Update app.py

app.py

@@ -225,26 +225,27 @@ bi_encoder_type = st.sidebar.selectbox(
 top_k = st.sidebar.slider("Number of Top Hits Generated",min_value=1,max_value=5,value=2)
 
 st.markdown(
-    """The app supports asymmetric Semantic search which seeks to improve search accuracy of documents/URL by understanding the content of the search query in contrast to traditional search engines which only find documents based on lexical matches, semantic search can also find synonyms. 
-    The idea behind semantic search is to embed all entries in your corpus, whether they be sentences, paragraphs, or documents, into a vector space. At search time, the query is embedded into the same vector space and the closest embeddings from your corpus are found. These entries should have a high semantic overlap with the query.
-    The all-* models where trained on all available training data (more than 1 billion training pairs) and are designed as general purpose models. The all-mpnet-base-v2 model provides the best quality, while all-MiniLM-L6-v2 is 5 times faster and still offers good quality. The models used have been trained on broad datasets, however, if your document/corpus is specialised, such as for science or economics, the results returned might be unsatisfactory.
+    """-The app supports asymmetric Semantic search which seeks to improve search accuracy of documents/URL by understanding the content of the search query in contrast to traditional search engines which only find documents based on lexical matches. 
+    -The idea behind semantic search is to embed all entries in your corpus, whether they be sentences, paragraphs, or documents, into a vector space. At search time, the query is embedded into the same vector space and the closest embeddings from your corpus are found. These entries should have a high semantic overlap with the query.
+    -The all-* models where trained on all available training data (more than 1 billion training pairs) and are designed as general purpose models. The all-mpnet-base-v2 model provides the best quality, while all-MiniLM-L6-v2 is 5 times faster and still offers good quality. The models used have been trained on broad datasets, however, if your document/corpus is specialised, such as for science or economics, the results returned might be unsatisfactory.""")
      
-    There models available to choose from:""")
+st.markdown("""There models available to choose from:""")
     
 st.markdown(
     """Model Source: 
-    Bi-Encoders - [multi-qa-mpnet-base-dot-v1](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1), [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2), [multi-qa-MiniLM-L6-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1) and [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
-    Cross-Encoder - [cross-encoder/ms-marco-MiniLM-L-12-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-12-v2)
+    -Bi-Encoders - [multi-qa-mpnet-base-dot-v1](https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-dot-v1), [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2), [multi-qa-MiniLM-L6-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1) and [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
+    -Cross-Encoder - [cross-encoder/ms-marco-MiniLM-L-12-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-12-v2)""")
     
-    Code and App Inspiration Source:
-    [Sentence Transformers](https://www.sbert.net/examples/applications/retrieve_rerank/README.html)
+st.markdown(
+    """Code and App Inspiration Source: [Sentence Transformers](https://www.sbert.net/examples/applications/retrieve_rerank/README.html)""")
     
-    Quick summary of the purposes of a Bi and Cross-encoder below, the image and info were adapted from [www.sbert.net](https://www.sbert.net/examples/applications/semantic-search/README.html):
     
-    Bi-Encoder (Retrieval): The Bi-encoder is responsible for independently embedding the sentences and search queries into a vector space. The result is then passed to the cross-encoder for checking the relevance/similarity between the query and sentences.
+st.markdown(    
+    """Quick summary of the purposes of a Bi and Cross-encoder below, the image and info were adapted from [www.sbert.net](https://www.sbert.net/examples/applications/semantic-search/README.html):""")
     
-    Cross-Encoder (Re-Ranker): A re-ranker based on a Cross-Encoder can substantially improve the final results for the user. The query and a possible document is passed simultaneously to transformer network, which then outputs a single score between 0 and 1 indicating how relevant the document is for the given query. The cross-encoder further boost the performance, especially when you search over a corpus for which the bi-encoder was not trained for.   """
-)
+st.markdown(    
+    """-Bi-Encoder (Retrieval): The Bi-encoder is responsible for independently embedding the sentences and search queries into a vector space. The result is then passed to the cross-encoder for checking the relevance/similarity between the query and sentences.
+    -Cross-Encoder (Re-Ranker): A re-ranker based on a Cross-Encoder can substantially improve the final results for the user. The query and a possible document is passed simultaneously to transformer network, which then outputs a single score between 0 and 1 indicating how relevant the document is for the given query. The cross-encoder further boost the performance, especially when you search over a corpus for which the bi-encoder was not trained for.""")
 
 st.image('encoder.png', caption='Retrieval and Re-Rank')
 
@@ -252,7 +253,8 @@ st.markdown("""
     In order to use the app:
     - Select the preferred Sentence Transformer model (Bi-Encoder).
     - Select the number of sentences per paragraph to partition your corpus (Window-Size), if you choose a small value the context from the other sentences might get lost and for larger values the results might take longer to generate.
-    - Paste the URL with your corpus or upload your preferred document in txt, pdf or Word format
+    - Select the number of top hits to be generated.
+    - Paste the URL with your corpus or upload your preferred document in txt, pdf or Word format.
     - Semantic Search away!! """
 )
 
@@ -265,11 +267,6 @@ st.markdown(
     unsafe_allow_html=True,
 )
 
-st.markdown(
-    "<h3 style='text-align: center; color: red;'>OR</h3>",
-    unsafe_allow_html=True,
-)
-
 upload_doc = st.file_uploader(
     "Upload a .txt, .pdf, .docx file"
 )