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from st_aggrid import GridOptionsBuilder, AgGrid
from streamlit_searchbox import st_searchbox
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("## Some explanations")
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, or search it by its name in the search boxes **Model 1, Model 2, or Model 3** bellow.")
st.markdown("You can select up to three models using the search boxes and/or the checkboxes.")
st.markdown("""In the case you use both the search boxes and the checkboxes, the search boxes will take precedence over the checkboxes,
i.e. the models searched using the search boxes will be prioritized over the ones selected using the checkboxes.
Please, search models using the search boxes first, and then use the checkboxes.
""")
st.markdown("This app 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()
categories_display = ["ARC", "GSM8K", "TruthfulQA", "Winogrande", "HellaSwag", "MMLU", "Average"]
st.markdown("## Leaderboard")
sort_selection = st.selectbox(label = "Sort by:", options = list(dataframe.columns.difference(["model_dtype"])), index = 1)
d_type_options = ["all", "torch.bfloat16", "torch.float16", "4bit", "8bit"]
d_type = st.radio(label = "Filter by dtype", options = d_type_options, index = 0, horizontal = True)
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
# Dynamic search boxes
def search_model(model_name: str):
model_list = None
if model_name is not None or model_name != "":
models = dataframe["model_name"].str.contains(model_name)
model_list = dataframe["model_name"][models]
else:
model_list = dataframe["model_name"]
return model_list
model_list = []
#Sidebar configurations
selection_mode = st.sidebar.radio(label= "Selection mode for the rows", options = ["single", "multiple"], index=1)
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)
dataframe = sort_by(dataframe=dataframe, column_name=sort_selection, ascending= ascending)
if d_type != "all":
dataframe = dataframe[dataframe["model_dtype"] == d_type]
dataframe_display = dataframe.copy()
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%'
)
model_one = st_searchbox(label = "Model 1", search_function = search_model, key = "model_1", default= None)
model_two = st_searchbox(label = "Model 2", search_function = search_model, key = "model_2", default= None)
model_three = st_searchbox(label = "Model 3", search_function = search_model, key = "model_3", default= None)
if model_one is not None:
row = dataframe[dataframe["model_name"] == model_one]
row[categories_display] = row[categories_display]*100
model_list.append(row.to_dict("records")[0])
if model_two is not None:
row = dataframe[dataframe["model_name"] == model_two]
row[categories_display] = row[categories_display]*100
model_list.append(row.to_dict("records")[0])
if model_three is not None:
row = dataframe[dataframe["model_name"] == model_three]
row[categories_display] = row[categories_display]*100
model_list.append(row.to_dict("records")[0])
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
#grid_response is now a Pandas dataframe, we need to
# convert to dict in order to merge with the searchboxes' results
model_list += grid_response['selected_rows'].to_dict("records")
model_list = model_list[:3]
model_list = sorted(model_list, key = lambda x: x["Average"], reverse = True)
if valid_categories:
figure = plot_radar_chart_rows(rows=model_list, categories = ordering_metrics)
else:
figure = plot_radar_chart_rows(rows=model_list)
st.plotly_chart(figure, use_container_width=False)
elif len(model_list) > 0:
figure = None
model_list = sorted(model_list, key = lambda x: x["Average"], reverse = True)
if valid_categories:
figure = plot_radar_chart_rows(rows=model_list, categories = ordering_metrics)
else:
figure = plot_radar_chart_rows(rows=model_list)
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 len(model_list) > 1:
n_col = len(model_list) if len(model_list) <=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](https://huggingface.co/%s)" % (model_list[i]["model_name"] , model_list[i]["model_name"]))
st.markdown("**Results:**")
st.markdown("""
* Average: %s
* ARC: %s
* GSM8K: %s
* TruthfulQA: %s
* Winogrande: %s
* HellaSwag: %s
* MMLU: %s
""" % (round(model_list[i]["Average"],2),
round(model_list[i]["ARC"],2),
round(model_list[i]["GSM8K"],2),
round(model_list[i]["TruthfulQA"],2),
round(model_list[i]["Winogrande"],2),
round(model_list[i]["HellaSwag"],2),
round(model_list[i]["MMLU"],2)
))
st.markdown("**dtype:** %s" % model_list[i]["model_dtype"])
elif len(model_list) == 1:
st.markdown("**Model name:** [%s](https://huggingface.co/%s)" % (model_list[0]["model_name"] , model_list[0]["model_name"]))
st.markdown("**Results:**")
st.markdown("""
* Average: %s
* ARC: %s
* GSM8K: %s
* TruthfulQA: %s
* Winogrande: %s
* HellaSwag: %s
* MMLU: %s
""" % (round(model_list[0]["Average"],2),
round(model_list[0]["ARC"],2),
round(model_list[0]["GSM8K"],2),
round(model_list[0]["TruthfulQA"],2),
round(model_list[0]["Winogrande"],2),
round(model_list[0]["HellaSwag"],2),
round(model_list[0]["MMLU"],2)
))
st.markdown("**dtype:** %s" % model_list[0]["model_dtype"])
st.markdown("For more details, hover over the radar chart.")
else:
st.markdown("**Model name:** %s" % model_name)
st.markdown("For more details, select the first model in the list/leaderboard.")
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