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import gradio as gr
import pandas as pd
from glob import glob
from pathlib import Path
from tabs.dashboard import df
from tabs.faq import (
about_olas_predict_benchmark,
about_olas_predict,
about_the_dataset,
about_the_tools
)
from tabs.howto_benchmark import how_to_run
from tabs.run_benchmark import run_benchmark_main
demo = gr.Blocks()
def run_benchmark_gradio(tool_name, model_name, openai_api_key, anthropic_api_key):
"""Run the benchmark using inputs."""
if tool_name is None:
return "Please enter the name of your tool."
if openai_api_key is None and anthropic_api_key is None:
return "Please enter either OpenAI or Anthropic API key."
result = run_benchmark_main(tool_name, model_name, openai_api_key, anthropic_api_key)
if result == 'completed':
# get the results file in the results directory
fns = glob('results/*.csv')
print(f"Number of files in results directory: {len(fns)}")
# convert to Path
files = [Path(file) for file in fns]
# get results and summary files
results_files = [file for file in files if 'results' in file.name]
# the other file is the summary file
summary_files = [file for file in files if 'summary' in file.name]
print(results_files, summary_files)
# get the path with results
results_df = pd.read_csv(results_files[0])
summary_df = pd.read_csv(summary_files[0])
# make sure all df float values are rounded to 4 decimal places
results_df = results_df.round(4)
summary_df = summary_df.round(4)
return gr.Dataframe(value=results_df), gr.Dataframe(value=summary_df)
return gr.Textbox(label="Benchmark Result", value=result, interactive=False), gr.Textbox(label="Summary", value="")
with demo:
gr.HTML("<h1>Olas Predict Benchmark</hjson>")
gr.Markdown("Leaderboard showing the performance of Olas Predict tools on the Autocast dataset and overview of the project.")
with gr.Tabs() as tabs:
# first tab - leaderboard
with gr.TabItem("🏅 Benchmark Leaderboard", id=0):
gr.components.Dataframe(
value=df,
)
# second tab - about
with gr.TabItem("ℹ️ About"):
with gr.Row():
with gr.Accordion("About the Benchmark", open=False):
gr.Markdown(about_olas_predict_benchmark)
with gr.Row():
with gr.Accordion("About the Tools", open=False):
gr.Markdown(about_the_tools)
with gr.Row():
with gr.Accordion("About the Autocast Dataset", open=False):
gr.Markdown(about_the_dataset)
with gr.Row():
with gr.Accordion("About Olas", open=False):
gr.Markdown(about_olas_predict)
# third tab - how to run the benchmark
with gr.TabItem("🚀 Contribute"):
gr.Markdown(how_to_run)
def update_dropdown(tool):
if "claude" in tool:
return ["claude-3-haiku-20240307", "claude-3-sonnet-20240229", "claude-3-opus-20240229"]
else:
return ["gpt-3.5-turbo-0125", "gpt-4-0125-preview"]
# fourth tab - run the benchmark
with gr.TabItem("🔥 Run the Benchmark"):
with gr.Row():
tool_name = gr.Dropdown(
[
"prediction-offline",
"prediction-online",
# "prediction-online-summarized-info",
"prediction-offline-sme",
"prediction-online-sme",
"claude-prediction-offline",
"claude-prediction-online",
'prediction-request-rag',
# "prediction-with-research-conservative",
# "prediction-with-research-bold",
"prediction-request-reasoning-claude",
"prediction-request-rag-claude",
"prediction-url-cot-claude",
], label="Tool Name", info="Choose the tool to run")
model_name = gr.Dropdown([
"gpt-3.5-turbo-0125",
"gpt-4-0125-preview"
"claude-3-haiku-20240307",
"claude-3-sonnet-20240229",
"claude-3-opus-20240229",
], label="Model Name", info="Choose the model to use")
with gr.Row():
openai_api_key = gr.Textbox(label="OpenAI API Key", placeholder="Enter your OpenAI API key here", type="password")
anthropic_api_key = gr.Textbox(label="Anthropic API Key", placeholder="Enter your Anthropic API key here", type="password")
with gr.Row():
run_button = gr.Button("Run Benchmark")
with gr.Row():
with gr.Accordion("Results", open=True):
result = gr.Dataframe()
with gr.Row():
with gr.Accordion("Summary", open=False):
summary = gr.Dataframe()
run_button.click(run_benchmark_gradio,
inputs=[tool_name, model_name, openai_api_key, anthropic_api_key],
outputs=[result, summary])
demo.queue(default_concurrency_limit=40).launch(server_port=7860) |