Update app.py

app.py

@@ -11,6 +11,7 @@ from word2number import w2n
 from sentence_transformers import SentenceTransformer
 from sklearn.metrics.pairwise import cosine_similarity
 import en_core_web_sm
+from annotated_text import annotated_text
 nlp = en_core_web_sm.load()
 nltk.download('punkt')
 nltk.download('stopwords')
@@ -243,7 +244,9 @@ def getSentence(listOfSentences,value):
         if value in sent:
             return sent
     return value
-
+def get_annotated_text(text,value,entity):
+    return [text.split(value)[0],(value,entity),text.split(value)[1]]
+    
 st.header("Transcript Analysis", divider='rainbow')
 mainTranscript = st.text_area("Enter the transcript:", height=100)
 doc = nlp(mainTranscript)
@@ -297,13 +300,16 @@ if st.button("Analyze"):
     expences=[x["expense"] for x in ner_result if "expense" in x]
     for idx in range(len(revenues)): 
          st.text_input(f'Revenue:{idx+1}', revenues[idx])
-         st.text_input(f'Revenue-Sentence:{idx+1}', getSentence(sentences,revenues[idx]))
+         annotated_text(get_annotated_text(getSentence(sentences,revenues[idx]),str(revenues[idx]),"Revenue"))
+         # st.text_input(f'Revenue-Sentence:{idx+1}', getSentence(sentences,revenues[idx]))
     for idx in range(len(profits)):
          st.text_input(f'Profit:{idx+1}', profits[idx])
-         st.text_input(f'Profit-Sentence:{idx+1}', getSentence(sentences,profits[idx]))
+         annotated_text(get_annotated_text(getSentence(sentences,revenues[idx]),str(revenues[idx]),"Profit"))
+         # st.text_input(f'Profit-Sentence:{idx+1}', getSentence(sentences,profits[idx]))
     for idx in range(len(expences)):
          st.text_input(f'Expences:{idx+1}', expences[idx])
-         st.text_input(f'Expences-Sentences:{idx+1}', getSentence(sentences,expences[idx]))
+         annotated_text(get_annotated_text(getSentence(sentences,revenues[idx]),str(revenues[idx]),"Expences"))
+         # st.text_input(f'Expences-Sentences:{idx+1}', getSentence(sentences,expences[idx]))
 
     st.subheader("Investment Recommendation", divider='rainbow')
     profitAmount=sum([convert_amount_to_number(x) for x in profits])