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| # import library | |
| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import pickle | |
| import json | |
| # Load Model | |
| with open('model.pkl', 'rb') as file: | |
| model = pickle.load(file) | |
| with open('feature.txt', 'r') as file: | |
| feature = json.load(file) | |
| # Function to run streamlit model predictor | |
| def run(): | |
| # Set Title | |
| st.title("Predict the Price Range of a Mobile Based on its Features") | |
| st.markdown('---') | |
| # Create a Form for Data Inference | |
| st.markdown('## Input Data') | |
| with st.form('my_form'): | |
| battery_power = st.number_input('Battery Power', min_value=500, max_value=8000) | |
| blue = st.selectbox('Has bluetooth or not? 0 = No, Yes = 1', (0,1)) | |
| clock_speed = st.number_input('Clock Speed (Speed at Wich Microprocessor Execute Instruction)', min_value=0.5, max_value=3.0) | |
| dual_sim = st.selectbox('Has dual sim or not? 0 = No, Yes = 1', (0,1)) | |
| fc = st.number_input('Front Camera mega pixels', min_value=0, max_value=40) | |
| four_g = st.selectbox('Has 4G or not? 0 = No, Yes = 1', (0,1)) | |
| int_memory = st.number_input('Internal Memory in Gigabytes', min_value=2, max_value=256) | |
| m_dep = st.number_input('Mobile Depth in cm', min_value=0.1, max_value=1.0) | |
| mobile_wt = st.number_input('Weight of Mobilephone', min_value=80, max_value=300) | |
| n_cores = st.number_input('Number of Cores of Processor', min_value=1, max_value=10) | |
| pc = st.number_input('Primary Camera mega pixels', min_value=0, max_value=20) | |
| px_height = st.number_input('Pixel Resolution Height', min_value=0, max_value=2000) | |
| px_width = st.number_input('Pixel Resolution Width', min_value=500, max_value=2000) | |
| ram = st.number_input('RAM in Megabytes', min_value=256, max_value=4000) | |
| sc_h = st.number_input('Screen Height of Mobile in cm', min_value=5, max_value=20) | |
| sc_w = st.number_input('Screen Width of Mobile in cm', min_value=1, max_value=15) | |
| talk_time = st.number_input('The Longest Time for one battery charge when you use it', min_value=2, max_value=168) | |
| three_g = st.selectbox('Has 3G or not? 0 = No, Yes = 1', (0,1)) | |
| touch_screen = st.selectbox('Has touch_screen or not? 0 = No, Yes = 1', (0,1)) | |
| wifi = st.selectbox('Has wifi or not? 0 = No, Yes = 1', (0,1)) | |
| # Create a button to make predictions | |
| submitted = st.form_submit_button("Predict") | |
| # Dataframe | |
| data = {'battery_power': battery_power, | |
| 'blue': blue, | |
| 'clock_speed': clock_speed, | |
| 'dual_sim': dual_sim, | |
| 'fc': fc, | |
| 'four_g': four_g, | |
| 'int_memory': int_memory, | |
| 'm_dep': m_dep, | |
| 'mobile_wt': mobile_wt, | |
| 'n_cores': n_cores, | |
| 'pc': pc, | |
| 'px_height': px_height, | |
| 'px_width': px_width, | |
| 'ram': ram, | |
| 'sc_h': sc_h, | |
| 'sc_w': sc_w, | |
| 'talk_time': talk_time, | |
| 'three_g': three_g, | |
| 'touch_screen': touch_screen, | |
| 'wifi': wifi} | |
| df = pd.DataFrame([data]) | |
| st.dataframe(df) | |
| if submitted: | |
| df_selected = df[feature] | |
| y_pred_inf = model.predict(df_selected) | |
| if y_pred_inf[0] == 0: | |
| st.subheader('~ The Mobile Features you Enter is in "Entry-Level" price ~') | |
| elif y_pred_inf[0] == 1: | |
| st.write('~ The Mobile Features you Enter is in "Mid-Range" price ~') | |
| elif y_pred_inf[0] == 2: | |
| st.write('~ The Mobile Features you Enter is in "High-End" price ~') | |
| elif y_pred_inf[0] == 3: | |
| st.write('~ The Mobile Features you Enter is in "Flagship" price ~') | |
| if __name__== '__main__': | |
| run() |