app.py

import gradio as gr
from transformers import pipeline
from PIL import Image
import numpy as np

# ----------------------------
# Inisialisasi model publik
# ----------------------------
model1 = pipeline("image-classification", model="microsoft/resnet-50")
model2 = pipeline("image-classification", model="google/vit-base-patch16-224")

# ----------------------------
# Fungsi deteksi dengan ensemble
# ----------------------------
def detect_image(image):
    results = []

    # Model 1
    res1 = model1(image)[0]
    label1, score1 = res1['label'].lower(), res1['score']
    label1_final = 'human' if 'person' in label1 or 'human' in label1 else 'ai'
    results.append((label1_final, score1))

    # Model 2
    res2 = model2(image)[0]
    label2, score2 = res2['label'].lower(), res2['score']
    label2_final = 'human' if 'person' in label2 or 'human' in label2 else 'ai'
    results.append((label2_final, score2))

    # ----------------------------
    # Voting + threshold
    # ----------------------------
    votes = [r[0] for r in results]
    final_label = max(set(votes), key=votes.count)

    # Rata-rata confidence untuk label final
    relevant_scores = [s for (l, s) in results if l == final_label]
    avg_confidence = np.mean(relevant_scores) * 100

    # Tentukan hasil akhir
    if avg_confidence < 80:
        final_result = "⚠️ Tidak Pasti (cek manual)"
    elif final_label == 'human':
        final_result = f"✅ Foto Asli ({avg_confidence:.2f}%)"
    else:
        final_result = f"🤖 AI Detected ({avg_confidence:.2f}%)"

    # Detail per model
    output = f"""
### Hasil Deteksi:
{final_result}

**Model 1 (ResNet-50):** {label1} ({score1*100:.2f}%)
**Model 2 (ViT):** {label2} ({score2*100:.2f}%)
"""
    return output

# ----------------------------
# Gradio Interface
# ----------------------------
iface = gr.Interface(
    fn=detect_image,
    inputs=gr.Image(type="pil"),
    outputs="markdown",
    title="AI vs Foto Asli Detector",
    description="Unggah gambar, sistem akan mendeteksi apakah gambar kemungkinan besar asli atau dihasilkan AI menggunakan ensemble 2 model publik + voting."
)

if __name__ == "__main__":
    iface.launch()