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sucess_strength.py
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sucess_strength.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Mar 2 21:17:04 2023
@author: krishna
"""
import numpy as np
import pickle
import streamlit as st
from PIL import Image
import numpy.ma as ma
from io import BytesIO
import requests
linkn = 'https://github.com/anandaiml19/End-to-End-Machine-Learning-Project-with-Deployment-using-Streamlit-FastAPI-and-AWS-EC2/blob/main/strengthhh_concrete.sav?raw=true'
nfile = BytesIO(requests.get(linkn).content)
# load the model
load_strength = pickle.load(nfile)
# create a prediction function
def strength_concrete(concc):
# convert to numpy array
output_numpyy = np.asarray(concc)
# reshape the data
reshape_strength = output_numpyy.reshape(1,-1)
# model predction
output_strength = load_strength.predict(reshape_strength)
masked_removed = ma.masked_array( output_strength, mask = [False])
strength_float = masked_removed.__float__()
return "The strength of concrete is:", round(strength_float,2)
def main():
#image input
col1,col2,col3 = st.columns(3)
with col1:
st.write(' ')
with col2:
image1 = Image.open('D:/EDA_LR/Capstone_Project_Cement/concrete.jpg')
image1 = image1.resize((220,160))
st.image(image1,use_column_width=False)
with col3:
st.write(' ')
# title of app
st.title("Concrete Strength Predction Web App")
# get input data from user
cement = st.number_input('Cement Component Value')
slag = st.number_input('Blast Furnance Slag Value')
ash = st.number_input('Fly Ash Value')
water = st.number_input('Water Mixed Value')
superplastic = st.number_input('Super Plasticizer Mixed Value')
coarseagg = st.number_input('Coarse Aggregate Mixed Value')
fineagg = st.number_input('Fine Aggregate Mixed Value')
age = st.number_input('No of Days Dried')
# plain final result variable
strengthc = ''
if st.button('Predict Concrete Strength'):
strengthc = strength_concrete([cement, slag, ash, water, superplastic, coarseagg, fineagg, age])
st.success(strengthc)
if __name__ == '__main__':
main()