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#import libraries
import matplotlib.pyplot as plt
import numpy
import sys
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
from itertools import islice
import pickle
import streamlit as st
import csv
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.metrics import confusion_matrix
import sklearn.metrics as metrics
# Read a CSV file and determine X features & y target
df=pd.read_csv("datatest.csv")
X=df.drop("level",axis=1).values
y=df.level.values
#Build a model & get a Scores
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X, y)
k_folds = KFold(n_splits = 5)
scores = cross_val_score(knn, X, y, cv = k_folds)
print("Cross Validation Scores: ", scores)
print("Average CV Score: ", scores.mean())
print("Number of CV Scores used in Average: ", len(scores))
y_true=df
expected=y_true
predict = knn.predict(X)
confusion_matrix(y_true['level'], y_pred=predict)
print(metrics.classification_report(y_true['level'], y_pred=predict))
print(type(X))
print(type(y))
# Title
st.title ("Prioritization for Control and Inspection")
# pickling the model
pickle_out = open("knn.pkl", "wb")
pickle.dump(knn, pickle_out)
pickle_out.close()
# loading in the model to predict(classify) on the data
pickle_in = open('knn.pkl', 'rb')
classifier = pickle.load(pickle_in)
def welcome():
return 'welcome all'
# defining the function which will make the prediction using
# the data which the user inputs
def prediction(ppm, SR, repeat_of_alarm, not_po_ka_yoke, high_price):
ppm=int(ppm)
SR=int(SR)
repeat_of_alarm=int(repeat_of_alarm)
not_po_ka_yoke=int(not_po_ka_yoke)
high_price=int(high_price)
prediction = classifier.predict([[ppm, SR, repeat_of_alarm, not_po_ka_yoke, high_price]])
print(prediction)
return prediction
# this is the main function in which we define our webpage
#dictionary for levels
def main():
idx2label={
0:"A",
1:"B",
2:"C",
3:"D",
4:"E",
5:"F"
}
# giving the webpage a title
st.title("Lets know about Grade of Parts")
html_temp = """
<div style ="background-color:yellow;padding:13px">
<h1 style ="color:black;text-align:center;"> Classifier ML App </h1>
</div>
"""
# this line allows us to display the front end aspects we have
st.markdown(html_temp, unsafe_allow_html = True)
ppm = st.selectbox("PPM", [0,1])
SR = st.selectbox("S/R", [0,1])
repeat_of_alarm = st.selectbox("Repeat of Alarm", [0,1])
not_po_ka_yoke =st.selectbox("Not Pokayoka", [0,1])
high_price= st.selectbox("High Price", [0,1])
level = ([])
# the below line ensures that when the button called 'Predict' is clicked,
# the prediction function defined above is called to make the prediction(classify)
# and store it in the variable result \
if st.button("See a Grade"):
level=prediction(ppm, SR, repeat_of_alarm, not_po_ka_yoke, high_price)
st.success((idx2label[level[0]]))
if __name__=='__main__':
main() |