Create app.py

app.py

@@ -0,0 +1,119 @@
+#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("C:\\Users\\M\\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))
+
+# Visualize the dataframe in the Streamlit app
+st.write("""
+# Grade of Parts
+""")
+st.bar_chart(y)
+
+# Title
+st.title ("Hello Engineer")
+
+# Header
+st.header("Lets know about Quality Status") 
+
+
+# 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("Classification")
+    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()