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import streamlit as st
import pandas as pd
import requests
# Set the title of the Streamlit app
st.title("Store Sales Prediction")
# Section for online prediction
st.subheader("Online Prediction")
# Collect user input for store and product
Product_Weight = st.number_input("Product_Weight", min_value=0.1, max_value=100.0, value=90.0)
Product_Sugar_Content = st.selectbox("Product_Sugar_Content", ["Low Sugar", "No Sugar", "Regular"])
Product_Allocated_Area = st.number_input("Product_Allocated_Area", min_value=0.001, max_value=1.0, value=0.045, step=0.001)
Product_Type = st.selectbox("Product_Type", ["Fruits and Vegetables","Snack Foods","Frozen Foods","Dairy","Household","Baking Goods","Canned","Health and Hygiene",
"Meat","Soft Drinks","Breads","Hard Drinks","Others","Starchy Foods","Breakfast","Seafood"])
Product_MRP = st.number_input("Product_MRP", min_value=10.0, max_value=500.0, value=150.75)
Store_Establishment_Year = st.number_input("Store_Establishment_Year", min_value=1980, max_value=2025, step=1, value=2009)
Store_Size = st.selectbox("Store_Size", ["High", "Medium", "Small"])
Store_Location_City_Type = st.selectbox("Store_Location_City_Type", ["Tier 1", "Tier 2", "Tier 3"])
Store_Type = st.selectbox("Store_Type", ["Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"])
# 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_Establishment_Year': Store_Establishment_Year,
'Store_Size': Store_Size,
'Store_Location_City_Type': Store_Location_City_Type,
'Store_Type': Store_Type
}])
# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
response = requests.post("https://nishantpathak461-Backend_Stores.hf.space/v1/sales", json=input_data.to_dict(orient='records')[0])
if response.status_code == 200:
prediction = response.json()['predicted_sales']
st.metric(f"Predicted Sales", f"₹{prediction:.2f}")
else:
st.error("Error in API request")
# 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://<jyotisharma/storesalesfrontend>.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("Error making batch prediction.")
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