Update app.py

app.py

@@ -533,45 +533,45 @@ def search_glossary(query):  # 🔍Run------------------------------------------
 
     # 🔍Run 1 - plain query
     #response = chat_with_model(query)  
-    response1 = chat_with_model45(query) 
+    #response1 = chat_with_model45(query) 
     
-    all = query + '   ' + response1
-    st.write('🔍Run 1 is Complete.')
+    #all = query + '   ' + response1
+    #st.write('🔍Run 1 is Complete.')
 
     # ArXiv searcher ~-<>-~
     client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
-    response2 = client.predict(
+    response1 = client.predict(
     		query,	
     		100,	
     		"Semantic Search - up to 10 Mar 2024",	# Literal['Semantic Search - up to 10 Mar 2024', 'Arxiv Search - Latest - (EXPERIMENTAL)']  in 'Search Source' Dropdown component
     		"mistralai/Mixtral-8x7B-Instruct-v0.1",	# Literal['mistralai/Mixtral-8x7B-Instruct-v0.1', 'mistralai/Mistral-7B-Instruct-v0.2', 'google/gemma-7b-it', 'None']  in 'LLM Model' Dropdown component
     		api_name="/update_with_rag_md"
     )
-    st.write('🔍Run 2 - Multi-Agent Systems is Complete')
+    st.write('🔍Run of Multi-Agent Systems is Complete')
 
 # experimental 45 - - - - - - - - - - - - -<><><><><>
         
     # 🔍Run PaperSummarizer
     PaperSummarizer = '  Create a paper summary as a markdown table clustering the features with short emoji outlines and preserve links, shortening it significantly. For each one create three simple points led by an emoji of the main three steps needed as method step process for implementing the idea as streamlit python app.  '
     # = str(result).replace('\n', ' ').replace('|', '   ')
-    response3 = chat_with_model45(PaperSummarizer + response1 + response2)        
+    response2 = chat_with_model45(PaperSummarizer + response1)        
     st.write('🔍Run 3 - Paper Summarizer is Complete.')
 
     # 🔍Run AppSpecifier
     AppSpecifier = '  Design and write a streamlit python app specification that implements each scientific method steps as function defined and described as a sorted and grouped topic table to outline the AI pipeline ensemble implementing code as full plan to build.'
     #result = str(result).replace('\n', ' ').replace('|', '   ')
-    response4 = chat_with_model45(AppSpecifier + response3)        
+    response3 = chat_with_model45(AppSpecifier + response2)        
     st.write('🔍Run 4 - AppSpecifier is Complete.')
     
     # 🔍Run PythonAppCoder
     PythonAppCoder = '  Create a streamlit python app implementing the following list of features using appropriate libraries and function design using streamlit.  Show full code listing as output with no commentary.  '
     #result = str(result).replace('\n', ' ').replace('|', '   ')
-    response5 = chat_with_model45(PythonAppCoder + response4)        
+    response4 = chat_with_model45(PythonAppCoder + response3)        
     st.write('🔍Run Python AppCoder is Complete.')
 
 # experimental 45 - - - - - - - - - - - - -<><><><><>
     
-    responseAll = query + response1 + response2 + response3 + response4 + response5
+    responseAll = query + response1 + response2 + response3 + response4 + response
     filename = generate_filename(responseAll, "md")
     create_file(filename, query, responseAll, should_save)
     #queries = query + specquery + codequery + webquery