Update app.py

app.py

@@ -1,36 +1,30 @@
 import streamlit as st
 from transformers import pipeline
+import math
 sentiment_model = pipeline("text-classification", model="AhmedTaha012/managersFeedback-V1.0.7")
 increase_decrease_model = pipeline("text-classification", model="AhmedTaha012/nextQuarter-status-V1.1.9")
-ner_model = pipeline("token-classification", model="AhmedTaha012/finance-ner-v0.0.8-finetuned-ner")
+ner_model = pipeline("token-classification", model="AhmedTaha012/finance-ner-v0.0.9-finetuned-ner")
 
 st.title("Transcript Analysis")
 transcript = st.text_area("Enter the transcript:", height=200)
+tokens=transcript.split()
+splitSize=256
+chunks=[tokens[r*splitSize:(r+1)*splitSize] for r in range(math.ceil(len(tokens)/splitSize))]
 
 if st.button("Analyze"):
     st.subheader("Sentiment Analysis")
-    sentiment = sentiment_model(transcript)[0]['label']
+    sentiment = [sentiment_model(x)[0]['label'] for x in chunks]
+    sentiment=max(sentiment,key=sentiment.count)
     sentiment_color = "green" if sentiment == "POSITIVE" else "red"
     st.markdown(f'<span style="color:{sentiment_color}">{sentiment}</span>', unsafe_allow_html=True)
 
     st.subheader("Increase/Decrease Prediction")
-    increase_decrease = increase_decrease_model(transcript)[0]['label']
+    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)
 
     st.subheader("NER Metrics")
-    ner_result = ner_model(transcript)
-    revenue = next((entity['entity'] for entity in ner_result if entity['entity'] == 'revenue'), None)
-    if revenue:
-        st.write(f"Revenue: {revenue}")
-    else:
-        st.write("Revenue not found.")
-
-    show_details = st.checkbox("Show Detailed Predictions")
-    if show_details:
-        st.subheader("Detailed Predictions")
-        st.json({
-            "Sentiment Analysis": sentiment,
-            "Increase/Decrease Prediction": increase_decrease,
-            "NER Metrics": ner_result
-        })
+    ner_result = [ner_model(x) for x in chunks]
+    st.write(str(ner_result))
+