Adding caching to streamlit demos

examples/demos/streamlit/CLIPForZeroShotClassification/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model, labels):
 
     image_assembler = ImageAssembler() \
@@ -121,4 +122,4 @@ def save_uploadedfile(uploadedfile):
 Pipeline = create_pipeline(model, lables)
 output = fit_data(Pipeline, selected_image)
 
-st.markdown(f'This document has been classified as  : **{output}**')
+st.markdown(f'This document has been classified as  : **{output}**')

examples/demos/streamlit/Question Answering Closed Book/streamlit_app.py

@@ -16,6 +16,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
     document_assembler = DocumentAssembler()\
     .setInputCol("text")\
@@ -100,4 +101,4 @@ def fit_data(pipeline, data):
 output = fit_data(Pipeline, selected_text)
 
 st.subheader('Prediction')
-st.write(output)
+st.write(output)

examples/demos/streamlit/Question Answering Open Book/streamlit_app.py

@@ -16,6 +16,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
         document_assembler = DocumentAssembler()\
         .setInputCol("text")\
@@ -101,4 +102,4 @@ def fit_data(pipeline, data):
 output = fit_data(Pipeline, selected_text)
 
 st.subheader('Prediction')
-st.write(output)
+st.write(output)

examples/demos/streamlit/Sentence Detector/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
     documenter = DocumentAssembler()\
     .setInputCol("text")\

examples/demos/streamlit/Sentence Grammer/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
     documentAssembler = DocumentAssembler() \
       .setInputCol("text") \

examples/demos/streamlit/Sentence Similarity/streamlit_app.py

@@ -16,6 +16,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
     document_assembler = DocumentAssembler()
     document_assembler.setInputCol('text')
@@ -150,4 +151,4 @@ def get_similarity(light_pipeline, input_list):
 else:
     similarity_str = "not similar"
 st.markdown(f'Detected similarity: **{similarity}%**')
-st.markdown(f'These sentences are **{similarity_str}**.')
+st.markdown(f'These sentences are **{similarity_str}**.')

examples/demos/streamlit/Sentiment Cyber Bullying/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
   documentAssembler = DocumentAssembler()\
     .setInputCol("text")\
@@ -104,4 +105,4 @@ def fit_data(pipeline, data):
 if output.lower() in ['neutral', 'normal']:
   st.markdown("""<h3>This seems like a <span style="color: green">{}</span> tweet. <span style="font-size:35px;">&#128515;</span></h3>""".format(output), unsafe_allow_html=True)
 elif output.lower() in ['racism', 'sexism']:
-  st.markdown("""<h3>This seems like a <span style="color: #B64434">{}</span> tweet. <span style="font-size:35px;">&#129324;</span></h3>""".format(output), unsafe_allow_html=True)
+  st.markdown("""<h3>This seems like a <span style="color: #B64434">{}</span> tweet. <span style="font-size:35px;">&#129324;</span></h3>""".format(output), unsafe_allow_html=True)

examples/demos/streamlit/Sentiment DA/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
     document = DocumentAssembler()\
         .setInputCol("text")\

examples/demos/streamlit/Sentiment DE/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
     document = DocumentAssembler()\
         .setInputCol("text")\

examples/demos/streamlit/Sentiment EN Emotion/streamlit_app.py

@@ -12,6 +12,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
   documentAssembler = DocumentAssembler()\
     .setInputCol("text")\

examples/demos/streamlit/Sentiment EN Finance/streamlit_app.py

@@ -12,6 +12,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
   document = DocumentAssembler()\
     .setInputCol("text")\

examples/demos/streamlit/Sentiment EN Sarcasm/streamlit_app.py

@@ -12,6 +12,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
   documentAssembler = DocumentAssembler()\
   .setInputCol("text")\
@@ -101,4 +102,4 @@ def fit_data(pipeline, data):
 if output in ['neutral', 'normal']:
   st.markdown("""<h3>This seems like <span style="color: #209DDC">{}</span> news. <span style="font-size:35px;">&#128578;</span></h3>""".format(output), unsafe_allow_html=True)
 elif output == 'sarcasm':
-  st.markdown("""<h3>This seems like a <span style="color: #B64434">{}</span> tweet. <span style="font-size:35px;">&#128579;</span></h3>""".format('sarcastic'), unsafe_allow_html=True)
+  st.markdown("""<h3>This seems like a <span style="color: #B64434">{}</span> tweet. <span style="font-size:35px;">&#128579;</span></h3>""".format('sarcastic'), unsafe_allow_html=True)

examples/demos/streamlit/Sentiment EN Toxic/streamlit_app.py

@@ -12,6 +12,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
   documentAssembler = DocumentAssembler() \
         .setInputCol("text") \

examples/demos/streamlit/Sentiment EN/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
     documentAssembler = DocumentAssembler()\
         .setInputCol("text")\

examples/demos/streamlit/Sentiment FR/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
     document = DocumentAssembler()\
         .setInputCol("text")\

