Update app.py

app.py

@@ -8,10 +8,10 @@ from nltk.corpus import stopwords
 import spacy
 from spacy import displacy
 from word2number import w2n
-import en_core_web_md
+
 nltk.download('punkt')
 nltk.download('stopwords')
-nlp = en_core_web_md.load()
+
 sentiment_model = pipeline("text-classification", model="AhmedTaha012/managersFeedback-V1.0.7")
 increase_decrease_model = pipeline("text-classification", model="AhmedTaha012/nextQuarter-status-V1.1.9")
 tokenizer = AutoTokenizer.from_pretrained("AhmedTaha012/finance-ner-v0.0.9-finetuned-ner")
@@ -233,20 +233,20 @@ if st.button("Analyze"):
         st.text_input(f'Profit:{idx+1}', 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.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.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])