streamlit.py

import numpy as np
import streamlit as st
import pickle
import pandas as pd


from PIL import Image


pickle_in=open("iris_model.pkl","rb")
classifier=pickle.load(pickle_in)

def welcome():
    return "Hello Guyz"

def Nikesh(SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm):
    species_mapping = {0: "Setosa", 1: "Versicolor", 2: "Virginica"}  # Add your species mapping here
    prediction=classifier.predict([[SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm]])
    species_name = species_mapping.get(prediction[0], "Unknown")  # Map the prediction to species name
    print(f"Prediction: {prediction[0]}, Species: {species_name}")
    return species_name


def main():
    st.title("iris flower prediction")
    html_temp="""
    <div style="backgrouund-color:tomato;padding:10px>"""
    st.markdown(html_temp,unsafe_allow_html=True)
    SepalLengthCm = st.text_input("SepalLengthCm","type Here")
    SepalWidthCm = st.text_input("SepalWidthCm","type Here")
    PetalLengthCm = st.text_input("PetalLengthCm","type here")
    PetalWidthCm = st.text_input("PetalWidthCm","type here")
    result = ""
    if st.button("Predict"):
        result=Nikesh(float(SepalLengthCm), float(SepalWidthCm), float(PetalLengthCm),
                      float(PetalWidthCm ))
    st.success('The output is {}'.format(result))
    if st.button("About"):
        st.text("let's learn")
        st.text("Built by Nikesh")
if __name__=='__main__':
    main()