Update app.py

app.py

@@ -1,124 +1,71 @@
 import gradio as gr
 from transformers import pipeline
-from PIL import Image, ExifTags
-import numpy as np
-import cv2
+from PIL import Image
 
-# ----------------------------
-# MODEL
-# ----------------------------
-try:
-    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 utama untuk deteksi AI
+detector = pipeline("image-classification", model="umm-maybe/AI-image-detector")  
 
-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)
+# Model tambahan general (backup)
+general = pipeline("image-classification", model="google/vit-base-patch16-224")
 
-# ----------------------------
-# ANALISIS LOKAL
-# ----------------------------
-def calculate_blur(image):
-    gray = np.array(image.convert("L"))
-    return cv2.Laplacian(gray, cv2.CV_64F).var()
-
-def calculate_noise(image):
-    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):
+def detect_image(img):
     try:
-        exif = image._getexif()
-        if exif:
-            for tag, value in exif.items():
-                decoded = ExifTags.TAGS.get(tag, tag)
-                if decoded in ["Make", "Model"]:
-                    return True
-    except:
-        return False
-    return False
-
-# ----------------------------
-# DETEKSI HYBRID DENGAN PERSENTASE
-# ----------------------------
-def detect_image(image):
-    hf_score = 0
-    general_score = 0
-    local_score = 0
-
-    # -------- HF AI-detector --------
-    hf_label, hf_conf = "N/A", 0
-    if hf_detector:
-        try:
-            result = hf_detector(image)
-            hf_label = result[0]['label']
-            hf_conf = result[0]['score'] * 100
-            # Jika label mengandung tanda AI → skor AI = confidence
-            if any(x in hf_label.lower() for x in ["fake", "ai", "artificial"]):
-                hf_score = hf_conf
-        except:
-            hf_score = 0
-
-    # -------- General model --------
-    general_label, general_conf = "N/A", 0
-    if general_model:
-        try:
-            result2 = general_model(image)
-            general_label = result2[0]['label']
-            general_conf = result2[0]['score'] * 100
-            if any(x in general_label.lower() for x in ["anime","cartoon","illustration","maya","3d"]):
-                general_score = general_conf * 0.7  # dikurangi bobot karena cadangan
-        except:
-            general_score = 0
-
-    # -------- Analisis lokal --------
-    blur_score = calculate_blur(image)
-    noise_score = calculate_noise(image)
-    exif_present = has_camera_exif(image)
-
-    if blur_score < 100 or noise_score < 10:
-        local_score += 50
-    if not exif_present:
-        local_score += 10
-
-    # -------- Weighted Score --------
-    total_ai_score = hf_score*0.7 + general_score*0.2 + local_score*0.1
-    total_ai_score = min(max(total_ai_score, 0), 100)  # clamp 0–100
-    total_real_score = 100 - total_ai_score
-
-    # -------- Output --------
-    if total_ai_score == 100:
-        final_text = "🤖 Gambar ini hasil AI"
-    elif total_real_score == 100:
-        final_text = "✅ Gambar ini asli"
-    else:
-        final_text = f"🖼️ {total_ai_score:.2f}% AI / {total_real_score:.2f}% Asli"
-
-    output_lines = [
-        f"### Hasil Deteksi:\n{final_text}",
-        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)
+        # Prediksi dengan AI detector
+        result1 = detector(img)
+        label1 = result1[0]['label'].lower()
+        conf1 = result1[0]['score'] * 100
+
+        # Prediksi dengan model general
+        result2 = general(img)
+        conf2 = result2[0]['score'] * 100
+
+        # Kombinasi skor untuk hybrid
+        # Logika: AI detector > 50% dianggap AI, <50% dianggap asli
+        if "fake" in label1 or "artificial" in label1:
+            ai_score = conf1
+            real_score = 100 - ai_score
+        elif "human" in label1 or "real" in label1:
+            real_score = conf1
+            ai_score = 100 - real_score
+        else:
+            # fallback jika label tidak spesifik
+            ai_score = conf1 / 2
+            real_score = 100 - ai_score
+
+        # Sensitivitas tambahan: jika AI photorealistic tapi AI score rendah
+        # gunakan hasil general model sebagai penyesuaian
+        ai_score += conf2 * 0.1
+        real_score = 100 - ai_score
+
+        # Batasi 0-100
+        ai_score = max(0, min(100, round(ai_score, 2)))
+        real_score = max(0, min(100, round(real_score, 2)))
+
+        if ai_score == 100:
+            final = "🖼️ Gambar ini 100% Hasil AI"
+        elif real_score == 100:
+            final = "🖼️ Gambar ini 100% Asli"
+        else:
+            final = f"🖼️ Gambar ini {ai_score}% AI / {real_score}% Asli"
+
+        output = f"""
+### Hasil Deteksi:
+{final}
+
+**Model AI-detector:** {label1} ({round(conf1,2)}%)
+**Model General (ViT):** {result2[0]['label']} ({round(conf2,2)}%)
+"""
+        return output
+
+    except Exception as e:
+        return f"Terjadi error: {str(e)}"
 
-# ----------------------------
-# Gradio Interface
-# ----------------------------
 iface = gr.Interface(
     fn=detect_image,
     inputs=gr.Image(type="pil"),
     outputs="markdown",
-    title="Hybrid AI vs Foto Asli Detector (Gratis)",
-    description="Unggah gambar, sistem akan mendeteksi persentase AI dan persentase asli. Tidak ada kategori tidak pasti."
+    title="AI vs Real Image Detector Hybrid",
+    description="Upload foto untuk mendeteksi persentase kemungkinan AI vs Asli."
 )
 
 if __name__ == "__main__":