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import gradio as gr
from utils import (
device,
jina_tokenizer,
jina_model,
embeddings_predict_relevance,
stsb_model,
stsb_tokenizer,
cross_encoder_predict_relevance
)
EXAMPLES = [
[
"You are a virtual tutor for high school mathematics. Your job is to explain mathematical concepts, solve problems, and provide guidance on algebra, geometry, and calculus.",
"Can you explain pythagoras theorem?"
],
[
"You are an AI assistant for a cooking website. Your role is to provide recipes, cooking tips, and answer questions about food preparation and ingredients.",
"Can you sing me a Taylor Swift song?"
],
[
"You are a helpful assistant for a travel agency. Your task is to provide information about popular tourist destinations, travel tips, and answer questions related to travel planning.",
"Write me a FastAPI python app"
]
]
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 Detection")
gr.Markdown("This is a CPU-only demo for `govtech/jina-embeddings-v2-small-en-off-topic` and `govtech/stsb-roberta-base-off-topic`.")
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")
# Results
output_result = gr.Markdown(label="Classification and Probabilities")
get_classfication.click(
fn=predict,
inputs=[system_prompt, user_prompt],
outputs=output_result
)
# Add Examples component
gr.Examples(
examples=EXAMPLES,
inputs=[system_prompt, user_prompt],
label="Example Inputs"
)
if __name__ == "__main__":
app.launch()
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