app.py

import torch
from transformers import BertForSequenceClassification, BertTokenizer
from safetensors.torch import load_file
import gradio as gr

model_path = "/kaggle/input/model_12k/other/default/1/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()

def detect_stress(input_text):
    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True, max_length=128)

    with torch.no_grad():
        outputs = model(**inputs)

    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

    labels = {
        0: ("Not Stress", "#8BC34A", "Currently you are not experiencing stress. Stay on top of your health!"),
        1: ("Mild Stress", "#FF7F00", "Saat ini anda sedang mengalami stres ringan. Luangkan waktu untuk relaksasi."),
        2: ("High Stress", "#F44336", "Currently you are experiencing mild stress. Take time to relax.")
    }

    level, color, message = labels[predicted_class]
    return f"<div style='background-color:{color}; color:white; text-align:center; padding:15px; border-radius:10px; font-size:16px; heigth:200px; width: 500px; margin:auto;'>" \
           f"Level stres Anda: {level}<br>{message}" \
           f"</div>"

# Apabila menggunakan model SVM atau ensemble learning
# pipeline = joblib.load("/kaggle/input/svm_model/other/default/1/svm_hybrid_pipeline.pkl")

# def detect_stress(input_text):
#     predicted_class = pipeline.predict([input_text])[0]
#     probs = pipeline.predict_proba([input_text])[0]
#     confidence = max(probs)
    
#     labels = {
#         0: ("Not Stress", "#8BC34A", "Currently you are not experiencing stress. Stay on top of your health!"),
#         1: ("Mild Stress", "#FF7F00", "Saat ini anda sedang mengalami stres ringan. Luangkan waktu untuk relaksasi."),
#         2: ("High Stress", "#F44336", "Currently you are experiencing mild stress. Take time to relax.")
#     }

#     level, color, message = labels[predicted_class]
#     return f"<div style='background-color:{color}; color:white; text-align:center; padding:15px; border-radius:10px; font-size:16px; heigth:200px; width: 500px; margin:auto;'>" \
#            f"Level stress anda : {level}<br>{message}" \
#            f"</div>"

custom_css = """
body {
    margin: 0;
    padding: 0;
    font-family: Arial, sans-serif;
    background-color: var(--background);
    color: var(--text);
    transition: background-color 0.3s, color 0.3s;
}

#title {
    position: fixed;
    top: 0;
    left: 0;
    width: 100vw;
    padding: 20px;
    background-color: #ff7a33;
    color: white;
    font-size: 28px;
    font-weight: bold;
    text-align: center;
    z-index: 1000;
}
body {
    padding-top: 80px;
}

#container {
    display: flex;
    flex-direction: column;
    align-items: center;
    justify-content: center;
    min-height: calc(100vh - 80px);
    padding: 20px;
}

textarea {
    background-color: var(--textarea-bg);
    color: var(--textarea-text);
    border: none;
    border-radius: 5px;
    padding: 10px;
    font-size: 16px;
    box-sizing: border-box;
    resize: none;
}
textarea:focus {
    outline: 2px solid #ff7a33;
}

.button_detect {
    background-color: #ff7a33;
    color: white;
    border: none;
    border-radius: 5px;
    padding: 15px 30px;
    font-size: 16px;
    cursor: pointer;
    margin-top: 10px;
    width: 200px;
    heigth: 100px;
    
}
.button_detect:hover {
    background-color: #e5662c;
}

@media (prefers-color-scheme: dark) {
    :root {
        --background: #121212;
        --text: white;
        --textarea-bg: #2c2c2c;
        --textarea-text: white;
    }
}
@media (prefers-color-scheme: light) {
    :root {
        --background: #ffffff;
        --text: black;
        --textarea-bg: #f0f0f0;
        --textarea-text: black;
    }
}
"""

# UI Layout
with gr.Blocks(css=custom_css) as demo:
    gr.HTML("<div id='title'>Stress Detector</div>")  # Banner on top

    with gr.Column(elem_id="container"):
        input_text = gr.Textbox(
            label="Input text",
            placeholder="Tell us your complaint here...",
            lines=5
        )
        btn_submit = gr.Button("Detect", elem_classes=["button_detect"])
        output_label = gr.HTML(label="Detection Results")

        btn_submit.click(fn=detect_stress, inputs=input_text, outputs=output_label)

demo.launch()