Update app.py

app.py

@@ -1,19 +1,32 @@
 import gradio as gr
-from PIL import Image, ImageStat, ExifTags
+from transformers import pipeline
+from PIL import Image, ExifTags
 import numpy as np
 import cv2
 
 # ----------------------------
-# Fungsi hitung sharpness / blur
+# Model Hugging Face AI detector (public)
 # ----------------------------
-def calculate_blur(image):
-    image_cv = np.array(image.convert("L"))  # konversi ke grayscale
-    laplacian_var = cv2.Laplacian(image_cv, cv2.CV_64F).var()
-    return laplacian_var
+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)
 
 # ----------------------------
-# Fungsi deteksi metadata
+# Analisis gambar 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)
+    return noise_std
+
 def has_camera_exif(image):
     try:
         exif = image._getexif()
@@ -27,25 +40,58 @@ def has_camera_exif(image):
     return False
 
 # ----------------------------
-# Fungsi prediksi AI vs Real
+# Deteksi hybrid AI vs Foto Asli
 # ----------------------------
 def detect_image(image):
+    scores = []
+
+    # ---- HUGGING FACE ----
+    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
+            else:
+                scores.append(0)  # Foto asli = 0 AI score
+        except:
+            scores.append(0)
+    else:
+        scores.append(0)
+
+    # ---- ANALISIS LOKAL ----
     blur_score = calculate_blur(image)
+    noise_score = calculate_noise(image)
     exif_present = has_camera_exif(image)
-    
-    # Threshold empiris (bisa disesuaikan)
-    # blur rendah + metadata → Foto Asli
-    # blur tinggi + tidak ada metadata → AI
-    if exif_present or blur_score > 100:
-        final_result = f"✅ Foto Asli"
+
+    # 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
+    if not exif_present:
+        local_ai_score += 10  # sedikit penalti jika metadata hilang
+
+    scores.append(local_ai_score)
+
+    # ---- Gabungkan skor ----
+    avg_score = sum(scores) / len(scores)
+
+    # ---- Tentukan hasil final ----
+    if avg_score > 50:
+        final_result = "🤖 AI Detected"
     else:
-        final_result = f"🤖 AI Detected"
-    
+        final_result = "✅ Foto Asli"
+
     output = f"""
 ### Hasil Deteksi:
 {final_result}
 
+**Skor rata-rata AI:** {avg_score:.2f}
 **Blur Score:** {blur_score:.2f}
+**Noise Score:** {noise_score:.2f}
 **Metadata Kamera:** {'Ada' if exif_present else 'Tidak Ada'}
 """
     return output
@@ -57,8 +103,8 @@ iface = gr.Interface(
     fn=detect_image,
     inputs=gr.Image(type="pil"),
     outputs="markdown",
-    title="AI vs Foto Asli Detector (Gratis)",
-    description="Unggah gambar, sistem akan mendeteksi apakah gambar kemungkinan besar asli atau dihasilkan AI berdasarkan analisis blur dan metadata."
+    title="AI vs Foto Asli Detector (Hybrid, Gratis)",
+    description="Unggah gambar, sistem hybrid akan mendeteksi apakah gambar kemungkinan besar asli atau dihasilkan AI."
 )
 
 if __name__ == "__main__":