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import gradio as gr
import requests
import pandas as pd
from huggingface_hub.hf_api import SpaceInfo
import matplotlib.pyplot as plt
import plotly.express as px
model_perf_table = "data/test.csv"
logo_path = "img/image.png"
def get_blocks_party_spaces():
df = pd.read_csv(model_perf_table)
df = df.sort_values(by=['score'],ascending=False)
return df
def get_blocks_party_spaces_with_formula(formula=None):
# get the dataframe
df = get_blocks_party_spaces()
if formula:
try:
df[str(formula)] = df.eval(formula)
except:
pass # Handle this error properly in your code
return df
def create_scatter(x, y, z):
df = get_blocks_party_spaces()
if z is None or z == 'None' or z == '':
fig, ax = plt.subplots()
ax.scatter(list(df[x]),list(df[y]), marker='o', s=50, c='blue')
for i, label in enumerate(list(df['model'])):
ax.text(list(df[x])[i],list(df[y])[i],str(label))
ax.set_xlabel(x)
ax.set_ylabel(y)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.grid(axis='y', linestyle='--', alpha=0.7)
else:
fig = px.scatter_3d(df, x=x, y=y, z=z, text=df['model'])
# Set axis labels and title
fig.update_layout(scene=dict(
xaxis_title=x,
yaxis_title=y,
zaxis_title=z,
),
title='3D Scatter Plot'
)
return fig
block = gr.Blocks()
with block:
# gr.outputs.HTML(f'<img src="{logo_path}" alt="logo" height="1000px">')
# img = gr.Image(logo_path,shape=[1,2]).style( rounded=False)
gr.Markdown(f"""
# 🦙💦SpitFight - Leaderboard for LLM
""")
with gr.Tabs():
with gr.TabItem("Leaderboard"):
with gr.Row():
data = gr.outputs.Dataframe(type="pandas")
with gr.Row():
formula_input = gr.inputs.Textbox(lines=1, label="User Designed Column", placeholder = 'e.g. verbosity/latency')
data_run = gr.Button("Add To Table")
data_run.click(get_blocks_party_spaces_with_formula, inputs=formula_input, outputs=data)
# running the function on page load in addition to when the button is clicked
with gr.Row():
with gr.Column():
scatter_input = [gr.inputs.Dropdown(choices=get_blocks_party_spaces().columns.tolist()[1:], label="X-axis"),
gr.inputs.Dropdown(choices=get_blocks_party_spaces().columns.tolist()[1:], label="Y-axis"),
gr.inputs.Dropdown(choices=[None]+get_blocks_party_spaces().columns.tolist()[1:], label="Z-axis (Optional)")]
fig_run = gr.Button("Generate Figure")
with gr.Column():
gen_figure = gr.Plot()# gr.outputs.Image(type="pil")
fig_run.click(create_scatter, inputs=scatter_input, outputs=gen_figure)
with gr.TabItem("About"):
gr.Markdown(f"""
## Metrics:
- **Human Score**: The average score given by human evaluators.
- **Throughput**: The number of tokens generated per second.
- **Verbosity**: The average number of generated tokens in the model's response.
- **Latency**: The average time it takes for the model to generate a response.
- **Memory**: The base memory usage of the model.
""")
block.load(get_blocks_party_spaces_with_formula, inputs=None, outputs=data)
block.launch(share=True)
# block.launch( )
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