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import torch
from transformers import BertForSequenceClassification, BertTokenizer
from safetensors.torch import load_file
import gradio as gr
# Load model dan tokenizer
model_path = "model (5).safetensors"
state_dict = load_file(model_path)
model = BertForSequenceClassification.from_pretrained('indobenchmark/indobert-base-p2', num_labels=3)
tokenizer = BertTokenizer.from_pretrained('indobenchmark/indobert-base-p2')
model.load_state_dict(state_dict, strict=False)
model.eval() # Set model ke mode evaluasi
# Fungsi deteksi stres dengan model
def detect_stress(input_text):
# Tokenisasi input teks
inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True, max_length=128)
# Inference
with torch.no_grad():
outputs = model(**inputs)
# Mengambil prediksi
logits = outputs.logits
predicted_class = torch.argmax(logits, dim=1).item()
# Label, warna, dan pesan berdasarkan tingkat stres
labels = {
0: ("Tidak Stres", "#8BC34A", "Saat ini anda tidak mengalami stres. Tetap jaga kesehatan Anda!"),
1: ("Stres Ringan", "#FF7F00", "Saat ini anda sedang mengalami stres ringan. Luangkan waktu untuk relaksasi."),
2: ("Stres Berat", "#F44336", "Saat ini anda sedang mengalami stres berat. Mohon untuk segera melakukan konsultasi.")
}
level, color, message = labels[predicted_class]
return f"<div style='color: white; background-color: {color}; padding: 10px; border-radius: 5px;'>" \
f"Level stress anda : {level}<br>{message}" \
f"</div>"
with gr.Blocks(css="""
body {
background-color: black;
color: white;
font-family: Arial, sans-serif;
}
.gradio-container {
width: 100%;
max-width: 600px;
margin: 0 auto;
text-align: center;
}
#title {
background-color: #ff7a33;
padding: 20px;
font-size: 24px;
font-weight: bold;
}
textarea {
background-color: #3a3a3a;
color: white;
border: none;
border-radius: 5px;
padding: 5px;
font-size: 14px;
}
textarea:focus {
border-color: #ff7a33 !important;
}
.button_detect {
background-color: #ff7a33;
color: white;
border: none;
border-radius: 5px;
width: 20px;
height: 50px;
font-size: 14px;
cursor: pointer;
}
.button_detect:hover {
background-color: #e5662c;
}
""") as demo:
with gr.Row():
gr.Markdown("<h1 id='title'>Stress Detector</h1>")
with gr.Row():
input_text = gr.Textbox(label="Masukkan teks", placeholder="Ceritakan keluhanmu disini...", lines=3)
# Tombol submit
with gr.Row():
btn_submit = gr.Button("Deteksi", elem_classes ="button_detect")
with gr.Row():
output_label = gr.HTML(label="Hasil Deteksi")
# Interaksi Gradio
btn_submit.click(fn=detect_stress, inputs=input_text, outputs=output_label)
# Jalankan demo
demo.launch()
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