Update app.py

app.py

@@ -2,132 +2,80 @@ import gradio as gr
 from transformers import pipeline
 from PIL import Image, ExifTags
 import numpy as np
+import cv2
+import io
 
-# Model HuggingFace
-detector = pipeline("image-classification", model="umm-maybe/AI-image-detector")
-general = pipeline("image-classification", model="google/vit-base-patch16-224")
-
-# --- Analisis tambahan ---
-
-def estimate_blur(img):
-    gray = img.convert("L")
-    arr = np.array(gray)
-    gx, gy = np.gradient(arr)
-    return np.var(gx + gy)
+# 1. Load model AI detector dari HuggingFace
+detector = pipeline("image-classification", model="microsoft/resnet-50")
 
+# Fungsi hitung noise (variansi Laplacian)
 def estimate_noise(img):
-    gray = img.convert("L")
-    arr = np.array(gray)
-    return np.var(arr - np.mean(arr))
-
-def jpeg_artifacts(img):
-    # konversi ke numpy
-    arr = np.array(img.convert("L"))
-    # ambil perbedaan antar piksel
-    diff = np.abs(arr[:, 1:] - arr[:, :-1])
-    return np.mean(diff)
+    gray = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2GRAY)
+    return cv2.Laplacian(gray, cv2.CV_64F).var()
 
-def edge_inconsistency(img):
-    gray = img.convert("L")
-    arr = np.array(gray)
-    gx, gy = np.gradient(arr)
-    edges = np.sqrt(gx**2 + gy**2)
-    return np.std(edges)
-
-def has_exif(img):
+# Fungsi cek metadata kamera
+def has_camera_metadata(img):
     try:
-        exif_data = img._getexif()
-        if exif_data:
-            return True, {ExifTags.TAGS.get(k, k): v for k, v in exif_data.items()}
-        return False, {}
+        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:
-        return False, {}
-
-# --- Penyesuaian AI score ---
-def adjust_ai_score(img, ai_score):
-    blur = estimate_blur(img)
-    noise = estimate_noise(img)
-    jpeg = jpeg_artifacts(img)
-    edge_std = edge_inconsistency(img)
-    has_meta, meta = has_exif(img)
-
-    adjustment = 0
-
-    # Metadata kamera sangat penting
-    if not has_meta:
-        adjustment += 15
-
-    # Blur terlalu kecil → terlalu tajam → AI photorealistic
-    if blur < 80:
-        adjustment += 10
-
-    # Noise terlalu rendah → gambar terlalu bersih → AI
-    if noise < 50:
-        adjustment += 10
-
-    # JPEG artifact rendah → AI cenderung hasil render
-    if jpeg < 5:
-        adjustment += 10
-
-    # Edge terlalu konsisten → AI
-    if edge_std < 20:
-        adjustment += 10
+        pass
+    return False
 
-    ai_score = min(ai_score + adjustment, 100)
-    return ai_score, blur, noise, jpeg, edge_std, has_meta
-
-# --- Deteksi utama ---
+# Fungsi utama deteksi hybrid
 def detect_image(img):
-    try:
-        # Prediksi AI
-        result1 = detector(img)
-        label1 = result1[0]['label'].lower()
-        conf1 = round(result1[0]['score'] * 100, 2)
-
-        # Prediksi general
-        result2 = general(img)
-        label2 = result2[0]['label']
-        conf2 = round(result2[0]['score'] * 100, 2)
-
-        # Hitung AI %
-        if "artificial" in label1 or "fake" in label1:
-            ai_percent = conf1
-        elif "human" in label1:
-            ai_percent = 100 - conf1
-        else:
-            ai_percent = conf1
-
-        # Penyesuaian tambahan
-        ai_percent, blur, noise, jpeg, edge_std, has_meta = adjust_ai_score(img, ai_percent)
-        real_percent = round(100 - ai_percent, 2)
-
-        meta_status = "Ada" if has_meta else "Tidak"
-
-        return f"""
-### 🔍 Hasil Deteksi:
-🖼️ Gambar ini {ai_percent:.2f}% AI / {real_percent:.2f}% Asli
-
-**Model AI-detector:** {label1} ({conf1}%)
-**Model General (ViT):** {label2} ({conf2}%)
-
-**Analisis Kamera & Teknis:**
-- Blur Score: {blur:.2f}
-- Noise Score: {noise:.2f}
-- JPEG Artifact Level: {jpeg:.2f}
-- Edge Consistency (STD): {edge_std:.2f}
-- Metadata Kamera: {meta_status}
-"""
-    except Exception as e:
-        return f"⚠️ Error: {str(e)}"
-
-# UI Gradio
-iface = gr.Interface(
+    # 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
+
+    # Hybrid Adjustment
+    # 1. Metadata kamera → tambah bobot real
+    if has_camera_metadata(img):
+        real_score += 30
+        ai_score -= 30
+
+    # 2. Noise → kalau noise tinggi berarti asli
+    noise = estimate_noise(img)
+    if noise > 500:  # ambang noise
+        real_score += 20
+        ai_score -= 20
+
+    # 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%"
+    else:
+        label = f"🖼️ Gambar ini {ai_score:.2f}% AI / {real_score:.2f}% Asli"
+
+    return label, f"Noise Score: {noise:.2f} | Metadata Kamera: {'Ada' if has_camera_metadata(img) else 'Tidak'}"
+
+# Gradio UI
+demo = gr.Interface(
     fn=detect_image,
     inputs=gr.Image(type="pil"),
-    outputs="markdown",
-    title="Ultimate AI vs Real Detector",
-    description="Detektor gratis untuk membedakan foto asli vs AI photorealistic. Memakai model HuggingFace + analisis metadata kamera, blur, noise, artefak JPEG, dan pola tepi."
+    outputs=[gr.Textbox(label="Hasil Deteksi"), gr.Textbox(label="Analisis Teknis")],
+    title="Hybrid AI vs Real Image Detector"
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    demo.launch()