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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 = plt.figure() | |
| # ax = fig.add_subplot() | |
| fig, ax = plt.subplots() | |
| ax.scatter(list(df[x]),list(df[y])) | |
| 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) | |
| 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) | |