Update app.py

app.py

@@ -1,24 +1,8 @@
 import gradio as gr
-from transformers import pipeline
 from PIL import Image, ExifTags
 import numpy as np
 import cv2
 
-# ----------------------------
-# MODEL DETEKSI AI REALISTIS
-# ----------------------------
-try:
-    hf_detector = pipeline("image-classification", model="danielgatis/rembg-image-ai-detector")
-except Exception as e:
-    hf_detector = None
-    print("HF AI-detector gagal dimuat:", e)
-
-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)
-
 # ----------------------------
 # ANALISIS LOKAL
 # ----------------------------
@@ -43,86 +27,68 @@ def has_camera_exif(image):
         return False
     return False
 
+# -------- FFT ANALYSIS --------
+def high_freq_artifacts(image):
+    gray = np.array(image.convert("L"), dtype=np.float32)
+    f = np.fft.fft2(gray)
+    fshift = np.fft.fftshift(f)
+    magnitude_spectrum = np.abs(fshift)
+    # rasio energi high frequency vs total
+    rows, cols = gray.shape
+    crow, ccol = rows // 2 , cols // 2
+    # buat mask low freq
+    r = min(crow, ccol) // 4
+    mask = np.zeros((rows, cols))
+    mask[crow-r:crow+r, ccol-r:ccol+r] = 1
+    low_energy = np.sum(magnitude_spectrum * mask)
+    total_energy = np.sum(magnitude_spectrum)
+    high_ratio = (total_energy - low_energy) / total_energy * 100
+    return high_ratio
+
 # ----------------------------
-# DETEKSI HYBRID
+# DETEKSI AI HYBRID
 # ----------------------------
 def detect_image(image: Image.Image):
     output_lines = []
 
-    # -------- HF AI-detector --------
-    hf_score = 0
-    hf_label = "N/A"
-    hf_conf = 0
-    if hf_detector:
-        try:
-            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 ["ai", "synthetic", "generated"]):
-                hf_score = hf_conf
-        except:
-            hf_score = 0
-
-    if hf_score > 40:  # threshold lebih sensitif
-        final_result = "🤖 AI Detected"
-        weighted_score = hf_score
-        output_lines.append(f"### Hasil Deteksi:\n{final_result}")
-        output_lines.append(f"HF AI-detector: {hf_label} ({hf_conf:.2f}%)")
-        return "\n".join(output_lines)
-
-    # -------- General model --------
-    general_score = 0
-    general_label = "N/A"
-    general_conf = 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
-        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 < 80 or noise_score < 8:
-        local_score += 30  # lebih ringan
+    high_freq_score = high_freq_artifacts(image)
+
+    # Weighted scoring
+    score = 0
+    if blur_score < 80:
+        score += 20
+    if noise_score < 8:
+        score += 20
     if not exif_present:
-        local_score += 10
-
-    # -------- Weighted Score --------
-    weighted_score = general_score*0.7 + local_score*0.3
+        score += 10
+    if high_freq_score > 25:  # banyak high freq artifacts = AI
+        score += 50
 
-    if weighted_score > 50:
+    if score > 40:
         final_result = "🤖 AI Detected"
     else:
         final_result = "✅ Foto Asli"
 
-    # -------- Output --------
     output_lines.append(f"### Hasil Deteksi:\n{final_result}")
-    output_lines.append(f"Weighted Skor: {weighted_score:.2f}")
-    output_lines.append(f"HF AI-detector: {hf_label} ({hf_conf:.2f}%)")
-    output_lines.append(f"General Model: {general_label} ({general_conf:.2f}%)")
+    output_lines.append(f"Weighted Skor: {score:.2f}")
     output_lines.append(f"Blur Score: {blur_score:.2f}")
     output_lines.append(f"Noise Score: {noise_score:.2f}")
+    output_lines.append(f"High-Freq Artifacts Score: {high_freq_score:.2f}")
     output_lines.append(f"Metadata Kamera: {'Ada' if exif_present else 'Tidak Ada'}")
 
     return "\n".join(output_lines)
 
 # ----------------------------
-# Gradio 5.x Interface
+# GRADIO INTERFACE
 # ----------------------------
-with gr.Blocks(title="AI Realistic Detector (Gratis & Akurat)") as demo:
+with gr.Blocks(title="Hybrid AI Realistic Detector (Gratis)") as demo:
     gr.Markdown("Unggah gambar, sistem akan mendeteksi apakah gambar kemungkinan besar asli atau dihasilkan AI.")
-    
     with gr.Row():
         img_input = gr.Image(type="pil", label="Unggah Gambar")
         output_md = gr.Markdown(label="Hasil Deteksi")
-    
     detect_btn = gr.Button("Deteksi")
     detect_btn.click(fn=detect_image, inputs=img_input, outputs=output_md)