Update app.py

app.py

@@ -1,6 +1,6 @@
 import streamlit as st
 from transformers import pipeline
-from transformers import AutoTokenizer,AutoModelForTokenClassification
+from transformers import AutoTokenizer,AutoModelForTokenClassification,AutoModelForSequenceClassification
 import math
 import nltk
 import torch
@@ -18,10 +18,10 @@ sentiment_model = pipeline("text-classification", model="AhmedTaha012/managersFe
 increase_decrease_model = pipeline("text-classification", model="AhmedTaha012/nextQuarter-status-V1.1.9")
 tokenizerTopic = AutoTokenizer.from_pretrained("nickmuchi/finbert-tone-finetuned-finance-topic-classification",use_fast=True)
 modelTopic = AutoModelForSequenceClassification.from_pretrained("nickmuchi/finbert-tone-finetuned-finance-topic-classification")
-torch.compile(modelTopic)
+# torch.compile(modelTopic)
 tokenizer = AutoTokenizer.from_pretrained("AhmedTaha012/finance-ner-v0.0.9-finetuned-ner")
 model = AutoModelForTokenClassification.from_pretrained("AhmedTaha012/finance-ner-v0.0.9-finetuned-ner")
-torch.compile(model)
+# torch.compile(model)
 # torch.compile(model)
 nlpPipe = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True)
 def getSpeakers(data):
@@ -243,7 +243,8 @@ if st.button("Analyze"):
     sentiment_color = "green" if sentiment == "postive" else "red"
     st.markdown(f'<span style="color:{sentiment_color}">{sentiment}</span>', unsafe_allow_html=True)
     st.subheader("Next Quarter Perdiction", divider='rainbow')
-    increase_decrease = [increase_decrease_model(x)[0]['label'] for x in chunks]
+    # increase_decrease = [increase_decrease_model(x)[0]['label'] for x in chunks]
+    
     increase_decrease=max(increase_decrease,key=increase_decrease.count)
     increase_decrease_color = "green" if increase_decrease == "Increase" else "red"
     st.markdown(f'<span style="color:{increase_decrease_color}">{increase_decrease}</span>', unsafe_allow_html=True)