app.py

import streamlit as st
import pandas as pd
#import requests
#import json
import pickle

with open(r"C:\Users\lenovo\PycharmProjects\MLOPS\Backend\model.pkl", "rb") as f:
 model = pickle.load(f)

st.write(""" # Mobile Price-Range Prediction""")
st.sidebar.header("Choose Phone Specs")

def input_features():
 battery_power = st.sidebar.slider("battery power",500,2000,1000)
 pix_height = st.sidebar.slider("pix height",0,2000,1000)
 pix_width = st.sidebar.slider("pix width",0,2000,1000)
 ram = st.sidebar.slider("ram",400,4000,2000)
 data = {"battery_power":battery_power,
 "px_height":pix_height,
 "px_width":pix_width,
 "ram":ram}
 feats = pd.DataFrame(data,index=[0])
 return feats, data

feats, data = input_features()

st.subheader("Phone Specs")
st.write(feats)

if st.button("Result"):
 #res = requests.post(url = "http://0.0.0.0:8008/predict", data=json.dumps(data)).text
 res = model.predict(feats)
 st.subheader(f"Predicted Price Range: {res}")