Update app.py

app.py

@@ -1,31 +1,34 @@
 import streamlit as st
-from hf_model import load_model, summarize_text
+from hf_model import load_model, summarize_text, answer_question
 
-# Load the model
+# Title and Sidebar
 st.title("Legal NLP Application")
 st.sidebar.title("Options")
 
 # Select the task
 task = st.sidebar.radio("Select Task", ["Summarization", "Question Answering"])
 
-# Initialize Hugging Face model
+# Task 1: Summarization
 if task == "Summarization":
     st.header("Summarize Legal Text")
-    user_input = st.text_area("Enter Legal Text", height=200)
-    
+    user_input = st.text_area("Enter Legal Text", height=200, placeholder="Paste a legal text or case summary here...")
+
     if st.button("Summarize"):
-        model = load_model(task="summarization")
+        st.write("Loading model...")
+        model = load_model(task="summarization")  # Load summarization model
         summary = summarize_text(model, user_input)
         st.write("### Summary")
         st.write(summary)
 
+# Task 2: Question Answering
 elif task == "Question Answering":
     st.header("Legal Question Answering")
-    context = st.text_area("Enter Context (e.g., Legal Contract or Case Description)", height=200)
-    question = st.text_input("Ask a Question")
-    
+    context = st.text_area("Enter Context", height=200, placeholder="Paste a legal document or case description here...")
+    question = st.text_input("Ask a Legal Question", placeholder="Type your legal question here...")
+
     if st.button("Get Answer"):
-        model = load_model(task="question-answering")
-        answer = model(question=question, context=context)
+        st.write("Loading model...")
+        model = load_model(task="question-answering")  # Load QA model
+        answer = answer_question(model, question, context)
         st.write("### Answer")
         st.write(answer)