Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from ortools.linear_solver import pywraplp
+import random
+import numpy as np
+import base64 
+from base64 import b64encode
+from io import BytesIO 
+
+# Fungsi untuk menyelesaikan Knapsack Problem
+def knapsack_solver(df, max_price, max_weight, min_rating):
+    solver = pywraplp.Solver.CreateSolver('SCIP')
+
+    # Menambahkan variabel biner untuk setiap produk
+    x = {}
+    for i in range(len(df)):
+        x[i] = solver.BoolVar('x[%i]' % i)
+
+    # Menetapkan fungsi tujuan (maksimalkan total rating)
+    objective = solver.Objective()
+    for i in range(len(df)):
+        objective.SetCoefficient(x[i], df['Rating'].iloc[i])
+    objective.SetMaximization()
+
+    # Menetapkan batasan harga, berat, dan minimal rating
+    price_constraint = solver.Constraint(0, max_price)
+    weight_constraint = solver.Constraint(0, max_weight)
+    rating_constraint = solver.Constraint(min_rating, solver.infinity())
+
+    for i in range(len(df)):
+        price_constraint.SetCoefficient(x[i], df['Price'].iloc[i])
+        weight_constraint.SetCoefficient(x[i], df['Weight'].iloc[i])
+        rating_constraint.SetCoefficient(x[i], df['Rating'].iloc[i])
+
+    # Menetapkan batasan minimal satu produk dipilih untuk setiap kategori
+    categories = df['Product_Category'].unique()
+    for category in categories:
+      category_df = df[df['Product_Category'] == category]
+      category_constraint = solver.Constraint(1, solver.infinity())  # Minimal satu produk yang dipilih
+      for i in range(len(category_df)):
+        category_constraint.SetCoefficient(x[df.index.get_loc(category_df.index[i])], 1)
+
+    # Menyelesaikan masalah
+    solver.Solve()
+
+    # Menyimpan hasil ke dalam DataFrame baru
+    selected_products = pd.DataFrame(columns=df.columns)
+    for i in range(len(df)):
+        if x[i].solution_value():
+            selected_products = pd.concat([selected_products, df.iloc[[i]]], ignore_index=True)
+
+    return selected_products
+
+# Load data
+df = pd.read_excel('OutputAspoo.xlsx')
+dataset = df[['kota_nama','nama_barang','harga_umum','name','kategori']]
+num_records = len(dataset)
+data = {
+    'Rating': np.random.uniform(low=3.0, high=5.0, size=num_records).round(2),
+    'Weight': np.random.uniform(low=0.1, high=2.0, size=num_records).round(2),
+}
+
+
+#Preprocessing data
+data_rating = pd.DataFrame(data)
+dataset_final = dataset.join(data_rating)
+dataset_final.rename(columns={'kategori':'Product_Category','nama_barang': 'Item', 'harga_umum': 'Price'}, inplace=True)
+dataset_final = dataset_final.dropna()
+dataset_final['Price'] = dataset_final['Price'].astype(int).astype(float)
+dataset_final['Price'] = dataset_final['Price'].div(1000)
+
+
+# Tampilan Streamlit
+st.title('Aplikasi Rekomendasi Parcell ASPOO')
+
+# Input field untuk memasukkan kota
+selected_city = st.selectbox('Pilih Nama Kota', df['kota_nama'].unique())
+
+# Input field untuk batasan harga, berat, dan minimal rating
+max_price = st.number_input('Batasan Harga Maksimal (... ribu)', min_value=0, value=200)
+max_weight = st.number_input('Batasan Berat Maksimal (... kg)', min_value=0, value=10)
+min_rating = st.number_input('Minimal Rating', min_value=0, max_value=5, value=4)
+
+# Tombol untuk memproses
+if st.button('Proses'):
+    # Filter berdasarkan kota yang dimasukkan
+    df_kota = dataset_final.loc[dataset_final['kota_nama']==selected_city]
+    df_kota_final = df_kota[['name','Item','Price','Product_Category','Rating','Weight']]
+
+    # Memanggil fungsi knapsack_solver
+    result_df = knapsack_solver(df_kota_final, max_price, max_weight, min_rating)
+
+    # Menampilkan hasil
+    st.markdown('### Hasil Rekomendasi Parcell ASPOO')
+    renamed_columns = {
+        'name': 'Nama Produk',
+        'Item': 'Barang',
+        'Price': 'Harga',
+        'Product_Category': 'Kategori Produk',
+        'Rating': 'Rating',
+        'Weight': 'Berat'
+    }
+    result_df.rename(columns=renamed_columns, inplace=True)
+    st.write(result_df)
+    
+    # Menyimpan hasil ke dalam file CSV
+    st.markdown('### Unduh Hasil Rekomendasi Parcell ASPOO')
+    result_df.reset_index().drop("index", axis = 1, inplace = True)
+    csv = result_df
+    csv_download = csv.to_csv(index=False)
+    href = f'<a href="data:file/csv;base64,{b64encode(csv_download.encode()).decode()}" download="hasil_parcell_ASPOO2023.csv">Unduh File</a>'
+    st.markdown(href, unsafe_allow_html=True)