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demand_forecast_app.py
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demand_forecast_app.py
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import streamlit as st
import pickle
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings('ignore')
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import r2_score,mean_squared_error
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
def center_id(datacol):
center_id_val_index_n = []
for i in datacol:
if i >= 10 and i <= 30:
center_id_val_index_n.append("10-30")
elif i >= 31 and i <=50:
center_id_val_index_n.append("31-50")
elif i >= 51 and i <=70:
center_id_val_index_n.append("51-70")
elif i >= 71 and i <=90:
center_id_val_index_n.append("71-90")
elif i >= 91 and i <=110:
center_id_val_index_n.append("91-110")
elif i >= 111 and i <=130:
center_id_val_index_n.append("111-130")
elif i >= 131 and i <=150:
center_id_val_index_n.append("131-150")
else:
center_id_val_index_n.append("151-190")
return center_id_val_index_n
def meal_id(datacol):
meal_id_val_index_n = []
for i in datacol:
if i >= 1000 and i <= 1300:
meal_id_val_index_n.append("1000-1300")
elif i >= 1301 and i <=1600:
meal_id_val_index_n.append("1301-1600")
elif i >= 1601 and i <=1900:
meal_id_val_index_n.append("1601-1900")
elif i >= 1901 and i <=2200:
meal_id_val_index_n.append("1901-2200")
elif i >= 2201 and i <=2500:
meal_id_val_index_n.append("2201-2500")
elif i >= 2501 and i <=2800:
meal_id_val_index_n.append("2501-2800")
else:
meal_id_val_index_n.append("2801-3000")
return meal_id_val_index_n
st.write("""
## Demand Forecasting Example
---
- Link to Slides: https://docs.google.com/presentation/d/1YDt9aR-OKCc5c8nbO74LVWCJ648hDRrs/edit?usp=sharing&ouid=100988340872896613643&rtpof=true&sd=true
=============== Cách sử dụng ===============
BƯỚC 1:
- Upload file .csv input data
- hoặc nhập thủ công giá trị của từng biến đầu vào
BƯỚC 2:
- Nhấp chuột chọn "Predict"
###### Tải file test tại: https://drive.google.com/file/d/1vaHWY9PQApvjz8cgE8HlK8RGRPIb-QdI/view?usp=share_link
---
""")
st.sidebar.header('Input Data')
uploaded_file = st.sidebar.file_uploader("Cách 1: Upload your input CSV file", type=["csv"])
if uploaded_file is not None:
input_df = pd.read_csv(uploaded_file)
else:
def user_input_features():
st.sidebar.write('Cách 2: Manually input your data')
week = st.sidebar.number_input('Week to predict', format='%d', min_value = 146, max_value=155,step = 1)
center_id = st.sidebar.selectbox('center_id',(55, 24, 11, 83, 32, 13, 109, 52, 93, 186, 146, 57, 149,
89, 124, 152, 97, 74, 108, 99, 66, 94, 91, 20, 34, 137,
92, 126, 36, 162, 75, 177, 27, 157, 106, 64, 129, 14, 17,
153, 139, 161, 81, 26, 73, 50, 104, 42, 113, 145, 53, 72,
67, 174, 29, 77, 41, 30, 76, 59, 88, 143, 58, 10, 101,
80, 43, 65, 39, 102, 110, 132, 23, 86, 68, 51, 61))
meal_id = st.sidebar.selectbox('meal_id',(1885, 1993, 2539, 2139, 2631, 1248, 1778, 1062, 2707, 1207, 1230,
2322, 2290, 1727, 1109, 2640, 2306, 2126, 2826, 1754, 1971, 1902,
1311, 1803, 1558, 2581, 1962, 1445, 2444, 2867, 1525, 2704, 2304,
2577, 1878, 1216, 1247, 1770, 1198, 1438, 2494, 1847, 2760, 2492,
1543, 2664, 2569, 2490, 1571, 2956, 2104))
emailer_for_promotion = st.sidebar.selectbox('emailer_for_promotion',(1, 0))
homepage_featured = st.sidebar.selectbox('homepage_featured',(1, 0))
checkout_price = st.sidebar.number_input('Week to predict', format="%.2f", min_value = 2.97, max_value= 866.27)
base_price = st.sidebar.number_input('Week to predict', format="%.2f", min_value = 55.35, max_value= 866.27)
data = {'week' : week,
'checkout_price' : checkout_price,
'base_price' : base_price,
'center_id' : center_id,
'meal_id' : meal_id,
'emailer_for_promotion' : emailer_for_promotion,
'homepage_featured' : homepage_featured
}
features = pd.DataFrame(data, index=[0])
return features
input_df = user_input_features() #input_df
exec_button = st.sidebar.button('Predict')
if exec_button:
center_id_val_index_n = center_id(input_df.center_id)
input_df.center_id = center_id_val_index_n
meal_id_val_index_n = meal_id(input_df.meal_id)
input_df.meal_id = meal_id_val_index_n
f_input = input_df.loc[:,['week','center_id','meal_id','checkout_price','base_price','emailer_for_promotion',
'homepage_featured']]
final_input = pd.get_dummies(f_input)
st.write('Your input:')
st.write(input_df)
RFRmodel = pickle.load(open('finalized_model.pkl', 'rb'))
df_predict = RFRmodel.predict(final_input)
input_df['num_orders'] = df_predict
sample = input_df.loc[:,['id','num_orders']]
st.subheader('Prediction result:')
sample = input_df.loc[:,['id','num_orders']]
st.write(sample)