| 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() | |