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# app.py | |
import streamlit as st | |
import pandas as pd | |
import numpy as np | |
import joblib | |
from datetime import datetime | |
# Assuming the necessary EDA functions are defined in eda.py and imported here | |
from eda import (average_sales_by_region, average_sales_and_profit_over_time, | |
segment_vs_region_distribution, sales_vs_profit_across_segments, | |
category_composition_for_profit_and_sales) | |
from prediction import make_prediction | |
# In your model training script and your Streamlit app script (app.py) | |
from transformers import UnitPriceTransformer, KMeansAndLabelTransformer, DynamicOneHotEncoder | |
# Load the dataset for EDA | |
def load_data(): | |
return pd.read_csv('superstore_clean.csv') | |
df = load_data() | |
# Sidebar for navigation | |
st.sidebar.title("Navigation") | |
selection = st.sidebar.radio("Go to", ["Home", "EDA", "Make a Prediction"]) | |
if selection == "Home": | |
st.title("Welcome to the Superstore Sales Dashboard") | |
elif selection == "EDA": | |
st.title("Exploratory Data Analysis (EDA)") | |
# Display EDA plots directly here or call a function that does | |
average_sales_by_region(df) | |
average_sales_and_profit_over_time(df) | |
segment_vs_region_distribution(df) | |
sales_vs_profit_across_segments(df) | |
category_composition_for_profit_and_sales(df) | |
elif selection == "Make a Prediction": | |
st.title("Make a Sales Prediction") | |
with st.form("input_form"): | |
# Simplify input fields based on what's actually used | |
sales = st.number_input('Sales', value=100.0, format="%.2f") | |
quantity = st.number_input('Quantity', value=2, format="%d") | |
discount = st.number_input('Discount', value=0.0, format="%.2f") | |
sub_category = st.selectbox('Sub-Category', ['Bookcases', 'Chairs', 'Labels', 'Tables', 'Storage', 'Furnishings', 'Art', 'Phones', 'Binders', 'Appliances', 'Paper', 'Accessories', 'Envelopes', 'Fasteners', 'Supplies', 'Machines', 'Copiers']) | |
submitted = st.form_submit_button("Predict") | |
if submitted: | |
input_features = pd.DataFrame([[sales, quantity, discount, sub_category]], columns=['sales', 'quantity', 'discount', 'sub_category']) | |
predicted_profit = make_prediction(input_features) | |
st.write(f'Predicted Profit: {predicted_profit:.2f}') | |