examples/demos/streamlit/Sentiment SW/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
     document_assembler = DocumentAssembler() \
         .setInputCol("text") \

examples/demos/streamlit/Sentiment TR/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
     document = DocumentAssembler()\
         .setInputCol("text")\

examples/demos/streamlit/Sentiment UR/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
   document_assembler = DocumentAssembler()\
       .setInputCol("text")\

examples/demos/streamlit/Sentiment VI/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
     document = DocumentAssembler()\
         .setInputCol("text")\

examples/demos/streamlit/Speech Recognition/streamlit_app.py

@@ -18,6 +18,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
 
     audio_assembler = AudioAssembler() \

examples/demos/streamlit/Speech recognition with whisper/streamlit_app.py

@@ -18,6 +18,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
 
     audio_assembler = AudioAssembler() \

examples/demos/streamlit/Spell Checker EN/streamlit_app.py

@@ -19,6 +19,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline():
 
     document_assembler = DocumentAssembler()\

examples/demos/streamlit/StopWords African/streamlit_app.py

@@ -19,6 +19,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model, language):
 
   documentAssembler = DocumentAssembler() \

examples/demos/streamlit/StopWords East Asain/streamlit_app.py

@@ -19,6 +19,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model, language):
 
   documentAssembler = DocumentAssembler() \

examples/demos/streamlit/StopWords European/streamlit_app.py

@@ -19,6 +19,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model, language):
 
   documentAssembler = DocumentAssembler() \

examples/demos/streamlit/StopWords Indian/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
     documenter = DocumentAssembler()\
     .setInputCol("text")\

examples/demos/streamlit/StopWords Middle Eastern/streamlit_app.py

@@ -19,6 +19,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model, language):
 
   documentAssembler = DocumentAssembler() \

examples/demos/streamlit/T5 Active Passive/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model, task):
     documentAssembler = DocumentAssembler() \
         .setInputCol("text") \

examples/demos/streamlit/T5 Formal Informal/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model, task):
     documentAssembler = DocumentAssembler() \
         .setInputCol("text") \

examples/demos/streamlit/T5 Grammer/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
     documentAssembler = DocumentAssembler() \
         .setInputCol("text") \

examples/demos/streamlit/T5 Linguistic/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
     documentAssembler = DocumentAssembler() \
         .setInputCol("text") \

examples/demos/streamlit/T5 SQL/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
     documentAssembler = DocumentAssembler() \
         .setInputCol("text") \

examples/demos/streamlit/Text Finder EN/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model, matches):
     documentAssembler = DocumentAssembler()\
         .setInputCol("text")\

examples/demos/streamlit/Text Preprocessing/streamlit_app.py

@@ -17,6 +17,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model, language):
 
     documentAssembler = DocumentAssembler()\
@@ -171,4 +172,4 @@ def fit_data(pipeline, data):
 if selected_models[5] is True:
   tcol_arr = np.asarray(df['removed_stopwords'].values[0])[:,3]
   st.subheader("Tokens after removing Stop Words:")
-  st.dataframe(pd.DataFrame({'removed_stopwords':tcol_arr}))
+  st.dataframe(pd.DataFrame({'removed_stopwords':tcol_arr}))

examples/demos/streamlit/Text Summarization/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
     document_assembler = DocumentAssembler()\
         .setInputCol("text")\
@@ -98,4 +99,4 @@ def fit_data(pipeline, data):
 
 Pipeline = create_pipeline(model)
 output = fit_data(Pipeline, selected_text)
-st.write(output)
+st.write(output)

examples/demos/streamlit/Text summarization with BART/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model, max_out_length):
     document_assembler = DocumentAssembler()\
         .setInputCol("text")\
@@ -103,4 +104,4 @@ def fit_data(pipeline, data):
 
 Pipeline = create_pipeline(model, max_out_length)
 output = fit_data(Pipeline, selected_text)
-st.write(output)
+st.write(output)

examples/demos/streamlit/Translation Marian/streamlit_app.py

@@ -14,6 +14,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
     documentAssembler = DocumentAssembler()\
       .setInputCol("text")\
@@ -122,4 +123,4 @@ def fit_data(pipeline, data):
 
 Pipeline = create_pipeline(selected_model)
 output = fit_data(Pipeline, selected_text)
-st.write(output)
+st.write(output)

examples/demos/streamlit/Typo Detector/streamlit_app.py

@@ -20,6 +20,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline():
 
     documentAssembler = DocumentAssembler()\

examples/demos/streamlit/VIT Image Classification/streamlit_app.py

@@ -15,6 +15,7 @@
 
 spark = sparknlp.start()
 
+@st.cache_resource
 def create_pipeline(model):
 
     image_assembler = ImageAssembler() \
@@ -113,4 +114,4 @@ def save_uploadedfile(uploadedfile):
 Pipeline = create_pipeline(model)
 output = fit_data(Pipeline, selected_image)
 
-st.markdown(f'This document has been classified as  : **{output}**')
+st.markdown(f'This document has been classified as  : **{output}**')