Spaces:
Sleeping
Sleeping
| from st_aggrid import GridOptionsBuilder, AgGrid | |
| import streamlit as st | |
| from .load_data import load_dataframe, sort_by, show_dataframe_top, search_by_name, validate_categories | |
| 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 in the sidebar.") | |
| st.markdown("By default 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.sidebar.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") | |
| #Sidebar configurations | |
| selection_mode = st.sidebar.radio(label= "Selection mode for the rows", options = ["single", "multiple"], index=0) | |
| st.sidebar.write("In multiple mode, you can select up to three models. If you select more than three models, only the first three will be displayed and plotted.") | |
| ordering_metrics = st.sidebar.text_input(label = "Order of the metrics on the circle, counter-clock wise, beginning at 3 o'clock.", | |
| placeholder = "ARC, GSM8K, TruthfulQA, Winogrande, HellaSwag, MMLU") | |
| ordering_metrics = ordering_metrics.replace(" ", "") | |
| ordering_metrics = ordering_metrics.split(",") | |
| st.sidebar.markdown(""" | |
| As a reminder, here are the different metrics: | |
| * ARC | |
| * GSM8K | |
| * TruthfulQA | |
| * Winogrande | |
| * HellaSwag | |
| * MMLU | |
| """) | |
| st.sidebar.markdown(""" | |
| If there are **typos** in the name of the metrics, or the number of metrics | |
| is **different of six**, there will be no effect on the chart and the | |
| default ordering will be used. | |
| """) | |
| valid_categories = validate_categories(ordering_metrics) | |
| # Search bar | |
| 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 = selection_mode, 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 = None | |
| if valid_categories: | |
| figure = plot_radar_chart_rows(rows=grid_response['selected_rows'][:3], categories = ordering_metrics) | |
| else: | |
| figure = plot_radar_chart_rows(rows=grid_response['selected_rows'][:3]) | |
| st.plotly_chart(figure, use_container_width=False) | |
| else: | |
| if len(subdata)>0: | |
| figure = None | |
| if valid_categories: | |
| figure = plot_radar_chart_name(dataframe=subdata, categories = ordering_metrics, model_name=model_name) | |
| else: | |
| 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']) > 1: | |
| n_col = len(grid_response['selected_rows']) if len(grid_response['selected_rows']) <=3 else 3 | |
| st.markdown("## Models") | |
| columns = st.columns(n_col) | |
| for i in range(n_col): | |
| with columns[i]: | |
| st.markdown("**Model name:** %s" % grid_response['selected_rows'][i]["model_name"]) | |
| elif grid_response['selected_rows'] is not None and len(grid_response['selected_rows']) == 1: | |
| st.markdown("**Model name:** %s" % grid_response['selected_rows'][0]["model_name"]) | |
| else: | |
| st.markdown("**Model name:** %s" % model_name) |