7 +123,7 @@ iface = gr.Interface(
     fn=detect_image,
     inputs=gr.Image(type="pil"),
     outputs="markdown",
-    title="AI vs Foto Asli Detector (Hybrid, Gratis)",
+    title="AI vs Foto Asli Detector (Hybrid + Voting)",
     description="Unggah gambar, sistem hybrid akan mendeteksi apakah gambar kemungkinan besar asli atau dihasilkan AI."
 )