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import gradio as gr | |
from utils import ( | |
device, | |
jina_tokenizer, | |
jina_model, | |
embeddings_predict_relevance, | |
stsb_model, | |
stsb_tokenizer, | |
ms_model, | |
ms_tokenizer, | |
cross_encoder_predict_relevance | |
) | |
def predict(system_prompt, user_prompt, selected_model): | |
if selected_model == "jinaai/jina-embeddings-v2-small-en": | |
predicted_label, probabilities = embeddings_predict_relevance(system_prompt, user_prompt, jina_model, jina_tokenizer, device) | |
elif selected_model == "cross-encoder/stsb-roberta-base": | |
predicted_label, probabilities = cross_encoder_predict_relevance(system_prompt, user_prompt, stsb_model, stsb_tokenizer, device) | |
elif selected_model == "cross-encoder/ms-marco-MiniLM-L-6-v2": | |
predicted_label, probabilities = cross_encoder_predict_relevance(system_prompt, user_prompt, ms_model, ms_tokenizer, device) | |
probability_off_topic = probabilities[0][1] * 100 | |
label = "Off-topic" if predicted_label==1 else "On-topic" | |
result = f""" | |
**Prediction Summary**: | |
- **Predicted Label**: {label} | |
- **Probability of Off-topic**: {probability_off_topic:.3f}% | |
""" | |
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.Textbox(label="System Prompt") | |
user_prompt = gr.Textbox(label="User Prompt") | |
with gr.Row(): | |
selected_model = gr.Dropdown( | |
["jinaai/jina-embeddings-v2-small-en", | |
"cross-encoder/stsb-roberta-base", | |
"cross-encoder/ms-marco-MiniLM-L-6-v2"], | |
label="Select a model") | |
# 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, selected_model], | |
outputs=output_result | |
) | |
if __name__ == "__main__": | |
app.launch() | |