Spaces:
Running
on
T4
Running
on
T4
rerank model
Browse files
pages/Multimodal_Conversational_Search.py
CHANGED
@@ -200,86 +200,86 @@ def write_user_message(placeholder,md):
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def render_answer(placeholder,question,answer,index,res_img):
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idx = 0
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print(res_img)
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for i in range(0,len(res_img)):
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if(st.session_state.input_is_colpali):
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if(st.session_state.show_columns == True):
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cols_per_row = 3
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st.session_state.image_placeholder=st.empty()
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with st.session_state.image_placeholder.container():
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row = st.columns(cols_per_row)
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for j, item in enumerate(res_img[i:i+cols_per_row]):
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with row[j]:
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st.image(item['file'])
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else:
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st.session_state.image_placeholder = st.empty()
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with st.session_state.image_placeholder.container():
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col3_,col4_,col5_ = st.columns([33,33,33])
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with col3_:
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st.image(res_img[i]['file'])
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else:
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if(res_img[i]['file'].lower()!='none' and idx < 1):
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col3,col4,col5 = st.columns([33,33,33])
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cols = [col3,col4]
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img = res_img[i]['file'].split(".")[0]
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caption = res_img[i]['caption']
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# with col_3:
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def render_answer(placeholder,question,answer,index,res_img):
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with placeholder.container():
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col1, col2, col_3 = st.columns([4,74,22])
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with col1:
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st.image(AI_ICON, use_column_width='always')
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with col2:
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ans_ = answer['answer']
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st.write(ans_)
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# polly_response = polly_client.synthesize_speech(VoiceId='Joanna',
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# OutputFormat='ogg_vorbis',
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# Text = ans_,
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# Engine = 'neural')
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# audio_col1, audio_col2 = st.columns([50,50])
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# with audio_col1:
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# st.audio(polly_response['AudioStream'].read(), format="audio/ogg")
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rdn_key_1 = ''.join([random.choice(string.ascii_letters)
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for _ in range(10)])
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def show_maxsim(placeholder,dummy):
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st.session_state.show_columns = True
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st.session_state.maxSimImages = colpali.img_highlight(st.session_state.top_img, st.session_state.query_token_vectors, st.session_state.query_tokens)
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handle_input()
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# placeholder = st.empty()
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# with placeholder.container():
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render_all(placeholder)
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if(st.session_state.input_is_colpali):
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st.button("Show similarity map",key=rdn_key_1,on_click = show_maxsim,args=(placeholder,"default_img"))
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colu1,colu2,colu3 = st.columns([4,82,20])
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with colu2:
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@st.cache_data
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def load_table_from_file(filepath):
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df = pd.read_csv(filepath, skipinitialspace=True, on_bad_lines='skip', delimiter='`')
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df.fillna(method='pad', inplace=True)
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return df
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with st.expander("Relevant Sources:"):
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with st.container():
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if(len(res_img)>0):
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#with st.expander("Images:"):
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idx = 0
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print(res_img)
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for i in range(0,len(res_img)):
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if(st.session_state.input_is_colpali):
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if(st.session_state.show_columns == True):
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cols_per_row = 3
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st.session_state.image_placeholder=st.empty()
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with st.session_state.image_placeholder.container():
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row = st.columns(cols_per_row)
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for j, item in enumerate(res_img[i:i+cols_per_row]):
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with row[j]:
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st.image(item['file'])
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else:
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st.session_state.image_placeholder = st.empty()
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with st.session_state.image_placeholder.container():
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col3_,col4_,col5_ = st.columns([33,33,33])
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with col3_:
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st.image(res_img[i]['file'])
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else:
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if(res_img[i]['file'].lower()!='none' and idx < 1):
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col3,col4,col5 = st.columns([33,33,33])
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cols = [col3,col4]
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img = res_img[i]['file'].split(".")[0]
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caption = res_img[i]['caption']
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with cols[idx]:
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st.image(parent_dirname+"/figures/"+st.session_state.input_index+"/"+img+".jpg")
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idx = idx+1
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if(st.session_state.show_columns == True):
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st.session_state.show_columns = False
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if(len(answer["table"] )>0):
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#with st.expander("Table:"):
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df = load_table_from_file(answer["table"][0]['name'])
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st.table(df)
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#with st.expander("Raw sources:"):
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st.write(answer["source"])
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# with col_3:
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