Update app.py

app.py

@@ -10,6 +10,8 @@ import requests
 import json
 import openai
 
+# initialize userinput
+userinput = ""
 # Define a function to split text into chunks
 def chunk_text(text, chunk_size=2000):
     chunks = []
@@ -25,6 +27,10 @@ def chunk_text(text, chunk_size=2000):
 if 'learning_objectives' not in st.session_state:
     st.session_state.learning_objectives = ""
 
+# Initialize the Whisper model outside the button
+if 'whisper_model' not in st.session_state:
+    st.session_state.whisper_model = whisper.load_model("base")
+
 # Streamlit User Input Form
 st.title("Patent Claims Extraction")
 
@@ -40,7 +46,7 @@ if audio_file is not None:
 
 # Moved the submit_button check here
 if 'submit_button' in st.session_state:
-    model = whisper.load_model("base")
+    model = st.session_state.whisper_model
 
     if audio_data:
         with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as audio_file:
@@ -140,5 +146,11 @@ for chunk in chunks:
     summary = bert_legal_model(chunk, min_length=8, ratio=0.05)
     summaries.append(summary)
 
+    
 # Now you have a list of summaries for each chunk
 # You can access them using `summaries[0]`, `summaries[1]`, etc.
+# After generating summaries
+for i, summary in enumerate(summaries):
+    st.write(f"### Summary {i+1}")
+    st.write(summary)
+