update

appStore/keyword_search.py

@@ -107,13 +107,12 @@ def app():
                                     split_by=lexical_split_by,
                                     split_length=lexical_split_length,
                                     split_overlap=lexical_split_overlap,
-                                    removePunc=lexical_remove_punc)
+                                    remove_punc=lexical_remove_punc)
                         logging.info("performing lexical search")
                         with st.spinner("Performing Exact matching search \
                                         (Lexical search) for you"):
-                            lexical_search(
-                                query=queryList,
-                                documents = all_documents['documents'],
+                            lexical_search(query=queryList,
+                        documents = all_documents['documents'],
                                 top_k = lexical_top_k )
                     else:
                         all_documents = runSemanticPreprocessingPipeline(
@@ -122,7 +121,7 @@ def app():
                                             split_by=split_by,
                                             split_length= split_length,
                                             split_overlap=split_overlap,
-                                            removePunc= remove_punc,
+                                            remove_punc= remove_punc,
                         split_respect_sentence_boundary=split_respect_sentence_boundary)
                         if len(all_documents['documents']) > 100:
                             warning_msg = ": This might take sometime, please sit back and relax."

paramconfig.cfg

@@ -16,7 +16,7 @@ READER = deepset/tinyroberta-squad2
 READER_TOP_K = 10
 THRESHOLD = 0.1
 SPLIT_BY = sentence
-SPLIT_LENGTH = 3
+SPLIT_LENGTH = 4
 SPLIT_OVERLAP = 1
 RESPECT_SENTENCE_BOUNDARY = 1
 REMOVE_PUNC = 0

utils/lexical_search.py

@@ -230,7 +230,7 @@ def lexical_search(query:Text,top_k:int, documents:List[Document]):
         if len(matches) != 0:
             if flag:
                 flag = False
-                if check_streamlit:
+                if check_streamlit():
                     st.markdown("##### Top few lexical search (TFIDF) hits #####")
                 else:
                     print("Top few lexical search (TFIDF) hits")
@@ -242,4 +242,7 @@ def lexical_search(query:Text,top_k:int, documents:List[Document]):
             spacyAnnotator(matches, doc)
 
     if flag:
-        st.info("🤔 No relevant result found. Please try another keyword.")    
+        if check_streamlit():
+            st.info("🤔 No relevant result found. Please try another keyword.")
+        else:
+            print("No relevant result found. Please try another keyword.")   

utils/sdg_classifier.py

@@ -94,7 +94,7 @@ def sdg_classification(haystack_doc:List[Document],
     """
     logging.info("Working on SDG Classification")
     if not classifier_model:
-        if check_streamlit:
+        if check_streamlit():
             classifier_model = st.session_state['sdg_classifier']
         else:
             logging.warning("No streamlit envinornment found, Pass the classifier")