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import argparse
import asyncio
import json
import math
import sys
# https://github.com/jerryjliu/llama_index/issues/7244:
asyncio.set_event_loop(asyncio.new_event_loop())
from millify import millify
import numpy as np
import streamlit as st
from streamlit_extras.switch_page_button import switch_page
from trulens_eval.db_migration import MIGRATION_UNKNOWN_STR
from trulens_eval.ux.styles import CATEGORY
st.runtime.legacy_caching.clear_cache()
from trulens_eval import Tru
from trulens_eval.ux import styles
from trulens_eval.ux.components import draw_metadata
st.set_page_config(page_title="Leaderboard", layout="wide")
from trulens_eval.ux.add_logo import add_logo_and_style_overrides
add_logo_and_style_overrides()
database_url = None
def streamlit_app():
tru = Tru(database_file="./models/trulens_eval.sqlite")
lms = tru.db
# Set the title and subtitle of the app
st.title("App Leaderboard")
st.write(
"Average feedback values displayed in the range from 0 (worst) to 1 (best)."
)
df, feedback_col_names = lms.get_records_and_feedback([])
feedback_defs = lms.get_feedback_defs()
feedback_directions = {
(
row.feedback_json.get("supplied_name", "") or
row.feedback_json["implementation"]["name"]
): row.feedback_json.get("higher_is_better", True)
for _, row in feedback_defs.iterrows()
}
if df.empty:
st.write("No records yet...")
return
df = df.sort_values(by="app_id")
if df.empty:
st.write("No records yet...")
apps = list(df.app_id.unique())
st.markdown("""---""")
for app in apps:
app_df = df.loc[df.app_id == app]
if app_df.empty:
continue
app_str = app_df["app_json"].iloc[0]
app_json = json.loads(app_str)
metadata = app_json.get("metadata")
# st.text('Metadata' + str(metadata))
st.header(app, help=draw_metadata(metadata))
app_feedback_col_names = [
col_name for col_name in feedback_col_names
if not app_df[col_name].isna().all()
]
col1, col2, col3, col4, *feedback_cols, col99 = st.columns(
5 + len(app_feedback_col_names)
)
latency_mean = (
app_df["latency"].
apply(lambda td: td if td != MIGRATION_UNKNOWN_STR else None).mean()
)
# app_df_feedback = df.loc[df.app_id == app]
col1.metric("Records", len(app_df))
col2.metric(
"Average Latency (Seconds)",
(
f"{millify(round(latency_mean, 5), precision=2)}"
if not math.isnan(latency_mean) else "nan"
),
)
col3.metric(
"Total Cost (USD)",
f"${millify(round(sum(cost for cost in app_df.total_cost if cost is not None), 5), precision = 2)}",
)
col4.metric(
"Total Tokens",
millify(
sum(
tokens for tokens in app_df.total_tokens
if tokens is not None
),
precision=2
),
)
for i, col_name in enumerate(app_feedback_col_names):
mean = app_df[col_name].mean()
st.write(
styles.stmetricdelta_hidearrow,
unsafe_allow_html=True,
)
higher_is_better = feedback_directions.get(col_name, True)
if "distance" in col_name:
feedback_cols[i].metric(
label=col_name,
value=f"{round(mean, 2)}",
delta_color="normal"
)
else:
cat = CATEGORY.of_score(mean, higher_is_better=higher_is_better)
feedback_cols[i].metric(
label=col_name,
value=f"{round(mean, 2)}",
delta=f"{cat.icon} {cat.adjective}",
delta_color=(
"normal" if cat.compare(
mean, CATEGORY.PASS[cat.direction].threshold
) else "inverse"
),
)
with col99:
if st.button("Select App", key=f"app-selector-{app}"):
st.session_state.app = app
switch_page("Evaluations")
# with st.expander("Model metadata"):
# st.markdown(draw_metadata(metadata))
st.markdown("""---""")
# Define the main function to run the app
def main():
streamlit_app()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--database-url", default=None)
try:
args = parser.parse_args()
except SystemExit as e:
# This exception will be raised if --help or invalid command line arguments
# are used. Currently, streamlit prevents the program from exiting normally,
# so we have to do a hard exit.
sys.exit(e.code)
database_url = args.database_url
main()
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