DataAIDemo / pages /reg.py
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import streamlit as st
from streamlit import session_state
import joblib
from io import StringIO
import json
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def find_optimal_price(data, model, buying_price):
start_price = data.PRICE.min() - 1 # start price
end_price = data.PRICE.min() + 10 # end price
test = pd.DataFrame(columns = ["PRICE", "QUANTITY"]) # choose required columns
test['PRICE'] = np.arange(start_price, end_price,0.01)
test['QUANTITY'] = model.predict(test['PRICE']) # make predictions
test['PROFIT'] = (test["PRICE"] - buying_price) * test["QUANTITY"]
plt.plot(test['PRICE'],test['QUANTITY']) # plot the results
plt.plot(test['PRICE'],test['PROFIT'])
plt.show()
ind = np.where(test['PROFIT'] == test['PROFIT'].max())[0][0]
values_at_max_profit = test.iloc[[ind]]
return values_at_max_profit
model = joblib.load("burger_model.sav")
uploaded_file = st.file_uploader("Choose a file")
if uploaded_file:
# Read data from file
df = pd.read_csv(uploaded_file)
# Clean data
df = df[df['PRICE'].notna()].reset_index(drop=True)
buying_price = st.slider("Select buying price", min_value=9, max_value=15, value=1, step=1)
result = find_optimal_price(df,model,buying_price)
st.text_area("PRICE Should be to achive maximum profit", value=list(result.to_dict()['PRICE'].values())[0])