Update app.py

app.py

@@ -125,7 +125,7 @@ recommender = SemanticSearch()
 
 st.title('PDF GPT')
 
-description = """ PDF GPT allows you to chat with your PDF file using Universal Sentence Encoder and Open AI. It gives hallucination free response than other tools as the embeddings are better than OpenAI. The returned response can even cite the page number in square brackets([]) where the information is located, adding credibility to the responses and helping to locate pertinent information quickly."""
+description = """ PDF GPT allows you to chat with your PDF file using Universal Sentence Encoder and Open AI. The returned response can cite the page number in square brackets([]) where the information is located, adding credibility to the responses and helping to locate pertinent information quickly."""
 
 st.markdown(description)
 
@@ -134,11 +134,12 @@ openAI_key = st.sidebar.text_input('Enter your OpenAI API key here')
 col1, col2, col3, col4 = st.columns(4)
 
 with col1:
-    url = st.text_input('Enter PDF URL here')
+    url = st.text_input('PDF URL')
 with col2:
     question = st.text_input('Question')
 with col3:
-    answer_placeholder = st.empty()
+    answer_placeholder = st.empty()  # Create an empty placeholder for the 'Answer' box
+    answer_placeholder.text_area('Answer', value='', height=40)  # Initialize the 'Answer' box with an empty string
 with col4:
     if st.button('Submit'):
         if openAI_key.strip()=='':
@@ -153,4 +154,4 @@ with col4:
             load_recommender('corpus.pdf')
 
             answer = generate_answer(question,openAI_key)
-            answer_placeholder.text_area('Answer', value=answer, height=80)
+            answer_placeholder.text_area('Answer', value=answer, height=40)  # Fill in the 'Answer' box with the generated answer