iris.py

import streamlit as st
import pandas as pd
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier


st.write("""
         # 🤍🤍A L I__H A S N A I N🤍🤍
         # Iris Flower Prediction App
         
         "**Ths app will predict the **Iris flower** type based on the features.**"
         """)


st.sidebar.header("**YOUR 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("YOUR INPUT DATA")
st.write(df)



iris = datasets.load_iris()
X = iris.data
Y = iris.target


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


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


st.subheader("**Class Label and their corresponding index number**")
st.write(iris.target_names)


st.subheader("**PREDICTION OF MY APP**")
st.write(iris.target_names[prediction])

st.subheader("**PROBABILITY WISE CHANCES**")
st.write(prediction_proba)