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 tanpa "Tidak Pasti"
# ----------------------------
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 atau ambil confidence tertinggi
    # ----------------------------
    votes = [r[0] for r in results]
    if votes[0] == votes[1]:
        final_label = votes[0]  # mayoritas sama
        final_conf = np.mean([r[1] for r in results if r[0]==final_label])*100
    else:
        # ambil model dengan confidence tertinggi
        if results[0][1] > results[1][1]:
            final_label = results[0][0]
            final_conf = results[0][1]*100
        else:
            final_label = results[1][0]
            final_conf = results[1][1]*100

    # ----------------------------
    # Hasil akhir
    # ----------------------------
    if final_label == 'human':
        final_result = f"✅ Foto Asli ({final_conf:.2f}%)"
    else:
        final_result = f"🤖 AI Detected ({final_conf:.2f}%)"

    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 AI atau Foto Asli berdasarkan 2 model publik."
)

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