Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,16 +1,24 @@
-app.py
+# app.py
+import streamlit as st
+from embedding import setup_embeddings, load_data
+from model_utils import load_llama_model, is_finance_question, ask_finance_bot
 
-import streamlit as st from embedding import setup_embeddings, load_data from model_utils import load_llama_model, is_finance_question, ask_finance_bot
+# Load data and embeddings
+questions, answers = load_data()
+embedding_model, index = setup_embeddings(answers)
+tokenizer, model = load_llama_model()
 
-Load data and embeddings
-
-questions, answers = load_data() embedding_model, index = setup_embeddings(answers) tokenizer, model = load_llama_model()
-
-Streamlit UI
-
-st.set_page_config(page_title="DiMowkayBot - Finance Assistant", layout="centered") st.title("🤖 DiMowkayBot - Your Finance Q&A Assistant")
+# Streamlit UI
+st.set_page_config(page_title="DiMowkayBot - Finance Assistant", layout="centered")
+st.title("DiMowkayBot - Your Finance Q&A Assistant")
 
 user_query = st.text_input("Ask a finance-related question:")
 
-if user_query: with st.spinner("Thinking..."): if not is_finance_question(user_query, tokenizer, model): st.warning("I'm specialized in finance and can't help with that. How can I assist you with a finance-related question today?") else: answer = ask_finance_bot(user_query, answers, embedding_model, index, tokenizer, model) st.success("Response:") st.write(answer)
-
+if user_query:
+    with st.spinner("Thinking..."):
+        if not is_finance_question(user_query, tokenizer, model):
+            st.warning("I'm specialized in finance and can't help with that. How can I assist you with a finance-related question today?")
+        else:
+            answer = ask_finance_bot(user_query, answers, embedding_model, index, tokenizer, model)
+            st.success("Response:")
+            st.write(answer)