Update app.py

app.py

@@ -1,85 +1,65 @@
 import gradio as gr
 from transformers import pipeline
 from PIL import Image
-import cv2
-import numpy as np
 
-# =========================
-# Preprocessing function
-# =========================
-def preprocess_image(img_path):
-    img = cv2.imread(img_path)
-    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-    img = cv2.resize(img, (512, 512))  
-    img = cv2.GaussianBlur(img, (3, 3), 0)  # Kurangi noise
-    return Image.fromarray(img)
+# === Load 3 model publik khusus AI vs Real ===
+model1 = pipeline("image-classification", model="prithivMLmods/AI-vs-Deepfake-vs-Real")
+model2 = pipeline("image-classification", model="dima806/ai_vs_real_image_detection")
+model3 = pipeline("image-classification", model="Hemg/AI-VS-REAL-IMAGE-DETECTION")
 
-# =========================
-# Load models
-# =========================
-detector_ai = pipeline("image-classification", model="umm-maybe/AI-image-detector")
-detector_resnet = pipeline("image-classification", model="microsoft/resnet-50")
+def detect_image(img: Image.Image):
+    results = {}
 
-# =========================
-# Detection Function
-# =========================
-def detect_image(img):
-    # Preprocess
-    img = preprocess_image(img)
+    # Model 1
+    res1 = model1(img)
+    results["Model 1 (prithivMLmods)"] = res1
 
-    # Run AI detector
-    ai_results = detector_ai(img)
-    ai_dict = {res['label']: res['score'] for res in ai_results}
+    # Model 2
+    res2 = model2(img)
+    results["Model 2 (dima806)"] = res2
 
-    human_score = ai_dict.get("human", 0)
-    artificial_score = ai_dict.get("artificial", 0)
+    # Model 3
+    res3 = model3(img)
+    results["Model 3 (Hemg)"] = res3
 
-    # Run ResNet for natural object cross-check
-    resnet_results = detector_resnet(img)
-    top_resnet = resnet_results[0]  # ambil label dengan skor tertinggi
+    # --- Ensemble sederhana ---
+    ai_score, real_score = 0, 0
+    for res in [res1, res2, res3]:
+        top = res[0]
+        label = top["label"].lower()
+        score = top["score"]
+        if "real" in label:
+            real_score += score
+        else:
+            ai_score += score
 
-    # =========================
-    # Decision Rule with Threshold
-    # =========================
-    if artificial_score > 0.75:
-        verdict = "🟣 AI-generated"
-    elif human_score > 0.65:
-        verdict = "🟢 Foto Asli"
-    else:
-        verdict = "⚠️ Tidak Pasti"
-
-    # Extra check: kalau AI-detector bilang AI tapi ResNet yakin objek nyata
-    if verdict == "🟣 AI-generated" and top_resnet['score'] > 0.70:
-        verdict = "⚠️ Tidak Pasti (deteksi objek nyata tinggi)"
+    # Normalisasi
+    total = ai_score + real_score + 1e-9
+    ai_percent = (ai_score / total) * 100
+    real_percent = (real_score / total) * 100
 
-    # =========================
-    # Output
-    # =========================
-    summary = f"""
-📊 Ringkasan Deteksi:
+    # Keputusan akhir
+    if real_percent > ai_percent:
+        verdict = f"✅ Foto Asli ({real_percent:.2f}%)"
+    else:
+        verdict = f"⚠️ AI Generated ({ai_percent:.2f}%)"
 
-🔹 AI Detector:
-   - Human: {human_score:.2%}
-   - Artificial: {artificial_score:.2%}
+    # Format output
+    output = f"## 📊 Ringkasan Deteksi\n"
+    for name, res in results.items():
+        output += f"\n🔹 **{name}**: {res}\n"
 
-🔹 ResNet50:
-   - Top Label: {top_resnet['label']} ({top_resnet['score']:.2%})
+    output += f"\n=== 🧠 ENSEMBLE HASIL AKHIR ===\n{verdict}\n"
 
-=== HASIL AKHIR ===
-{verdict}
-"""
-    return summary
+    return output
 
-# =========================
-# Gradio App
-# =========================
-demo = gr.Interface(
+iface = gr.Interface(
     fn=detect_image,
-    inputs=gr.Image(type="filepath"),
-    outputs="text",
-    title="Deteksi AI vs Foto Asli",
-    description="Upload foto untuk mendeteksi apakah gambar AI-generated atau Foto Asli. Menggunakan threshold + preprocessing."
+    inputs=gr.Image(type="pil"),
+    outputs="markdown",
+    title="Deteksi AI vs Foto Asli (Ensemble 3 Model Publik)",
+    description="Menggunakan 3 model publik Hugging Face (prithivMLmods, dima806, Hemg) untuk mendeteksi apakah gambar AI-generated atau asli."
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    iface.launch()