Update app.py

app.py

@@ -213,29 +213,56 @@ def search_glossary(query):
         # 🔍 ArXiv RAG researcher expert ~-<>-~ Paper Summary & Ask LLM
         #database_choice Literal['Semantic Search', 'Arxiv Search - Latest - (EXPERIMENTAL)'] Default: "Semantic Search"
         #llm_model_picked Literal['mistralai/Mixtral-8x7B-Instruct-v0.1', 'mistralai/Mistral-7B-Instruct-v0.2', 'google/gemma-7b-it', 'None'] Default: "mistralai/Mistral-7B-Instruct-v0.2"
-        
         client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
-        response2 = client.predict(
-            message=query,	# str  in 'parameter_13' Textbox component
-            llm_results_use=10,
-    		database_choice="Semantic Search",
-    		llm_model_picked="mistralai/Mistral-7B-Instruct-v0.2",
-            api_name="/update_with_rag_md"
-        )
-        st.markdown(response2)
-        st.code(response2, language="python", line_numbers=True, wrap_lines=True)
 
-        #llm_model_picked Literal['mistralai/Mixtral-8x7B-Instruct-v0.1', 'mistralai/Mistral-7B-Instruct-v0.2', 'google/gemma-7b-it', 'None'] Default: "mistralai/Mistral-7B-Instruct-v0.2"
-                
-        # 🔍 ArXiv RAG researcher expert ~-<>-~ Paper Summary & Ask LLM
+        
+        # 🔍 ArXiv RAG researcher expert ~-<>-~ Paper Summary & Ask LLM - api_name: /ask_llm
         result = client.predict(
     		prompt=query,
-    		llm_model_picked="mistralai/Mistral-7B-Instruct-v0.2",
+    		llm_model_picked="mistralai/Mixtral-8x7B-Instruct-v0.1",
     		stream_outputs=True,
     		api_name="/ask_llm"
         )
         st.markdown(result)
         st.code(result, language="python", line_numbers=True)
+               
+        # 🔍 ArXiv RAG researcher expert ~-<>-~ Paper Summary & Ask LLM - api_name: /ask_llm
+        result2 = client.predict(
+    		prompt=query,
+    		llm_model_picked="mistralai/Mistral-7B-Instruct-v0.2",
+    		stream_outputs=True,
+    		api_name="/ask_llm"
+        )
+        st.markdown(result2)
+        st.code(result2, language="python", line_numbers=True)
+      
+        # 🔍 ArXiv RAG researcher expert ~-<>-~ Paper Summary & Ask LLM - api_name: /ask_llm
+        result3 = client.predict(
+    		prompt=query,
+    		llm_model_picked="google/gemma-7b-it",
+    		stream_outputs=True,
+    		api_name="/ask_llm"
+        )
+        st.markdown(result3)
+        st.code(result3, language="python", line_numbers=True)
+
+
+        
+        # 🔍 ArXiv RAG researcher expert ~-<>-~ Paper Summary & Ask LLM - api_name: /update_with_rag_md
+        response2 = client.predict(
+            message=query,	# str  in 'parameter_13' Textbox component
+            llm_results_use=10,
+    		database_choice="Semantic Search",
+    		llm_model_picked="mistralai/Mistral-7B-Instruct-v0.2",
+            api_name="/update_with_rag_md"
+        )        # update_with_rag_md     Returns tuple of 2 elements   [0] str   The output value that appears in the "value_14" Markdown component.   [1] str
+
+        st.markdown(response2[0])
+        st.code(response2[0], language="python", line_numbers=True, wrap_lines=True)  
+        
+        st.markdown(response2[1])
+        st.code(response2[1], language="python", line_numbers=True, wrap_lines=True)
+        
         
         # Aggregate hyperlinks and show with emojis
         hyperlinks = extract_hyperlinks([response1, response2])