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import streamlit as st |
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import pandas as pd |
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from ortools.linear_solver import pywraplp |
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import random |
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import numpy as np |
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import base64 |
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from base64 import b64encode |
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from io import BytesIO |
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def knapsack_solver(df, max_price, max_weight, min_rating): |
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solver = pywraplp.Solver.CreateSolver('SCIP') |
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x = {} |
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for i in range(len(df)): |
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x[i] = solver.BoolVar('x[%i]' % i) |
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objective = solver.Objective() |
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for i in range(len(df)): |
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objective.SetCoefficient(x[i], df['Rating'].iloc[i]) |
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objective.SetMaximization() |
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price_constraint = solver.Constraint(0, max_price) |
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weight_constraint = solver.Constraint(0, max_weight) |
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rating_constraint = solver.Constraint(min_rating, solver.infinity()) |
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for i in range(len(df)): |
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price_constraint.SetCoefficient(x[i], df['Price'].iloc[i]) |
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weight_constraint.SetCoefficient(x[i], df['Weight'].iloc[i]) |
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rating_constraint.SetCoefficient(x[i], df['Rating'].iloc[i]) |
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categories = df['Product_Category'].unique() |
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for category in categories: |
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category_df = df[df['Product_Category'] == category] |
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category_constraint = solver.Constraint(1, solver.infinity()) |
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for i in range(len(category_df)): |
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category_constraint.SetCoefficient(x[df.index.get_loc(category_df.index[i])], 1) |
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solver.Solve() |
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selected_products = pd.DataFrame(columns=df.columns) |
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for i in range(len(df)): |
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if x[i].solution_value(): |
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selected_products = pd.concat([selected_products, df.iloc[[i]]], ignore_index=True) |
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return selected_products |
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df = pd.read_excel('OutputAspoo.xlsx') |
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dataset = df[['kota_nama','nama_barang','harga_umum','name','kategori']] |
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num_records = len(dataset) |
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data = { |
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'Rating': np.random.uniform(low=3.0, high=5.0, size=num_records).round(2), |
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'Weight': np.random.uniform(low=0.1, high=2.0, size=num_records).round(2), |
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} |
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data_rating = pd.DataFrame(data) |
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dataset_final = dataset.join(data_rating) |
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dataset_final.rename(columns={'kategori':'Product_Category','nama_barang': 'Item', 'harga_umum': 'Price'}, inplace=True) |
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dataset_final = dataset_final.dropna() |
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dataset_final['Price'] = dataset_final['Price'].astype(int).astype(float) |
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dataset_final['Price'] = dataset_final['Price'].div(1000) |
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st.title('Aplikasi Rekomendasi Parcell ASPOO') |
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selected_city = st.selectbox('Pilih Nama Kota', df['kota_nama'].unique()) |
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max_price = st.number_input('Batasan Harga Maksimal (... ribu)', min_value=0, value=200) |
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max_weight = st.number_input('Batasan Berat Maksimal (... kg)', min_value=0, value=10) |
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min_rating = st.number_input('Minimal Rating', min_value=0, max_value=5, value=4) |
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if st.button('Proses'): |
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df_kota = dataset_final.loc[dataset_final['kota_nama']==selected_city] |
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df_kota_final = df_kota[['name','Item','Price','Product_Category','Rating','Weight']] |
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result_df = knapsack_solver(df_kota_final, max_price, max_weight, min_rating) |
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st.markdown('### Hasil Rekomendasi Parcell ASPOO') |
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renamed_columns = { |
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'name': 'Nama Produk', |
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'Item': 'Barang', |
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'Price': 'Harga', |
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'Product_Category': 'Kategori Produk', |
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'Rating': 'Rating', |
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'Weight': 'Berat' |
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} |
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result_df.rename(columns=renamed_columns, inplace=True) |
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st.write(result_df) |
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st.markdown('### Unduh Hasil Rekomendasi Parcell ASPOO') |
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result_df.reset_index().drop("index", axis = 1, inplace = True) |
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csv = result_df |
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csv_download = csv.to_csv(index=False) |
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href = f'<a href="data:file/csv;base64,{b64encode(csv_download.encode()).decode()}" download="hasil_parcell_ASPOO2023.csv">Unduh File</a>' |
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st.markdown(href, unsafe_allow_html=True) |
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