Update app.py

app.py

@@ -75,21 +75,21 @@ selected_model = st.sidebar.selectbox("", model_name)
 
 if selected_model == "nerdl_fewnerd_100d":
     app_title= "Detect up to 8 entity types in general domain texts"
-    app_description= "Named Entity Recognition model aimed to detect up to 8 entity types from general domain texts. This model was trained on the Few-NERD/inter public dataset using Spark NLP, and it is available in Spark NLP Models hub (https://nlp.johnsnowlabs.com/models)"
+    app_description= "Named Entity Recognition model aimed to detect up to 8 entity types from general domain texts. This model was trained on the Few-NERD/inter public dataset using Spark NLP, and it is available in Spark NLP Models hub. "
     st.title(app_title)
     st.markdown("<h2>"+app_description+"</h2>" , unsafe_allow_html=True)
     st.markdown("**`PERSON`**   **,** **`ORGANIZATION`**    **,**  **`LOCATION`** **,**  **`ART`** **,**  **`BUILDING`** **,**  **`PRODUCT`** **,**  **`EVENT`** **,**  **`OTHER`**", unsafe_allow_html=True)
 
 elif selected_model== "ner_conll_elmo":
     app_title= "Detect up to 4 entity types in general domain texts"
-    app_description= "Named Entity Recognition model aimed to detect up to 4 entity types from general domain texts. This model was trained on the CoNLL 2003 text corpu using Spark NLP, and it is available in Spark NLP Models hub (https://nlp.johnsnowlabs.com/models)"
+    app_description= "Named Entity Recognition model aimed to detect up to 4 entity types from general domain texts. This model was trained on the CoNLL 2003 text corpus using Spark NLP, and it is available in Spark NLP Models hub. "
     st.title(app_title)
     st.markdown("<h2>"+app_description+"</h2>" , unsafe_allow_html=True)
     st.markdown("**`PER`**   **,** **`LOC`**    **,**  **`ORG`** **,**  **`MISC` **", unsafe_allow_html=True)
 
 elif selected_model== "ner_mit_movie_complex_distilbert_base_cased":
     app_title= "Detect up to 12 entity types in movie domain texts"
-    app_description= "Named Entity Recognition model aimed to detect up to 12 entity types from movie domain texts. This model was trained on the MIT Movie Corpus complex queries dataset to detect movie trivia using Spark NLP, and it is available in Spark NLP Models hub (https://nlp.johnsnowlabs.com/models)"
+    app_description= "Named Entity Recognition model aimed to detect up to 12 entity types from movie domain texts. This model was trained on the MIT Movie Corpus complex queries dataset to detect movie trivia using Spark NLP, and it is available in Spark NLP Models hub. "
     st.title(app_title)
     st.markdown("<h2>"+app_description+"</h2>" , unsafe_allow_html=True)
     st.markdown("""**`ACTOR`**   **,** **`AWARD`**    **,**  **`CHARACTER_NAME`** **,**  **`DIRECTOR`** **,**  **`GENRE`** **,**  **`OPINION`** **,**  **`ORIGIN`** **,**  **`PLOT`**,
@@ -98,14 +98,14 @@ elif selected_model== "ner_mit_movie_complex_distilbert_base_cased":
 
 elif selected_model=="ner_conll_albert_large_uncased":
     app_title= "Detect up to 4 entity types in general domain texts"
-    app_description= "Named Entity Recognition model aimed to detect up to 4 entity types from general domain texts. This model was trained on the CoNLL 2003 text corpus using Spark NLP, and it is available in Spark NLP Models hub (https://nlp.johnsnowlabs.com/models)"
+    app_description= "Named Entity Recognition model aimed to detect up to 4 entity types from general domain texts. This model was trained on the CoNLL 2003 text corpus using Spark NLP, and it is available in Spark NLP Models hub. "
     st.title(app_title)
     st.markdown("<h2>"+app_description+"</h2>" , unsafe_allow_html=True)
     st.markdown("**`PER`**   **,** **`LOC`**    **,**  **`ORG`** **,**  **`MISC` **", unsafe_allow_html=True)
 
 elif selected_model=="onto_100":
     app_title= "Detect up to 18 entity types in general domain texts"
-    app_description= "Named Entity Recognition model aimed to detect up to 18 entity types from general domain texts. This model was trained with GloVe 100d word embeddings using Spark NLP, so be sure to use same embeddings in the pipeline. It is available in Spark NLP Models hub (https://nlp.johnsnowlabs.com/models)"
+    app_description= "Named Entity Recognition model aimed to detect up to 18 entity types from general domain texts. This model was trained with GloVe 100d word embeddings using Spark NLP, so be sure to use same embeddings in the pipeline. It is available in Spark NLP Models hub. "
     st.title(app_title)
     st.markdown("<h2>"+app_description+"</h2>" , unsafe_allow_html=True)
     st.markdown("""**`CARDINAL`**   **,** **`EVENT`**    **,**  **`WORK_OF_ART`** **,**  **`ORG`** **,**  **`DATE`** **,**  **`GPE`** **,**  **`PERSON`** **,**  **`PRODUCT`**,
@@ -308,4 +308,6 @@ st.sidebar.title('')
 st.sidebar.markdown('Try it yourself:')
 st.sidebar.markdown(try_link, unsafe_allow_html=True)
 
-
+st.sidebar.markdown("""Want to see more? 
+- Check Spark NLP in action, including our Spark NLP for Healthcare & Spark OCR demos at [here](https://www.johnsnowlabs.com/spark-nlp-in-action/#)
+- Check our 4.4K+ models available in Spark NLP Models Hub [here](https://nlp.johnsnowlabs.com/models)""")