adding runtime info

appStore/coherence.py

@@ -77,6 +77,17 @@ def app():
             investment needs). The assignment of the paragraph to a corresponding\
             indicator is based on vector similarities in which top 3 results
             if found are shown to the user. """)
+        st.write("")
+        col1,col2= st.columns(2)
+        with col1:
+            st.caption("OCR File processing")
+            # st.markdown('<div style="text-align: center;">50 sec</div>', unsafe_allow_html=True)
+            st.write("50 sec")
+           
+        with col2:
+            st.caption("NDC comparison on 200 paragraphs(~ 35 pages)")
+            # st.markdown('<div style="text-align: center;">12 sec</div>', unsafe_allow_html=True)
+            st.write("140 sec")
     
     with st.sidebar:
 

appStore/keyword_search.py

@@ -70,8 +70,25 @@ def app():
         st.write("")
         st.write(""" The Exact Matches gives back top {} findings, and Semantic
         search provides with top {} answers.""".format(lexical_top_k, retriever_top_k))
-
-    
+        st.write("")
+        st.write("")
+        st.markdown("Some runtime metrics tested with cpu: Intel(R) Xeon(R) CPU @ 2.20GHz, memory: 13GB")
+        col1,col2,col3= st.columns([2,4,4])
+        with col1:
+            st.caption("OCR File processing")
+            # st.markdown('<div style="text-align: center;">50 sec</div>', unsafe_allow_html=True)
+            st.write("50 sec")
+           
+        with col2:
+            st.caption("Lexical Search on 200 paragraphs(~ 35 pages)")
+            # st.markdown('<div style="text-align: center;">12 sec</div>', unsafe_allow_html=True)
+            st.write("15 sec")
+           
+        with col3:
+            st.caption("Semantic search on 200 paragraphs(~ 35 pages)")
+            # st.markdown('<div style="text-align: center;">120 sec</div>', unsafe_allow_html=True)
+            st.write("120 sec(excluding emebedding creation)")
+ 
     with st.sidebar:
         with open('docStore/sample/keywordexample.json','r') as json_file:
             keywordexample = json.load(json_file)

appStore/sdg_analysis.py

@@ -87,20 +87,20 @@ def app():
         col1,col2,col3,col4 = st.columns([2,2,3,3])
         with col1:
             st.caption("Loading Time Classifier")
-            st.markdown('<div style="text-align: center;">12 sec</div>', unsafe_allow_html=True)
-            # st.write("12 sec")
+            # st.markdown('<div style="text-align: center;">12 sec</div>', unsafe_allow_html=True)
+            st.write("12 sec")
         with col2:
             st.caption("OCR File processing")
-            st.markdown('<div style="text-align: center;">50 sec</div>', unsafe_allow_html=True)
-            # st.write("50 sec")
+            # st.markdown('<div style="text-align: center;">50 sec</div>', unsafe_allow_html=True)
+            st.write("50 sec")
         with col3:
-            st.caption("SDG Classification of 200 paragraphs")
-            st.markdown('<div style="text-align: center;">120 sec</div>', unsafe_allow_html=True)
-            # st.write("120 sec")
+            st.caption("SDG Classification of 200 paragraphs(~ 35 pages)")
+            # st.markdown('<div style="text-align: center;">120 sec</div>', unsafe_allow_html=True)
+            st.write("120 sec")
         with col4:
-            st.caption("Keyword extraction for 200 paragraphs")
-            st.markdown('<div style="text-align: center;">3 sec</div>', unsafe_allow_html=True)
-            # st.write("3 sec")
+            st.caption("Keyword extraction for 200 paragraphs(~ 35 pages)")
+            # st.markdown('<div style="text-align: center;">3 sec</div>', unsafe_allow_html=True)
+            st.write("3 sec")