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#import streamlit as st
#from src.load_data import load_dataframe, sort_by
#from src.plot import plot_radar_chart_index, plot_radar_chart_name
#from st_aggrid import GridOptionsBuilder, AgGrid
from st_aggrid import GridOptionsBuilder, AgGrid
import streamlit as st
from .load_data import load_dataframe, sort_by, show_dataframe_top, search_by_name
from .plot import plot_radar_chart_name, plot_radar_chart_rows
def display_app():
st.markdown("# Open LLM Leaderboard Viz")
st.markdown("This is a visualization of the results in [open-llm-leaderboard/results](https://huggingface.co/datasets/open-llm-leaderboard/results)")
st.markdown("To select a model, click on the checkbox beside its name.")
st.markdown("This displays the top 100 models by default, but you can change that using the number input below.")
st.markdown("By defalut as well, the maximum number of row you can display is 500, it is due to the problem with st__aggrid component loading.")
st.markdown("If your model doesn't show up, please search it by its name.")
dataframe = load_dataframe()
sort_selection = st.selectbox(label = "Sort by:", options = list(dataframe.columns), index = 7)
number_of_row = st.number_input("Number of top rows to display", min_value=100, max_value=500, value="min", step=100)
ascending = True
if sort_selection is None:
sort_selection = "model_name"
ascending = True
elif sort_selection == "model_name":
ascending = True
else:
ascending = False
name = st.text_input(label = ":mag: Search by name")
len_name_input = len(name)
if len_name_input > 0:
dataframe_by_search = search_by_name(name)
if len(dataframe_by_search) > 0:
#st.write("number of model name with name", len(dataframe_by_search))
dataframe = dataframe_by_search
else:
dataframe = load_dataframe()
dataframe = sort_by(dataframe=dataframe, column_name=sort_selection, ascending= ascending)
dataframe_display = dataframe.copy()
if len_name_input == 0:
# Show every only top n row
dataframe_display = show_dataframe_top(number_of_row,dataframe_display)
dataframe_display[["ARC", "HellaSwag", "TruthfulQA", "Winogrande", "GSM8K" ,"MMLU", "Average"]] = dataframe[["ARC", "HellaSwag", "TruthfulQA", "Winogrande", "GSM8K" ,"MMLU", "Average"]].astype(float)
dataframe_display[["ARC", "HellaSwag", "TruthfulQA", "Winogrande", "GSM8K" ,"MMLU", "Average"]] = dataframe_display[["ARC", "HellaSwag", "TruthfulQA", "Winogrande", "GSM8K" ,"MMLU", "Average"]] *100
dataframe_display[["ARC", "HellaSwag", "TruthfulQA", "Winogrande", "GSM8K" ,"MMLU", "Average"]] = dataframe_display[["ARC", "HellaSwag", "TruthfulQA", "Winogrande", "GSM8K" ,"MMLU", "Average"]].round(2)
#Infer basic colDefs from dataframe types
gb = GridOptionsBuilder.from_dataframe(dataframe_display)
gb.configure_selection(selection_mode = "single", use_checkbox=True)
gb.configure_grid_options(domLayout='normal')
gridOptions = gb.build()
column1,col3, column2 = st.columns([0.26, 0.05, 0.69], gap = "small")
with column1:
grid_response = AgGrid(
dataframe_display,
gridOptions=gridOptions,
height=300,
width='40%'
)
subdata = dataframe.head(1)
if len(subdata) > 0:
model_name = subdata["model_name"].values[0]
else:
model_name = ""
with column2:
if grid_response['selected_rows'] is not None and len(grid_response['selected_rows']) > 0:
figure = plot_radar_chart_rows(rows=grid_response['selected_rows'])
#figure = plot_radar_chart_name(dataframe= dataframe, model_name=grid_response['selected_rows'][0]["model_name"])
st.plotly_chart(figure, use_container_width=True)
else:
if len(subdata)>0:
figure = plot_radar_chart_name(dataframe=subdata, model_name=model_name)
st.plotly_chart(figure, use_container_width=True)
if grid_response['selected_rows'] is not None and len(grid_response['selected_rows']) > 0:
st.markdown("**Model name:** %s" % grid_response['selected_rows'][0]["model_name"])
else:
st.markdown("**Model name:** %s" % model_name) |