app.py

import gradio as gr

from utils import (
    device,
    jina_tokenizer,
    jina_model,
    embeddings_predict_relevance,
    stsb_model,
    stsb_tokenizer,
    cross_encoder_predict_relevance
)

def predict(system_prompt, user_prompt):
    predicted_label_jina, probabilities_jina = embeddings_predict_relevance(system_prompt, user_prompt, jina_model, jina_tokenizer, device)
    predicted_label_stsb, probabilities_stsb = cross_encoder_predict_relevance(system_prompt, user_prompt, stsb_model, stsb_tokenizer, device)

    result = f"""
    **Prediction Summary**

    **1. Model: jinaai/jina-embeddings-v2-small-en**
    - **Prediction**: {"🟥 Off-topic" if predicted_label_jina==1 else "🟩 On-topic"}
    - **Probability of being off-topic**: {probabilities_jina[0][1]:.2%}

    **2. Model: cross-encoder/stsb-roberta-base**
    - **Prediction**: {"🟥 Off-topic" if predicted_label_stsb==1 else "🟩 On-topic"}
    - **Probability of being off-topic**: {probabilities_stsb[0][1]:.2%}
    """

    return result

with gr.Blocks(theme=gr.themes.Soft(), fill_height=True) as app:

    gr.Markdown("# Off-Topic Classification using Fine-tuned Embeddings and Cross-Encoder Models")

    with gr.Row():
        system_prompt = gr.TextArea(label="System Prompt", lines=5)
        user_prompt = gr.TextArea(label="User Prompt", lines=5)

    # Button to run the prediction
    get_classfication = gr.Button("Check Content")

    output_result = gr.Markdown(label="Classification and Probabilities")

    get_classfication.click(
        fn=predict,
        inputs=[system_prompt, user_prompt],
        outputs=output_result
    )

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