Lightning website

import streamlit as st
import pandas as pd
from sklearn import datasets 
from sklearn.linear_model import LogisticRegression 
from sklearn.model_selection import train_test_split
from sklearn import metrics
from PIL import Image
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.metrics import r2_score, explained_variance_score, mean_absolute_error

st.write("""
# Danger of Lightning Strikes
Using machine learning to predict the danger of lightning strikes:
""")

image = Image.open(r'C:\Users\eilee\Downloads\lightningpic.jpg')
st.image(image, caption='https://unsplash.com/s/photos/lightning',use_column_width=True)

st.sidebar.header('User Input Parameters')

def user_input_features():
    latitude = st.sidebar.slider('Latitude', 30.0, 50.0, 40.0)
    longitude = st.sidebar.slider('Longitude', -120.0, -60.0, -80.0)
    prcp = st.sidebar.slider('Precipitation levels in mm', 0.0, 100.0, 0.0)
    avgtemp = st.sidebar.slider('Temperature in Celsius', -50.0, 50.0, 0.0)
    windspeed = st.sidebar.slider('Wind speed in km/h', 0.0, 40.0, 10.0)
    data = {'latitude': latitude,
            'longitude': longitude,
            'prcp': prcp,
            'avgtemp': avgtemp,
            'windspeed': windspeed}
    features = pd.DataFrame(data, index =[0])
    return features

df = user_input_features()

#---------------------------------------------------------------------------#

col_names = ['latitude', 'longitude', 'prcp', 'avgtemp', 'windspeed','label']
pima = pd.read_csv(r"C:\Users\eilee\Downloads\allValues.csv", header=0, names = col_names)

feature_names = ['latitude', 'longitude', 'prcp', 'avgtemp', 'windspeed']


X = pima[feature_names] # Features
y = pima.label # Target variable

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=.25,random_state=None)

# instantiate the model (using the default parameters)
logreg = LogisticRegression(solver='liblinear')


# fit the model with data
logreg.fit(X_train,y_train)

#
y_pred=logreg.predict(X_test)

prediction = logreg.predict(df)
prediction_proba = logreg.predict_proba(df)


cnf_matrix = metrics.confusion_matrix(y_test, y_pred)


#-------------------------------------------------------------------------#

st.subheader('User Input Parameters')
st.write(df)

st.subheader('Confusion Matrix:')
st.write(cnf_matrix)


st.subheader('Prediction Probability:')
st.write(prediction_proba)

st.text('The number under the 0 column is the probability of no lightning strikes')
st.text('The number under the 1 column is the probability of lightning strikes') 

st.subheader('                                                                                                                                                                           ')

if st.button('Extra information'):
    st.write('Created by Eileen Chen')
    st.write('Project A31')
    st.write('Made with streamlit')