Pane width
Use a value between 19% and 30%
<div _="@=1853,dis=none"><div _="@=1854,dis=none,[@=1855]"> import numpy as np import pickle import pandas as pd import streamlit as st from PIL import Image pickle_in = open("classifier.pkl","rb") classifier= pickle.load(pickle_in) def welcome(): return "Welcome All" def predict_note_authentication(variance,skewness,curtosis,entropy): """Let's Authenticate the Banks Note This is using docstrings for specifications. --- parameters: - name: variance in: query type: number required: true - name: skewness in: query type: number required: true - name: curtosis in: query type: number required: true - name: entropy in: query type: number required: true responses: 200: description: The output values """ prediction=classifier.predict([[variance,skewness,curtosis,entropy]]) print(prediction) return prediction def main(): st.title("Bank Authenticator") html_temp = """ <div style="background-color:tomato;padding:10px"> <h2 style="color:white;text-align:center;">Streamlit Bank Authenticator ML App </h2> </div> """ st.markdown(html_temp,unsafe_allow_html=True) variance = st.text_input("Variance") skewness = st.text_input("skewness") curtosis = st.text_input("curtosis") entropy = st.text_input("entropy") result="" if st.button("Predict"): result=predict_note_authentication(variance,skewness,curtosis,entropy) st.success('The output is {}'.format(result)) if st.button("About"): st.text("App build for learning purpose.... Build by Collins") st.text("Built with Streamlit all the way") if __name__=='__main__': main()</div></div> import numpy as np import pickle import pandas as pd import streamlit as st from PIL import Image pickle_in = open("classifier.pkl","rb") classifier= pickle.load(pickle_in) def welcome(): return "Welcome All" def predict_note_authentication(variance,skewness,curtosis,entropy): """Let's Authenticate the Banks Note This is using docstrings for specifications. --- parameters: - name: variance in: query type: number required: true - name: skewness in: query type: number required: true - name: curtosis in: query type: number required: true - name: entropy in: query type: number required: true responses: 200: description: The output values """ prediction=classifier.predict([[variance,skewness,curtosis,entropy]]) print(prediction) return prediction def main(): st.title("Bank Authenticator") html_temp = """ <div style="background-color:tomato;padding:10px"> <h2 style="color:white;text-align:center;">Streamlit Bank Authenticator ML App </h2> </div> """ st.markdown(html_temp,unsafe_allow_html=True) variance = st.text_input("Variance") skewness = st.text_input("skewness") curtosis = st.text_input("curtosis") entropy = st.text_input("entropy") result="" if st.button("Predict"): result=predict_note_authentication(variance,skewness,curtosis,entropy) st.success('The output is {}'.format(result)) if st.button("About"): st.text("App build for learning purpose.... Build by Collins") st.text("Built with Streamlit all the way") if __name__=='__main__': main()
