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### 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()
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