# 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()