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import streamlit as st
import pandas as pd
import requests
from datetime import datetime
# Set the title of the Streamlit app
st.title("Super Kart Sales Prediction")
# Section for online prediction
st.subheader("Online Prediction")
# Product Weight
Product_Weight = st.number_input("Product Weight", min_value=1.0, max_value=100.0, value=10.0, step=0.01)
# Product Sugar Content
Product_Sugar_Content = st.selectbox("Product Sugar Content", options=["Low Sugar", "Regular", "No Sugar"])
# Product Allocated Area
Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.000, max_value=1.00, value=0.05, step=0.001)
# Product Type
Product_Type = st.selectbox( "Product Type", options=["Frozen Foods", "Dairy", "Canned", "Baking Goods", "Health and Hygiene", "Breads", "Fruits and Vegetables", "Meat", "Seafood", "Soft Drinks", "Hard Drinks", "Breakfast", "Starchyfoods"])
# Product MRP
Product_MRP = st.number_input("Product MRP", min_value=0.0, max_value=1000.0, value=100.0, step=1.0)
# Store Establishment Year
Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, value=2010, step=1)
# Store Size
Store_Size = st.selectbox("Store Size", options=["Small", "Medium", "High"])
# Store Location City Type
Store_Location_City_Type = st.selectbox("Store Location City Type", options=["Tier 1", "Tier 2", "Tier 3"])
# Store Type
Store_Type = st.selectbox("Store Type",options=["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"])
#Store Id
Store_id = st.selectbox("Store Id",options=["OUT001", "OUT002", "OUT003", "OUT004"])
#calculation of store age
Current_Year = datetime.now().year
Store_Age = Current_Year - Store_Establishment_Year
# Convert user input into a DataFrame
input_data = pd.DataFrame([{
'Product_Weight': Product_Weight,
'Product_Sugar_Content': Product_Sugar_Content,
'Product_Allocated_Area': Product_Allocated_Area,
'Product_Type': Product_Type,
'Product_MRP': Product_MRP,
'Store_Age': Store_Age,
'Store_Size': Store_Size,
'Store_Location_City_Type': Store_Location_City_Type,
'Store_Type': Store_Type,
"Store_id": Store_id
}])
# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
response = requests.post("https://cheeka84-SalesPredictionBackend.hf.space/v1/sales", json=input_data.to_dict(orient='records')[0]) # Send data to Flask API
if response.status_code == 200:
prediction = response.json()['Predicted Sales (in dollars)']
st.success(f"Predicted Sales (in dollars): {prediction}")
else:
st.error(response.status_code)
# Section for batch prediction
st.subheader("Batch Prediction")
# Allow users to upload a CSV file for batch prediction
uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])
# Make batch prediction when the "Predict Batch" button is clicked
if uploaded_file is not None:
if st.button("Predict Batch"):
response = requests.post("https://cheeka84-SalesPredictionBackend.hf.space/v1/salesbatch", files={"file": uploaded_file}) # Send file to Flask API
if response.status_code == 200:
predictions = response.json()
st.success("Batch predictions completed!")
st.write(predictions) # Display the predictions
else:
st.error(response.status_code)
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