Upload 8 files

Procfile

@@ -0,0 +1 @@
+web: sh setup.sh && streamlit run app.py

app.py

@@ -0,0 +1,132 @@
+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("""
+# Breast Cancer Prediction App
+
+
+Kanker payudara adalah kanker paling umum di kalangan wanita di dunia. Ini menyumbang 25% dari semua kasus kanker, dan mempengaruhi lebih dari 2,1 Juta orang pada tahun 2015 saja. Ini dimulai ketika sel-sel di payudara mulai tumbuh di luar kendali. Sel-sel ini biasanya membentuk tumor yang dapat dilihat melalui sinar-X atau dirasakan sebagai benjolan di area payudara.
+
+Tantangan utama untuk mendeteksinya adalah bagaimana mengklasifikasikan tumor menjadi ganas (kanker) atau jinak (non kanker).
+""")
+
+url_dataset = f'<a href="breast_cancer.csv">Download Dataset CSV File</a>'
+st.markdown(url_dataset, unsafe_allow_html=True)
+
+def user_input_features() :
+    radius_mean = st.sidebar.slider('radius_mean', 6.981,28.11)
+    texture_mean = st.sidebar.slider('texture_mean', 9.71,39.28)
+    perimeter_mean = st.sidebar.slider('perimeter_mean', 43.79,188.5)
+    area_mean = st.sidebar.slider('area_mean', 143.5,2501.0)
+    smoothness_mean = st.sidebar.slider('smoothness_mean', 0.05263,0.1634)
+    compactness_mean = st.sidebar.slider('compactness_mean', 0.01938,0.3454)
+    concavity_mean = st.sidebar.slider('concavity_mean', 0.0,4268.0)
+    concave_points_mean = st.sidebar.slider('concave_points_mean', 0.0,0.2012)
+    symmetry_mean = st.sidebar.slider('symmetry_mean', 0.106,0.304)
+    fractal_dimension_mean = st.sidebar.slider('fractal_dimension_mean', 0.04996,0.09744)
+    radius_se = st.sidebar.slider('radius_se', 0.1115,2.873)
+    texture_se = st.sidebar.slider('texture_se', 0.3602,4.885)
+    perimeter_se = st.sidebar.slider('perimeter_se', 0.757,21.98)
+    area_se = st.sidebar.slider('area_se', 6.802,542.2)
+    smoothness_se = st.sidebar.slider('smoothness_se', 0.001713,0.03113)
+    compactness_se = st.sidebar.slider('compactness_se', 0.002252,0.1354)
+    concavity_se = st.sidebar.slider('concavity_se', 0.0,0.396)
+    concave_points_se = st.sidebar.slider('concave_points_se', 0.0,0.05279)
+    symmetry_se = st.sidebar.slider('symmetry_se', 0.007882,0.07895)
+    fractal_dimension_se = st.sidebar.slider('fractal_dimension_se', 0.000895,0.02984)
+    radius_worst = st.sidebar.slider('radius_worst', 7.93,36.04)
+    texture_worst = st.sidebar.slider('texture_worst', 12.02,49.54)
+    radius_worst = st.sidebar.slider('radius_worst', 0.1115,2.873)
+    perimeter_worst = st.sidebar.slider('perimeter_worst', 50.41,251.2)
+    area_worst = st.sidebar.slider('area_worst', 185.2,4254.0)
+    smoothness_worst = st.sidebar.slider('smoothness_worst', 0.07117,0.2226)
+    compactness_worst = st.sidebar.slider('compactness_worst', 0.02729,1.058)
+    concavity_worst = st.sidebar.slider('concavity_worst', 0.0,1.252)
+    concave_points_worst = st.sidebar.slider('concave_points_worst', 0.0,0.291)
+    symmetry_worst = st.sidebar.slider('symmetry_worst', 0.1565,0.6638)
+    fractal_dimension_worst = st.sidebar.slider('fractal_dimension_worst', 0.1565,0.2075)
+
+    
+    data = {'radius_mean':[radius_mean],
+    'texture_mean':[texture_mean],
+    'radius_mean':[radius_mean],
