Update app.py

app.py

@@ -10,6 +10,8 @@ from spacy import displacy
 from word2number import w2n
 from sentence_transformers import SentenceTransformer
 from sklearn.metrics.pairwise import cosine_similarity
+import en_core_web_sm
+nlp = en_core_web_sm.load()
 nltk.download('punkt')
 nltk.download('stopwords')
 
@@ -231,10 +233,18 @@ def getQuarterPrediction(text):
         logits = modelQuarter(**tokens).logits
     predicted_class_id = logits.argmax().item()
     return modelQuarter.config.id2label[predicted_class_id]
+def getSentence(listOfSentences,value):
+    for sent in listOfSentences:
+        if value in sent:
+            return sent
+    return value
+
 
 
 st.header("Transcript Analysis", divider='rainbow')
 mainTranscript = st.text_area("Enter the transcript:", height=100)
+doc = nlp(mainTranscript)
+sentences = [sent.text for sent in doc.sents]
 quarter = st.text_input('Enter your quarter', '')
 year = st.text_input('Enter your year', '')
 if st.button("Analyze"):
@@ -280,26 +290,17 @@ if st.button("Analyze"):
     profits=[x["profit"] for x in ner_result if "profit" in x]
     revenues=[x["revenue"] for x in ner_result if "revenue" in x]
     expences=[x["expense"] for x in ner_result if "expense" in x]
-    for idx in range(len(revenues)):
+    for idx in range(len(revenues)): 
         st.text_input(f'Revenue:{idx+1}', revenues[idx])
+        st.text_input(f'Sentences', getSentence(sentences,revenues[idx]))
     for idx in range(len(profits)):
         st.text_input(f'Profit:{idx+1}', profits[idx])
+         st.text_input(f'Sentences', getSentence(sentences,profits[idx]))
     for idx in range(len(expences)):
         st.text_input(f'Expences:{idx+1}', expences[idx])
-    # st.subheader("Parts from transcript that contais financial metrics", divider='rainbow')
-    # for idx in savedchunks:
-    #     doc = nlp(chunks[idx])
-    #     entity_list=nlpPipe(chunks[idx])
-    #     entities = []
-    #     for entity in entity_list:
-    #         span = doc.char_span(entity['start'], entity['end'], label=entity['entity_group'])
-    #         entities.append(span)
-    #     try:
-    #         doc.ents = entities
-    #         ent_html = displacy.render(doc, style="ent", jupyter=False)
-    #         st.markdown(ent_html, unsafe_allow_html=True)
-    #     except:
-    #         pass
+         st.text_input(f'Sentences', getSentence(sentences,expences[idx]))
+
+    
     st.subheader("Investment Recommendation", divider='rainbow')
     profitAmount=sum([convert_amount_to_number(x) for x in profits])
     expencesAmount=sum([convert_amount_to_number(x) for x in expences])