Update app.py

app.py

@@ -228,92 +228,92 @@ def search_glossary(query):
 
     
     # Run Button with Emoji
-    if st.button("🚀 Run"):
-        
-        # 🕵️‍♂️ Searching the glossary for: query
-        all_results = ""
-        st.markdown(f"- {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")
-
-        
-        # 🔍 ArXiv RAG researcher expert ~-<>-~ Paper Summary & Ask LLM - api_name: /ask_llm
-        result = client.predict(
-    		prompt=query,
-    		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)
+    #if st.button("🚀 Run"):
+    
+    # 🕵️‍♂️ Searching the glossary for: query
+    all_results = ""
+    st.markdown(f"- {query}")
 
-        
-        # 🔍 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)
+    # 🔍 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")
 
-        # When saving results, pass the container
-        try:
-            save_to_cosmos_db(st.session_state.cosmos_container, query, result, result)
-            save_to_cosmos_db(st.session_state.cosmos_container, query, result2, result2)
-            save_to_cosmos_db(st.session_state.cosmos_container, query, result3, result3)
-            save_to_cosmos_db(st.session_state.cosmos_container, query, response2[0], response2[0])
-            save_to_cosmos_db(st.session_state.cosmos_container, query, response2[1], response2[1])
-        except exceptions.CosmosHttpResponseError as e:
-            return False, f"HTTP error occurred: {str(e)} 🚨"
-        except Exception as e:
-            return False, f"An unexpected error occurred: {str(e)} 😱"
     
+    # 🔍 ArXiv RAG researcher expert ~-<>-~ Paper Summary & Ask LLM - api_name: /ask_llm
+    result = client.predict(
+        prompt=query,
+        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)
 
-        try:    
-            # Aggregate hyperlinks and show with emojis
-            hyperlinks = extract_hyperlinks([response1, response2])
-            st.markdown("### 🔗 Aggregated Hyperlinks")
-            for link in hyperlinks:
-                st.markdown(f"🔗 [{link}]({link})")
     
-            # Show responses in a code format with line numbers
-            st.markdown("### 📜 Response Outputs with Line Numbers")
-            st.code(f"Response 1: \n{format_with_line_numbers(response1)}\n\nResponse 2: \n{format_with_line_numbers(response2)}", language="json")
-        except exceptions.CosmosHttpResponseError as e:
-            return False, f"HTTP error occurred: {str(e)} 🚨"
-        except Exception as e:
-            return False, f"An unexpected error occurred: {str(e)} 😱"
+    # 🔍 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)
+
+    # When saving results, pass the container
+    try:
+        save_to_cosmos_db(st.session_state.cosmos_container, query, result, result)
+        save_to_cosmos_db(st.session_state.cosmos_container, query, result2, result2)
+        save_to_cosmos_db(st.session_state.cosmos_container, query, result3, result3)
+        save_to_cosmos_db(st.session_state.cosmos_container, query, response2[0], response2[0])
+        save_to_cosmos_db(st.session_state.cosmos_container, query, response2[1], response2[1])
+    except exceptions.CosmosHttpResponseError as e:
+        return False, f"HTTP error occurred: {str(e)} 🚨"
+    except Exception as e:
+        return False, f"An unexpected error occurred: {str(e)} 😱"
+
+
+    try:    
+        # Aggregate hyperlinks and show with emojis
+        hyperlinks = extract_hyperlinks([response1, response2])
+        st.markdown("### 🔗 Aggregated Hyperlinks")
+        for link in hyperlinks:
+            st.markdown(f"🔗 [{link}]({link})")
+
+        # Show responses in a code format with line numbers
+        st.markdown("### 📜 Response Outputs with Line Numbers")
+        st.code(f"Response 1: \n{format_with_line_numbers(response1)}\n\nResponse 2: \n{format_with_line_numbers(response2)}", language="json")
+    except exceptions.CosmosHttpResponseError as e:
+        return False, f"HTTP error occurred: {str(e)} 🚨"
+    except Exception as e:
+        return False, f"An unexpected error occurred: {str(e)} 😱"
+