+    'perimeter_mean':[perimeter_mean],
+    'radius_mean':[radius_mean],
+    'area_mean':[area_mean],
+    'smoothness_mean':[smoothness_mean],
+    'compactness_mean':[compactness_mean],
+    'concavity_mean':[concavity_mean],
+    'concave_points_mean':[concave_points_mean],
+    'symmetry_mean':[symmetry_mean],
+    'fractal_dimension_mean':[fractal_dimension_mean],
+    'radius_se':[radius_se],
+    'texture_se':[texture_se],
+    'perimeter_se':[perimeter_se],
+    'area_se':[area_se],
+    'smoothness_se':[smoothness_se],
+    'compactness_se':[compactness_se],
+    'concavity_se':[concavity_se],
+    'concave_points_se':[concave_points_se],
+    'symmetry_se':[symmetry_se],
+    'fractal_dimension_se':[fractal_dimension_se],
+    'fractal_dimension_se':[fractal_dimension_se],
+    'radius_worst':[radius_worst],
+    'texture_worst':[texture_worst],
+    'perimeter_worst':[perimeter_worst],
+    'area_worst':[area_worst],
+    'smoothness_worst':[smoothness_worst],
+    'compactness_worst':[compactness_worst],
+    'concavity_worst':[concavity_worst],
+    'concave_points_worst':[concave_points_worst],
+    'symmetry_worst':[symmetry_worst],
+    'fractal_dimension_worst':[fractal_dimension_worst]
+    }
+
+    features = pd.DataFrame(data)
+    return features
+
+input_df = user_input_features()
+
+
+breast_raw = pd.read_csv('breast_cancer.csv')
+breast_raw.fillna(0, inplace=True)
+breast = breast_raw.drop(columns=['diagnosis'])
+df = pd.concat([input_df,breast],axis=0)
+
+df = df[:1] # Selects only the first row (the user input data)
+df.fillna(0, inplace=True)
+
+features = ['radius_mean', 'texture_mean', 'perimeter_mean',
+       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',
+       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',
+       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',
+       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',
+       'fractal_dimension_se', 'radius_worst', 'texture_worst',
+       'perimeter_worst', 'area_worst', 'smoothness_worst',
+       'compactness_worst', 'concavity_worst', 'concave points_worst',
+       'symmetry_worst', 'fractal_dimension_worst']
+
+df = df[features]
+
+
+st.subheader('User Input features')
+st.write(df)
+
+load_clf = pickle.load(open('breast_clf.pkl', 'rb'))
+prediction = load_clf.predict(df)
+prediction_proba = load_clf.predict_proba(df)
+breast_labels = np.array(['Benign','Malignant'])
+st.subheader('Detection')
+if(prediction == 'B') :
+    st.write(breast_labels[0])
+else :
+    st.write(breast_labels[1])
+st.subheader('Detection Probability')
+df_proba = pd.DataFrame(data=prediction_proba, index=['Probability'], columns=breast_labels)
+st.write(df_proba)
+

breast_clf.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1850cc1962b012413cce74e5d0ebca84b455eb7beb47d9a39aad7fbb2ef362af
+size 340235

requirements.txt

@@ -0,0 +1,6 @@
+matplotlib==3.5.1
+numpy==1.21.5
+pandas==1.4.2
+seaborn==0.11.2
+streamlit==1.12.2
+sklearn

setup.sh

@@ -0,0 +1,13 @@
+mkdir -p ~/.streamlit/
+
+echo "\
+[general]\n\
+email = \"your-email@domain.com\"\n\
+" > ~/.streamlit/credentials.toml
+
+echo "\
+[server]\n\
+headless = true\n\
+enableCORS=false\n\
+port = $PORT\n\
+" > ~/.streamlit/config.toml