Update app.py

app.py

@@ -25,7 +25,7 @@ def preprocess_text(element):
 
 def get_openai_response(text, min_length=100, max_length=100, model="gpt-3.5-turbo-instruct"):
     summarizer = pipeline("summarization", model=model)
-    return summarizer(text, min_length = min_length, max_length = max_length)
+    return summarizer(text, min_length=min_length, max_length=max_length)
 
 ## Streamlit app
 
@@ -34,8 +34,8 @@ st.header("PDF Summarizer")
 
 # User options
 st.subheader("Settings")
-min_summary_length = st.slider("Summary Length", min_value=50, max_value=500, value=100)
-max_summary_length = st.slider("Summary Length", min_value=50, max_value=500, value=100)
+min_summary_length = st.slider("Minimum Summary Length", min_value=50, max_value=500, value=100)
+max_summary_length = st.slider("Maximum Summary Length", min_value=50, max_value=500, value=100)
 summarization_model = st.selectbox("Summarization Model", ["gpt-3.5-turbo-instruct", "t5-small", "facebook/bart-large-cnn"])
 
 # File upload and processing
@@ -49,10 +49,9 @@ if uploaded_file is not None:
         if text:
             submit = st.button("Generate Summary")
             if submit:
-                st.spinner("Summarizing...")
-                response = get_openai_response(text, min_length=min_summary_length, max_length=max_summary_length, model=summarization_model)
-                st.subheader("Summary")
-                st.write(response[0]["summary_text"])
+                with st.spinner("Summarizing..."):
+                    response = get_openai_response(text, min_length=min_summary_length, max_length=max_summary_length, model=summarization_model)
+                    st.subheader("Summary")
+                    st.write(response[0]["summary_text"])
         else:
             st.error("No text found in the PDF.")
-