Update app.py

app.py

@@ -10,6 +10,22 @@ from utils import (
     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)
@@ -31,7 +47,7 @@ def predict(system_prompt, user_prompt):
 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`")
+    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)
@@ -40,6 +56,13 @@ with gr.Blocks(theme=gr.themes.Soft(), fill_height=True) as app:
     # Button to run the prediction
     get_classfication = gr.Button("Check Content")
 
+    # Add Examples component
+    gr.Examples(
+        examples=EXAMPLES,
+        inputs=[system_prompt, user_prompt],
+        label="Example Inputs"
+    )
+    
     output_result = gr.Markdown(label="Classification and Probabilities")
 
     get_classfication.click(