Update app.py

app.py

@@ -1,42 +1,22 @@
-# # ***** Finacial Sentiment Analysis App *****
-# # Team *** AI Supremo
-# # Author *** Lerato Chere
+# ***** Finacial Sentiment Analysis App *****
+# Team *** AI Supremo
+# Author *** Lerato Chere
 
-# import streamlit as st
-# from transformers import pipeline
-
-# sentiment_pipeline = pipeline("sentiment-analysis")
-
-# st.title("Financial Sentiment Analysis \n Team AI Supremo ")
-# st.write("Enter a Financial Statement to Analyze the Sentiment:")
-# user_input = st.text_input("")
-# st.write("Press the Enter key")
-
-# if user_input:
-#     result = sentiment_pipeline(user_input)
-#     sentiment = result[0]["label"]
-#     confidence = result[0]["score"]
-
-#     st.write(f"Sentiment: {sentiment}")
-#     st.write(f"Confidence: {confidence:.2%}")
-
-
-
-from transformers import BertTokenizer, BertForSequenceClassification
+import streamlit as st
 from transformers import pipeline
 
-model = BertForSequenceClassification.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis",num_labels=3)
-tokenizer = BertTokenizer.from_pretrained("ahmedrachid/FinancialBERT-Sentiment-Analysis")
+sentiment_pipeline = pipeline("sentiment-analysis")
+
+st.title("Financial Sentiment Analysis \n Team AI Supremo ")
+st.write("Enter a Financial Statement to Analyze the Sentiment:")
+user_input = st.text_input("")
+st.write("Press the Enter key")
 
-nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+if user_input:
+    result = sentiment_pipeline(user_input)
+    sentiment = result[0]["label"]
+    confidence = result[0]["score"]
 
-sentences = ["Operating profit rose to EUR 13.1 mn from EUR 8.7 mn in the corresponding period in 2007 representing 7.7 % of net sales.",  
-             "Bids or offers include at least 1,000 shares and the value of the shares must correspond to at least EUR 4,000.", 
-             "Raute reported a loss per share of EUR 0.86 for the first half of 2009 , against EPS of EUR 0.74 in the corresponding period of 2008.", 
-             ]
-results = nlp(sentences)
-print(results)
+    st.write(f"Sentiment: {sentiment}")
+    st.write(f"Confidence: {confidence:.2%}")
 
-[{'label': 'positive', 'score': 0.9998133778572083},
- {'label': 'neutral', 'score': 0.9997822642326355},
- {'label': 'negative', 'score': 0.9877365231513977}]