import streamlit as st
import pandas as pd 
import seaborn as sns
import numpy as np
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier


iris = datasets.load_iris()

X=iris.data
y=iris.target

clf= RandomForestClassifier()
clf.fit(X,y)


st.title('Iris Flower Prediction App')
st.sidebar.header('User input parameters')

def user_input_features():
    sepal_length = st.sidebar.slider('Sepal length', 4.3,7.9,5.4)
    sepal_width = st.sidebar.slider('Sepal width', 2.0,4.4,3.4)
    petal_length= st.sidebar.slider('Petal length', 1.0,6.9,1.3)
    petal_width = st.sidebar.slider('Petal width', 0.1,2.5,0.2)

    data= {'sepal_length': sepal_length, 
           'sepal_width':sepal_width,
           'petal_length':petal_length,
           'petal_width': petal_width}
    features= pd.DataFrame(data, index=[0])

    return features

df= user_input_features()
st.subheader("User Input Parameters")
st.write(df)

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

st.subheader('CLass namesand correspondung numbers')
st.write(iris.target_names)


st.header('Prediction')
st.write(iris.target_names[prediction])

st.header('Prediction Probability')
st.write(prediction_proba)