Update app.py

app.py

@@ -54,14 +54,6 @@ transhuman_glossary = {
 }
 
 
-# Function to search glossary and display results
-def search_glossary(query):
-    for category, terms in transhuman_glossary.items():
-        if query.lower() in (term.lower() for term in terms):
-            st.markdown(f"### {category}")
-            st.write(f"- {query}")
-
-    
             
 # Display the glossary with Streamlit components, ensuring emojis are used
 def display_glossary(area):
@@ -868,6 +860,28 @@ def add_medical_exam_buttons2():
         if col7.button("Ramipril 💊"):
             StreamLLMChatResponse(descriptions["Ramipril 💊"])
         
+
+# Function to search glossary and display results
+def search_glossary(query):
+    for category, terms in transhuman_glossary.items():
+        if query.lower() in (term.lower() for term in terms):
+            st.markdown(f"### {category}")
+            st.write(f"- {query}")
+
+    st.write('## Processing query against GPT and Llama:')
+    # ------------------------------------------------------------------------------------------------
+    st.write('Reasoning with your inputs using GPT...')
+    response = chat_with_model(transcript)
+    st.write('Response:')
+    st.write(response)
+    filename = generate_filename(response, "txt")
+    create_file(filename, transcript, response, should_save)
+    
+    st.write('Reasoning with your inputs using Llama...')    
+    response = StreamLLMChatResponse(transcript)
+    filename_txt = generate_filename(transcript, "md")
+    create_file(filename_txt, transcript, response, should_save)
+    # ------------------------------------------------------------------------------------------------
         
 
 # 17. Main
@@ -1085,36 +1099,35 @@ def main():
 
     # Feedback
     # Step: Give User a Way to Upvote or Downvote
-    with st.expander("Give your feedback 👍", expanded=False):
-
-        feedback = st.radio("Step 8: Give your feedback", ("👍 Upvote", "👎 Downvote"))
-        if feedback == "👍 Upvote":
-            st.write("You upvoted 👍. Thank you for your feedback!")
-        else:
-            st.write("You downvoted 👎. Thank you for your feedback!")
-            
-        load_dotenv()
-        st.write(css, unsafe_allow_html=True)
-        st.header("Chat with documents :books:")
-        user_question = st.text_input("Ask a question about your documents:")
-        if user_question:
-            process_user_input(user_question)
-        with st.sidebar:
-            st.subheader("Your documents")
-            docs = st.file_uploader("import documents", accept_multiple_files=True)
-            with st.spinner("Processing"):
-                raw = pdf2txt(docs)
-                if len(raw) > 0:
-                    length = str(len(raw))
-                    text_chunks = txt2chunks(raw)
-                    vectorstore = vector_store(text_chunks)
-                    st.session_state.conversation = get_chain(vectorstore)
-                    st.markdown('# AI Search Index of Length:' + length + ' Created.')  # add timing
-                    filename = generate_filename(raw, 'txt')
-                    create_file(filename, raw, '', should_save)
-
-
+    GiveFeedback=False
+    if GiveFeedback:
+        with st.expander("Give your feedback 👍", expanded=False):
     
+            feedback = st.radio("Step 8: Give your feedback", ("👍 Upvote", "👎 Downvote"))
+            if feedback == "👍 Upvote":
+                st.write("You upvoted 👍. Thank you for your feedback!")
+            else:
+                st.write("You downvoted 👎. Thank you for your feedback!")
+                
+            load_dotenv()
+            st.write(css, unsafe_allow_html=True)
+            st.header("Chat with documents :books:")
+            user_question = st.text_input("Ask a question about your documents:")
+            if user_question:
+                process_user_input(user_question)
+            with st.sidebar:
+                st.subheader("Your documents")
+                docs = st.file_uploader("import documents", accept_multiple_files=True)
+                with st.spinner("Processing"):
+                    raw = pdf2txt(docs)
+                    if len(raw) > 0:
+                        length = str(len(raw))
+                        text_chunks = txt2chunks(raw)
+                        vectorstore = vector_store(text_chunks)
+                        st.session_state.conversation = get_chain(vectorstore)
+                        st.markdown('# AI Search Index of Length:' + length + ' Created.')  # add timing
+                        filename = generate_filename(raw, 'txt')
+                        create_file(filename, raw, '', should_save)
     
     # Relocated!  Hope you like your new space - enjoy!
     # Display instructions and handle query parameters