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import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import gradio as gr
dummy_data = [1, 2, 3, 4]
def get_plot(model_name):
plt.plot(dummy_data)
plt.legend(model_name)
return plt.gcf()
demo = gr.Blocks()
with demo:
with gr.Tabs():
with gr.TabItem("Greedy Search"):
model_selector = gr.Dropdown(
choices=["DistilGPT2", "GPT2", "OPT 1.3B", "GPTJ-6B", "T5 Small", "T5 Base", "T5 Large", "T5 3B"],
value="T5 Small",
label="Model",
interactive=True,
)
model_selector.change(fn=get_plot, inputs=model_selector, outputs="plot")
with gr.TabItem("Sample"):
gr.Button("New Tiger")
with gr.TabItem("Beam Search"):
gr.Button("New Tiger")
with gr.TabItem("Benchmark Information"):
gr.Dataframe(
headers=["Parameter", "Value"],
value=[
["Transformers Version", "4.22.dev0"],
["TensorFlow Version", "2.9.1"],
["Pytorch Version", "1.11.0"],
["OS", "22.04 LTS (3090) / Debian 10 (other GPUs)"],
["CUDA", "11.6 (3090) / 11.3 (others GPUs)"],
["Number of runs", "100 (the first run was discarded to ignore compilation time)"],
["Is there code to reproduce?", "Yes -- https://gist.github.com/gante/f0017e3f13ac11b0c02e4e4db351f52f"],
],
)
demo.launch()
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