rerank model

pages/Multimodal_Conversational_Search.py

@@ -200,86 +200,86 @@ def write_user_message(placeholder,md):
 
 def render_answer(placeholder,question,answer,index,res_img):
     
-    
-    col1, col2, col_3 = st.columns([4,74,22])
-    with col1:
-        st.image(AI_ICON, use_column_width='always')
-    with col2:
-        ans_ = answer['answer']
-        st.write(ans_)
-
-        # polly_response = polly_client.synthesize_speech(VoiceId='Joanna',
-        #                 OutputFormat='ogg_vorbis', 
-        #                 Text = ans_,
-        #                 Engine = 'neural')
-
-        # audio_col1, audio_col2 = st.columns([50,50])
-        # with audio_col1:
-        #     st.audio(polly_response['AudioStream'].read(), format="audio/ogg")
-        rdn_key_1 = ''.join([random.choice(string.ascii_letters)
-                              for _ in range(10)])
-        def show_maxsim(placeholder,dummy):
-            st.session_state.show_columns = True
-            st.session_state.maxSimImages = colpali.img_highlight(st.session_state.top_img, st.session_state.query_token_vectors, st.session_state.query_tokens)
-            handle_input()
-            # placeholder = st.empty()
-            # with placeholder.container():
-            render_all(placeholder)    
-        if(st.session_state.input_is_colpali):
-            st.button("Show similarity map",key=rdn_key_1,on_click = show_maxsim,args=(placeholder,"default_img"))
-                
-    colu1,colu2,colu3 = st.columns([4,82,20])
-    with colu2:
-        @st.cache_data
-        def load_table_from_file(filepath):
-            df = pd.read_csv(filepath, skipinitialspace=True, on_bad_lines='skip', delimiter='`')
-            df.fillna(method='pad', inplace=True)
-            return df
-
-        with st.expander("Relevant Sources:"):
-            with st.container():
-                if(len(res_img)>0):
-                    #with st.expander("Images:"):
-                        
-                    idx = 0
-                    print(res_img)
-                    for i in range(0,len(res_img)):
-                        
-                        if(st.session_state.input_is_colpali):
-                            if(st.session_state.show_columns == True):
-                                cols_per_row = 3
-                                st.session_state.image_placeholder=st.empty()
-                                with st.session_state.image_placeholder.container():
-                                    row = st.columns(cols_per_row)
-                                for j, item in enumerate(res_img[i:i+cols_per_row]):
-                                    with row[j]:
-                                        st.image(item['file'])
-                                
-                            else:
-                                st.session_state.image_placeholder = st.empty()
-                                with st.session_state.image_placeholder.container():
-                                    col3_,col4_,col5_ = st.columns([33,33,33])
-                                    with col3_:
-                                        st.image(res_img[i]['file'])
-                        else:
-                            if(res_img[i]['file'].lower()!='none' and idx < 1):
-                                col3,col4,col5 = st.columns([33,33,33])
-                                cols = [col3,col4]
-                                img = res_img[i]['file'].split(".")[0]
-                                caption = res_img[i]['caption']
+    with placeholder.container():
+        col1, col2, col_3 = st.columns([4,74,22])
+        with col1:
+            st.image(AI_ICON, use_column_width='always')
+        with col2:
+            ans_ = answer['answer']
+            st.write(ans_)
+
+            # polly_response = polly_client.synthesize_speech(VoiceId='Joanna',
+            #                 OutputFormat='ogg_vorbis', 
+            #                 Text = ans_,
+            #                 Engine = 'neural')
+
+            # audio_col1, audio_col2 = st.columns([50,50])
+            # with audio_col1:
+            #     st.audio(polly_response['AudioStream'].read(), format="audio/ogg")
+            rdn_key_1 = ''.join([random.choice(string.ascii_letters)
+                                for _ in range(10)])
+            def show_maxsim(placeholder,dummy):
+                st.session_state.show_columns = True
+                st.session_state.maxSimImages = colpali.img_highlight(st.session_state.top_img, st.session_state.query_token_vectors, st.session_state.query_tokens)
+                handle_input()
+                # placeholder = st.empty()
+                # with placeholder.container():
+                render_all(placeholder)    
+            if(st.session_state.input_is_colpali):
+                st.button("Show similarity map",key=rdn_key_1,on_click = show_maxsim,args=(placeholder,"default_img"))
+                    
+        colu1,colu2,colu3 = st.columns([4,82,20])
+        with colu2:
+            @st.cache_data
+            def load_table_from_file(filepath):
+                df = pd.read_csv(filepath, skipinitialspace=True, on_bad_lines='skip', delimiter='`')
+                df.fillna(method='pad', inplace=True)
+                return df
+
+            with st.expander("Relevant Sources:"):
+                with st.container():
+                    if(len(res_img)>0):
+                        #with st.expander("Images:"):
                             
-                                with cols[idx]:
+                        idx = 0
+                        print(res_img)
+                        for i in range(0,len(res_img)):
+                            
+                            if(st.session_state.input_is_colpali):
+                                if(st.session_state.show_columns == True):
+                                    cols_per_row = 3
+                                    st.session_state.image_placeholder=st.empty()
+                                    with st.session_state.image_placeholder.container():
+                                        row = st.columns(cols_per_row)
+                                    for j, item in enumerate(res_img[i:i+cols_per_row]):
+                                        with row[j]:
+                                            st.image(item['file'])
                                     
-                                    st.image(parent_dirname+"/figures/"+st.session_state.input_index+"/"+img+".jpg")
-                            idx = idx+1
-                    if(st.session_state.show_columns == True):
-                        st.session_state.show_columns = False
-                if(len(answer["table"] )>0):
-                    #with st.expander("Table:"):
-                    df = load_table_from_file(answer["table"][0]['name'])
-                    st.table(df)
-                #with st.expander("Raw sources:"):
-                st.write(answer["source"])
+                                else:
+                                    st.session_state.image_placeholder = st.empty()
+                                    with st.session_state.image_placeholder.container():
+                                        col3_,col4_,col5_ = st.columns([33,33,33])
+                                        with col3_:
+                                            st.image(res_img[i]['file'])
+                            else:
+                                if(res_img[i]['file'].lower()!='none' and idx < 1):
+                                    col3,col4,col5 = st.columns([33,33,33])
+                                    cols = [col3,col4]
+                                    img = res_img[i]['file'].split(".")[0]
+                                    caption = res_img[i]['caption']
+                                
+                                    with cols[idx]:
+                                        
+                                        st.image(parent_dirname+"/figures/"+st.session_state.input_index+"/"+img+".jpg")
+                                idx = idx+1
+                        if(st.session_state.show_columns == True):
+                            st.session_state.show_columns = False
+                    if(len(answer["table"] )>0):
+                        #with st.expander("Table:"):
+                        df = load_table_from_file(answer["table"][0]['name'])
+                        st.table(df)
+                    #with st.expander("Raw sources:"):
+                    st.write(answer["source"])
                
         
     # with col_3: