Update app.py

app.py

@@ -1,49 +1,119 @@
 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
-detector = pipeline("image-classification", model="umm-maybe/AI-image-detector")
+# ----------------------------
+# 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 tambahan general classifier (backup)
-general = pipeline("image-classification", model="google/vit-base-patch16-224")
+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)
 
-def detect_image(img):
+# ----------------------------
+# 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):
     try:
-        result1 = detector(img)
-        label1 = result1[0]['label']
-        conf1 = round(result1[0]['score'] * 100, 2)
-
-        result2 = general(img)
-        label2 = result2[0]['label']
-        conf2 = round(result2[0]['score'] * 100, 2)
-
-        # Logika baru tanpa cek manual
-        if "fake" in label1.lower() or "artificial" in label1.lower():
-            final = f"⚠️ Gambar ini kemungkinan besar dihasilkan AI ({conf1}%)"
-        elif "human" in label1.lower() and conf1 < 95:
-            final = f"⚠️ Gambar ini kemungkinan besar dihasilkan AI ({100-conf1:.2f}%)"
-        else:
-            final = f"✅ Gambar ini kemungkinan besar asli ({conf1}%)"
-
-        output = f"""
+        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
+# ----------------------------
+def detect_image(image):
+    hf_score = 0
+    if hf_detector:
+        try:
+            result = hf_detector(image)
+            label = result[0]['label']
+            conf = result[0]['score'] * 100
+            if "fake" in label.lower() or "artificial" in label.lower():
+                hf_score = conf
+            elif "human" in label.lower():
+                hf_score = 100 - conf  # human confidence rendah → kemungkinan AI
+        except:
+            hf_score = 0
+
+    general_score = 0
+    if general_model:
+        try:
+            result2 = general_model(image)
+            label2 = result2[0]['label']
+            conf2 = result2[0]['score'] * 100
+            if any(x in label2.lower() for x in ["anime","cartoon","illustration","3d","maya"]):
+                general_score = conf2
+        except:
+            general_score = 0
+
+    # Analisis lokal
+    blur_score = calculate_blur(image)
+    noise_score = calculate_noise(image)
+    exif_present = has_camera_exif(image)
+    local_score = 0
+    if blur_score < 100 or noise_score < 10:
+        local_score += 50
+    if not exif_present:
+        local_score += 10
+
+    # Weighted score
+    weighted_score = hf_score*0.6 + general_score*0.2 + local_score*0.2
+    weighted_score = min(max(weighted_score, 0), 100)  # clamp 0-100
+
+    # Output final
+    if weighted_score >= 95:
+        final_result = f"⚠️ Gambar ini hasil AI ({weighted_score:.2f}%)"
+    elif weighted_score <= 5:
+        final_result = f"✅ Gambar ini asli ({100-weighted_score:.2f}%)"
+    else:
+        final_result = f"AI: {weighted_score:.2f}%, Asli: {100-weighted_score:.2f}%"
+
+    output = f"""
 ### Hasil Deteksi:
-{final}
+{final_result}
 
-**Model AI-detector:** {label1} ({conf1}%)
-**Model General (ViT):** {label2} ({conf2}%)
+HF AI-detector: {label if hf_detector else 'N/A'} ({conf if hf_detector else 0:.2f}%)
+General Model: {label2 if general_model else 'N/A'} ({conf2 if general_model else 0:.2f}%)
+Blur Score: {blur_score:.2f}
+Noise Score: {noise_score:.2f}
+Metadata Kamera: {'Ada' if exif_present else 'Tidak Ada'}
 """
-        return output
-    except Exception as e:
-        return f"Terjadi error: {str(e)}"
+    return output
 
-# UI Gradio
+# ----------------------------
+# Gradio Interface
+# ----------------------------
 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 Foto Asli Detector (Hybrid)",
+    description="Unggah gambar, sistem akan mendeteksi apakah gambar kemungkinan besar asli atau dihasilkan AI."
 )
 
 if __name__ == "__main__":