| import streamlit as st | |
| import pandas as pd | |
| import requests | |
| import json | |
| 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 | |
| st.subheader(f"Predicted Price Range: {res}") |