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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()
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