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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 | |
| 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.") | |
| #container = st.container(height = 150) | |
| dataframe = load_dataframe() | |
| sort_selection = st.selectbox(label = "Sort by:", options = list(dataframe.columns)) | |
| ascending = True | |
| indexes = None | |
| 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") | |
| if name is not None: | |
| indexes = dataframe["model_name"].str.contains(name) | |
| if len(indexes) > 0: | |
| dataframe = dataframe[indexes] | |
| else: | |
| dataframe = load_dataframe() | |
| dataframe = sort_by(dataframe=dataframe, column_name=sort_selection, ascending= ascending) | |
| dataframe_display = dataframe.copy() | |
| 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: | |
| #with container: | |
| #st.dataframe(dataframe_display) | |
| 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']) | |
| 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) |