File size: 1,790 Bytes
28dd0d5 976eb10 28dd0d5 7aef7b8 28dd0d5 7fa6af3 28dd0d5 7aef7b8 28dd0d5 7c04b7a 9a8c083 7fa6af3 9a8c083 28dd0d5 7aef7b8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 |
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import gradio as gr
def sales_projections(employee_data):
sales_data = employee_data.iloc[:, 1:4].astype("int").to_numpy()
regression_values = np.apply_along_axis(
lambda row: np.array(np.poly1d(np.polyfit([0, 1, 2], row, 2))), 0, sales_data
)
projected_months = np.repeat(
np.expand_dims(np.arange(3, 12), 0), len(sales_data), axis=0
)
projected_values = np.array(
[
month * month * regression[0] + month * regression[1] + regression[2]
for month, regression in zip(projected_months, regression_values)
]
)
plt.plot(projected_values.T)
plt.legend(employee_data["Name"])
return employee_data, plt.gcf(), regression_values
demo = gr.Blocks()
with demo:
with gr.Tabs():
with gr.TabItem("Greedy Search"):
gr.Dropdown(["DistilGPT2", "GPT2", "OPT 1.3B", "GPTJ-6B", "T5 small", "T5 base", "T5 large", "T5 3B"])
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)"],
["Is there code to reproduce?", "Yes -- https://gist.github.com/gante/f0017e3f13ac11b0c02e4e4db351f52f"],
],
)
demo.launch()
|