Update app.py

app.py

@@ -5,25 +5,22 @@ import numpy as np
 import cv2
 
 # ----------------------------
-# Model AI-detector (gratis)
+# MODEL
 # ----------------------------
 try:
-    ai_detector = pipeline("image-classification", model="umm-maybe/AI-image-detector")
-except:
-    ai_detector = None
-    print("HF AI-detector gagal dimuat")
+    hf_detector = pipeline("image-classification", model="umm-maybe/AI-image-detector")
+except Exception as e:
+    hf_detector = None
+    print("HF AI-detector gagal dimuat:", e)
 
-# ----------------------------
-# Model General (backup)
-# ----------------------------
 try:
     general_model = pipeline("image-classification", model="google/vit-base-patch16-224")
-except:
+except Exception as e:
     general_model = None
-    print("General classifier gagal dimuat")
+    print("General classifier gagal dimuat:", e)
 
 # ----------------------------
-# Analisis kamera / lokal
+# ANALISIS LOKAL
 # ----------------------------
 def calculate_blur(image):
     gray = np.array(image.convert("L"))
@@ -47,26 +44,26 @@ def has_camera_exif(image):
     return False
 
 # ----------------------------
-# Deteksi hybrid
+# DETEKSI HYBRID
 # ----------------------------
 def detect_image(image):
-    output_lines = []
-
-    # ---- AI-detector ----
-    ai_score = 0
-    ai_label = "N/A"
-    ai_conf = 0
-    if ai_detector:
+    # Prediksi HF AI-detector
+    hf_score = 0
+    hf_label = "N/A"
+    hf_conf = 0
+    if hf_detector:
         try:
-            result = ai_detector(image)
-            ai_label = result[0]['label']
-            ai_conf = result[0]['score'] * 100
-            if any(x in ai_label.lower() for x in ["fake", "ai", "artificial"]):
-                ai_score = ai_conf
+            result = hf_detector(image)
+            hf_label = result[0]['label']
+            hf_conf = result[0]['score'] * 100
+            if any(x in hf_label.lower() for x in ["fake", "ai", "artificial"]):
+                hf_score = hf_conf
+            else:
+                hf_score = 100 - hf_conf  # jika human, kurangi
         except:
-            ai_score = 0
+            hf_score = 0
 
-    # ---- General model ----
+    # Prediksi general model
     general_score = 0
     general_label = "N/A"
     general_conf = 0
@@ -80,41 +77,41 @@ def detect_image(image):
         except:
             general_score = 0
 
-    # ---- Analisis kamera ----
+    # Analisis lokal
     blur_score = calculate_blur(image)
     noise_score = calculate_noise(image)
     exif_present = has_camera_exif(image)
-    camera_score = 0
-    if blur_score > 100:  # Foto asli biasanya lebih fokus
-        camera_score += 40
-    if noise_score > 15:  # Sensor noise alami
-        camera_score += 30
-    if exif_present:      # Ada EXIF
-        camera_score += 30
+    local_score = 0
+
+    # Blur rendah atau noise rendah → kemungkinan AI
+    if blur_score < 100 or noise_score < 10:
+        local_score += 50
+    # Tidak ada metadata kamera → kemungkinan AI
+    if not exif_present:
+        local_score += 40
+    else:
+        local_score -= 20  # ada metadata kamera → lebih asli
 
-    # ---- Weighted hybrid score ----
-    weighted_ai = ai_score * 0.6 + general_score * 0.2
-    weighted_camera = camera_score
-    final_score = weighted_camera - weighted_ai  # Positif = asli, Negatif = AI
+    # Weighted hybrid
+    weighted_score = hf_score*0.4 + general_score*0.2 + local_score*0.4
 
-    if final_score >= 95:
-        final_result = "✅ Gambar ini asli"
-    elif final_score <= -95:
-        final_result = "🤖 Gambar ini hasil AI"
+    # Konversi ke persen AI / Asli
+    if weighted_score >= 95:
+        final_result = "🖼️ Gambar ini hasil AI (100%)"
+    elif weighted_score <= 5:
+        final_result = "🖼️ Gambar ini asli (100%)"
     else:
-        # Persentase
-        if final_score > 0:
-            final_result = f"✅ Gambar ini {min(round(final_score,2),100)}% asli"
-        else:
-            final_result = f"🤖 Gambar ini {min(round(-final_score,2),100)}% hasil AI"
+        final_result = f"🖼️ Gambar ini {round(weighted_score,2)}% AI / {round(100-weighted_score,2)}% Asli"
 
-    # ---- Output ----
-    output_lines.append(f"### Hasil Deteksi:\n{final_result}")
-    output_lines.append(f"AI-detector: {ai_label} ({ai_conf:.2f}%)")
-    output_lines.append(f"General Model: {general_label} ({general_conf:.2f}%)")
-    output_lines.append(f"Blur Score: {blur_score:.2f}")
-    output_lines.append(f"Noise Score: {noise_score:.2f}")
-    output_lines.append(f"Metadata Kamera: {'Ada' if exif_present else 'Tidak Ada'}")
+    # Output
+    output_lines = [
+        f"### Hasil Deteksi:\n{final_result}",
+        f"HF AI-detector: {hf_label} ({hf_conf:.2f}%)",
+        f"General Model: {general_label} ({general_conf:.2f}%)",
+        f"Blur Score: {blur_score:.2f}",
+        f"Noise Score: {noise_score:.2f}",
+        f"Metadata Kamera: {'Ada' if exif_present else 'Tidak Ada'}"
+    ]
 
     return "\n".join(output_lines)
 
@@ -126,7 +123,7 @@ iface = gr.Interface(
     inputs=gr.Image(type="pil"),
     outputs="markdown",
     title="AI vs Foto Asli Detector (Hybrid)",
-    description="Unggah gambar, sistem akan mendeteksi apakah gambar asli atau hasil AI (menggunakan kombinasi AI-detector dan jejak kamera)."
+    description="Unggah gambar, sistem akan mendeteksi persentase AI vs Asli."
 )
 
 if __name__ == "__main__":