Update app.py

app.py

@@ -92,6 +92,44 @@ def SpeechSynthesis(result):
     '''
     components.html(documentHTML5, width=1280, height=300)
 
+
+
+ # 🔍Search Glossary 
+# @st.cache_resource
+def search_glossary(query): 
+    all=""
+    st.markdown(f"- {query}")
+    
+    # 🔍Run 1 - ArXiv RAG researcher expert ~-<>-~ Paper Summary & Ask LLM
+    client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
+    response2 = client.predict(
+            query,	# str  in 'parameter_13' Textbox 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
+            #"mistralai/Mistral-7B-Instruct-v0.2",	# Literal['mistralai/Mixtral-8x7B-Instruct-v0.1', 'mistralai/Mistral-7B-Instruct-v0.2', 'google/gemma-7b-it', 'None']  in 'LLM Model' Dropdown component
+            "google/gemma-7b-it",	# Literal['mistralai/Mixtral-8x7B-Instruct-v0.1', 'mistralai/Mistral-7B-Instruct-v0.2', 'google/gemma-7b-it', 'None']  in 'LLM Model' Dropdown component
+            True,	# bool  in 'Stream output' Checkbox component
+            api_name="/ask_llm"
+    )
+    st.write('🔍Run of Multi-Agent System Paper Summary Spec is Complete')
+    st.markdown(response2)
+
+    # ArXiv searcher ~-<>-~ Paper References - Update with RAG
+    client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
+    response1 = client.predict(
+    		query,	
+    		10,	
+    		"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 of Multi-Agent System Paper References is Complete')
+    responseall = response2 + response1[0] + response1[1]
+    st.markdown(responseall)
+    return responseall
+
+
+
+
 def parse_to_markdown(text):
     return text
         
@@ -587,13 +625,10 @@ def FileSidebar():
                 if new_file_content_area != file_contents:
                     st.markdown(new_file_content_area) #changed
 
-            if st.button("🔍 Run AI Meta Strategy", key="filecontentssearch"):
-                #search_glossary(file_content_area)
-                filesearch = PromptPrefix + file_content_area
+        if next_action=='search':  
+                filesearch = PromptPrefix + file_contents
                 st.markdown(filesearch)
-                
-                if st.button(key=rerun, label='🔍AI Search' ):
-                    search_glossary(filesearch)
+                search_glossary(filesearch)
 
         if next_action=='md':
             st.markdown(file_contents)
@@ -628,7 +663,7 @@ def FileSidebar():
             #search_glossary(file_contents)
             filesearch = PromptPrefix2 + file_content_area
             st.markdown(filesearch)
-            if st.button(key=rerun, label='🔍Re-Code' ):
+            if st.button(key='rerun', label='🔍Re-Code' ):
                 #search_glossary(filesearch)
                 search_arxiv(filesearch)
                     
@@ -723,39 +758,6 @@ def load_score(key):
     return 0
 
 
- # 🔍Search Glossary 
-@st.cache_resource
-def search_glossary(query): 
-    all=""
-    st.markdown(f"- {query}")
-    
-    # 🔍Run 1 - ArXiv RAG researcher expert ~-<>-~ Paper Summary & Ask LLM
-    client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
-    response2 = client.predict(
-            query,	# str  in 'parameter_13' Textbox 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
-            #"mistralai/Mistral-7B-Instruct-v0.2",	# Literal['mistralai/Mixtral-8x7B-Instruct-v0.1', 'mistralai/Mistral-7B-Instruct-v0.2', 'google/gemma-7b-it', 'None']  in 'LLM Model' Dropdown component
-            "google/gemma-7b-it",	# Literal['mistralai/Mixtral-8x7B-Instruct-v0.1', 'mistralai/Mistral-7B-Instruct-v0.2', 'google/gemma-7b-it', 'None']  in 'LLM Model' Dropdown component
-            True,	# bool  in 'Stream output' Checkbox component
-            api_name="/ask_llm"
-    )
-    st.write('🔍Run of Multi-Agent System Paper Summary Spec is Complete')
-    st.markdown(response2)
-
-    # ArXiv searcher ~-<>-~ Paper References - Update with RAG
-    client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
-    response1 = client.predict(
-    		query,	
-    		10,	
-    		"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 of Multi-Agent System Paper References is Complete')
-    responseall = response2 + response1[0] + response1[1]
-    st.markdown(responseall)
-    return responseall
-
 
 # Function to display the glossary in a structured format
 def display_glossary(glossary, area):