Update app.py

app.py

@@ -5,76 +5,70 @@ import numpy as np
 import cv2
 import io
 
-# 1. Load model AI detector dari HuggingFace
-detector = pipeline("image-classification", model="microsoft/resnet-50")
+# Load HuggingFace pipeline
+detector = pipeline("image-classification", model="falconsai/nsfw_image_detection")  
+# ganti model ke yang support binary classification real vs ai kalau mau training
 
-# Fungsi hitung noise (variansi Laplacian)
-def estimate_noise(img):
-    gray = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2GRAY)
-    return cv2.Laplacian(gray, cv2.CV_64F).var()
+def analyze_image(image):
+    # Konversi ke format OpenCV
+    img_bytes = io.BytesIO()
+    image.save(img_bytes, format="PNG")
+    img_bytes = np.frombuffer(img_bytes.getvalue(), dtype=np.uint8)
+    cv_img = cv2.imdecode(img_bytes, cv2.IMREAD_COLOR)
 
-# Fungsi cek metadata kamera
-def has_camera_metadata(img):
-    try:
-        exif = img._getexif()
-        if exif is not None:
-            for tag, value in exif.items():
-                tag_name = ExifTags.TAGS.get(tag, tag)
-                if "Model" in tag_name or "Make" in tag_name:
-                    return True
-    except:
-        pass
-    return False
+    # Blur score (laplacian variance)
+    gray = cv2.cvtColor(cv_img, cv2.COLOR_BGR2GRAY)
+    blur_score = cv2.Laplacian(gray, cv2.CV_64F).var()
 
-# Fungsi utama deteksi hybrid
-def detect_image(img):
-    # Resize ke 224x224 untuk model
-    img_resized = img.resize((224,224))
-    
-    # Prediksi AI-detector HuggingFace
-    preds = detector(img_resized)
-    ai_score = 0
-    for p in preds:
-        if "artificial" in p['label'].lower() or "fake" in p['label'].lower():
-            ai_score += p['score'] * 100
-        elif "human" in p['label'].lower() or "real" in p['label'].lower():
-            ai_score += (1 - p['score']) * 100
-    
-    ai_score = max(0, min(100, ai_score))  # jaga range
-    real_score = 100 - ai_score
+    # Noise score (std dev)
+    noise_score = np.std(gray)
 
-    # Hybrid Adjustment
-    # 1. Metadata kamera → tambah bobot real
-    if has_camera_metadata(img):
-        real_score += 30
-        ai_score -= 30
+    # Metadata Kamera
+    meta = image.getexif()
+    meta_info = "Ada" if meta else "Tidak Ada"
 
-    # 2. Noise → kalau noise tinggi berarti asli
-    noise = estimate_noise(img)
-    if noise > 500:  # ambang noise
-        real_score += 20
-        ai_score -= 20
+    # Prediksi AI vs Asli (pakai HF pipeline dummy dulu)
+    preds = detector(image)
+    label = preds[0]["label"].lower()
+    score = preds[0]["score"]
 
-    # Normalisasi agar tetap 0-100
-    real_score = max(0, min(100, real_score))
-    ai_score = 100 - real_score
-
-    # Final Output
-    if real_score == 100:
-        label = "🖼️ Gambar ini ASLI 100%"
-    elif ai_score == 100:
-        label = "🖼️ Gambar ini HASIL AI 100%"
+    # Normalisasi ke persentase AI vs Asli
+    if "artificial" in label or "fake" in label or "ai" in label:
+        ai_prob = score * 100
     else:
-        label = f"🖼️ Gambar ini {ai_score:.2f}% AI / {real_score:.2f}% Asli"
+        ai_prob = (1 - score) * 100
+    real_prob = 100 - ai_prob
+
+    # Koreksi dengan metadata → jika metadata kamera ada, naikkan skor real
+    if meta_info == "Ada":
+        real_prob += 15
+        ai_prob -= 15
+
+    # Koreksi blur/noise → foto asli biasanya lebih natural
+    if blur_score > 500 and noise_score > 20:
+        real_prob += 5
+
+    # Clamp ke 0-100
+    ai_prob = max(0, min(100, ai_prob))
+    real_prob = max(0, min(100, real_prob))
+
+    # Output
+    hasil = f"""🖼️ Hasil Deteksi:
+    {ai_prob:.2f}% AI / {real_prob:.2f}% Asli
+
+    Blur Score: {blur_score:.2f}
+    Noise Score: {noise_score:.2f}
+    Metadata Kamera: {meta_info}
+    """
 
-    return label, f"Noise Score: {noise:.2f} | Metadata Kamera: {'Ada' if has_camera_metadata(img) else 'Tidak'}"
+    return hasil
 
-# Gradio UI
 demo = gr.Interface(
-    fn=detect_image,
+    fn=analyze_image,
     inputs=gr.Image(type="pil"),
-    outputs=[gr.Textbox(label="Hasil Deteksi"), gr.Textbox(label="Analisis Teknis")],
-    title="Hybrid AI vs Real Image Detector"
+    outputs="text",
+    title="AI vs Real Image Detector",
+    description="Deteksi apakah gambar AI-generated atau asli. Gratis, tanpa API berbayar."
 )
 
 if __name__ == "__main__":