Delete kidney_app.py

kidney_app.py

@@ -1,102 +0,0 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-import pickle
-import base64
-import seaborn as sns
-import matplotlib.pyplot as plt
-
-st.write("""
-# Chronic Kidney Disease
-
-#Context
-This dataset is originally from UCI Machine Learning Repository. The objective of the dataset is to diagnostically predict whether a patient is having chronic kidney disease or not, based on certain diagnostic measurements included in the dataset.
-
-#Content
-The datasets consists of several medical predictor variables and one target variable, Class. Predictor variables includes Blood Pressure(Bp), Albumin(Al), etc.
-
-#Inspiration
-Can you build a machine learning model to accurately predict whether or not the patients in the dataset have chronic kidney disease or not?
-""")
-
-url_dataset = f'<a href="new_model.csv">Download Dataset CSV File</a>'
-st.markdown(url_dataset, unsafe_allow_html=True)
-
-def user_input_features() :
-    Bp = st.sidebar.slider('Bp', 50.000, 180.000)
-    Sg = st.sidebar.slider('Sg', 1.005, 1.025)
-    Al = st.sidebar.slider('Al', 0.000, 5.000)
-    Su = st.sidebar.slider('Su', 0.000, 5.000)
-    Rbc = st.sidebar.slider('Rbc', 0.000, 1.000)
-    Bu = st.sidebar.slider('Bu', 1.500, 391.000)
-    Sc = st.sidebar.slider('Sc', 0.400, 76.000)
-    Sod = st.sidebar.slider('Sod', 4.500, 163.000)
-    Pot = st.sidebar.slider('Pot', 2.500, 47.000)
-    Hemo = st.sidebar.slider('Hemo', 3.100, 17.800)
-    Wbcc = st.sidebar.slider('Wbcc', 2200.000, 26400.000)
-    Rbcc = st.sidebar.slider('Rbcc', 2.100, 8.000)
-    Htn = st.sidebar.slider('Htn', 0.000, 1.000)
-    Class = st.sidebar.slider('Class', 0.000, 1.000)
-
-
-
-    data = {'Bp':[Bp], 
-            'Sg':[Sg],
-            'Al':[Al],
-            'Su':[Su],
-            'Rbc':[Rbc],
-            'Bu':[Bu],
-            'Sc':[Sc],
-            'Sod':[Sod],
-            'Pot':[Pot],
-            'Hemo':[Hemo],
-            'Wbcc':[Wbcc],
-            'Rbcc':[Rbcc],
-            'Htn':[Htn],
-            'Class':[Class]
-            }
-
-    features = pd.DataFrame(data)
-    return features
-
-input_df = user_input_features()
-
-
-kidney_raw = pd.read_csv('new_model.csv')
-kidney_raw.fillna(0, inplace=True)
-kidney = kidney_raw.drop(columns=['Class'])
-df = pd.concat([input_df, kidney], axis=0)
-
-df = df[:1] # Selects only the first row (the user input data)
-df.fillna(0, inplace=True)
-
-features = ['Bp', 'Sg', 'Al',
-'Su',
-'Rbc',
-'Bu',
-'Sc',
-'Sod',
-'Pot',
-'Hemo',
-'Wbcc',
-'Rbcc',
-'Htn'
-]
-
-df = df[features]
-
-
-st.subheader('User Input features')
-st.write(df)
-
-load_clf = pickle.load(open('kidney_clf.pkl', 'rb'))
-prediction = load_clf.predict(df)
-prediction_proba = load_clf.predict_proba(df)
-kidney_labels = np.array(['Normal', 'Chronic Kidney'])
-st.subheader('Prediction')
-st.write(kidney_labels[int(prediction)])
-st.subheader('Prediction Probability')
-df_prob = pd.DataFrame(data = prediction_proba, 
-                    index = ['Probability'], 
-                    columns = kidney_labels)
-st.write(df_prob)