### 预测实例
#the original source code:
#https://github.com/AzeemWaqarRao/Streamlit-Iris-Classification-App
import simplestart as ss

from sklearn.datasets import load_iris
import pandas as pd
import pickle
import numpy as np

#data and api
species = ['setosa', 'versicolor', 'virginica']
image = ['./images/setosa.jpg', './images/versicolor.jpg', './images/virginica.jpg']
with open('./data/model.pkl', 'rb') as f:
    model = pickle.load(f)
    
    
def slidechange(event):
    predict()

def predict():
    # Getting Prediction from model
    inp = np.array([sepal_length.value, sepal_width.value, petal_length.value, petal_width.value])
    inp = np.expand_dims(inp,axis=0)
    prediction = model.predict_proba(inp)
    #test
    #prediction = [["aaa", "bbb","cccds sdfdsafd sagdsfasf sdfsdf"]]

    ## Show Results when prediction is done
    if True:
        df = pd.DataFrame(prediction, index = ['result'], columns=species).round(4)
        table_result.data = df
        ss.session["result"] = species[np.argmax(prediction)]
        image_flower.image = image[np.argmax(prediction)]
        
#ui 
with ss.sidebar():
    ss.write("### Inputs")
    
    sepal_length = ss.slider("sepal length (cm)",4.3, 7.9, 5.0, onchange=slidechange)
    
    sepal_width = ss.slider("sepal width (cm)",2.0,4.4,3.6, onchange=slidechange)
    petal_length = ss.slider("petal length (cm)",1.0,6.9,1.4, onchange=slidechange)
    petal_width = ss.slider("petal width (cm)",0.1,2.5,0.2, onchange=slidechange)


ss.write("## 鸢尾花分类预测")
ss.write("改变花萼花瓣的长度宽度，在3种可能的类别中预测")

ss.write('''
# Results
Following is the probability of each class
''')

ss.space()

table_result = ss.table(show_border = True)
ss.write("**This flower belongs to @result" + " class**")

ss.space()

image_flower = ss.image(image[0])
    
predict()