Upload app.py

app.py

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+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)