Create app.py

app.py

@@ -0,0 +1,28 @@
+import streamlit as st
+from transformers import pipeline
+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")
+def main():
+    st.title("Transcript Analysis")
+    transcript = st.text_area("Enter the transcript:", height=200)
+
+    if st.button("Analyze"):
+        st.subheader("Sentiment Analysis")
+        sentiment = sentiment_model(transcript)[0]['label']
+        st.write(sentiment)
+
+        st.subheader("Increase/Decrease Prediction")
+        increase_decrease = increase_decrease_model(transcript)[0]['label']
+        st.write(increase_decrease)
+
+        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.")
+
+if __name__ == "__main__":
+    main()