| # import gradio as gr | |
| # | |
| # def greet(name): | |
| # return "Hello " + name + "!!" | |
| # | |
| # iface = gr.Interface(fn=greet, inputs="text", outputs="text") | |
| # iface.launch() | |
| import gradio as gr | |
| import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) | |
| from Mall_Customer import kmean_demo | |
| df = pd.read_csv("dssv.csv", sep = ";", encoding='utf-8') | |
| # df = pd.read_csv('Mall_Customers.csv') | |
| def search_student(name): | |
| """ | |
| :param name: | |
| :return: | |
| """ | |
| return (pd.DataFrame(df[df["Họ và tên"] == name.strip()])) | |
| # search name service | |
| inputs = [gr.Dataframe(row_count = (2, "dynamic"), col_count=(4,"dynamic"), label="Input Data", interactive=1)] | |
| outputs = [gr.Dataframe(row_count = (2, "dynamic"), col_count=(16, "fixed"),interactive=1, label="Predictions")] | |
| demo = gr.Interface(fn=search_student, inputs='text', outputs=outputs, examples = [[df.head(2)]]) | |
| demo.launch() | |
| # ## Mall customer service v1 | |
| # inputs = [gr.Dataframe(label="Supersoaker Production Data")] | |
| # outputs = [gr.Gallery(label="Profiling Dashboard", columns=[1], rows=[3], height="auto"), "text"] | |
| # demo = gr.Interface(kmean_demo, inputs=inputs, outputs=outputs, examples=[df.head(100)], | |
| # title="Supersoaker Failures Analysis Dashboard").launch() |