Update app.py

app.py

@@ -53,11 +53,11 @@ def find_similar_texts(model_name, input_text):
 
 # Create Gradio interface using Blocks
 with gr.Blocks() as demo:
-    gr.Markdown("## Sentence-Transformer based Patent-Abstract Search")
+    gr.Markdown("## Sentence-Transformer based AI-Generated-Patent-Abstract Search")
     with gr.Row():
         with gr.Column():
             model_selector = gr.Dropdown(choices=list(model_options.keys()), label="Chose Sentence-Transformer")
-            text_input = gr.Textbox(lines=2, placeholder="machine learning for drug dosing", label="input_text (example: machine learning for drug dosing. Remember, this is only a small selection of machine learning patents!)")
+            text_input = gr.Textbox(lines=2, placeholder="machine learning for drug dosing", label="input_text (example: machine learning for drug dosing. Remark: This is only a small number of AI generated machine learning patents!)")
             submit_button = gr.Button("search")
             
         with gr.Column():
@@ -68,7 +68,7 @@ with gr.Blocks() as demo:
 
     gr.Markdown("""
     ### Description
-    This demo app leverages several Sentence Transformer models to compute the semantic distance between user input and a small number of patent abstracts in the field of machine learning and AI.
+    This demo app leverages several Sentence Transformer models to compute the semantic distance between user input and a small number of AI generated patent abstracts in the field of machine learning and AI.
 
 - 'all-MiniLM-L6-v2': embedding size is 384. [More info](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) and [here](https://sbert.net/).
 - 'intfloat/e5-large-v2'. Text Embeddings by Weakly-Supervised Contrastive Pre-training, embedding size is 1024. [More info](https://huggingface.co/intfloat/e5-large-v2).
@@ -76,7 +76,7 @@ with gr.Blocks() as demo:
 - 'thenlper/gte-large': General Text Embeddings (GTE) model, embedding size is 1024. [More info](https://huggingface.co/thenlper/gte-large) and [here](https://arxiv.org/abs/2308.03281).
 - 'avsolatorio/GIST-large-Embedding-v0': Fine-tuned on top of the BAAI/bge-large-en-v1.5 using the MEDI dataset augmented with mined triplets from the MTEB Classification training dataset, embedding size is 1024. [More info](https://huggingface.co/avsolatorio/GIST-large-Embedding-v0) and [here](https://arxiv.org/abs/2402.16829).
 
-The patents can be viewed at [Espacenet](https://worldwide.espacenet.com/?locale=en_EP), the free onine service by the European Patent Office.
+
 
 Please note: The data used in this demo contains only a very limited subset of patent abstracts and is intended only for demonstration purposes. It does by far not cover all patents or their complete data.
     """)
@@ -84,3 +84,5 @@ Please note: The data used in this demo contains only a very limited subset of p
     text_input.submit(find_similar_texts, inputs=[model_selector, text_input], outputs=output)
 
 demo.launch()
+
+#The patents can be viewed at [Espacenet](https://worldwide.espacenet.com/?locale=en_EP), the free onine service by the European Patent Office.