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import streamlit as st
import requests
import pandas as pd
# Title and description
st.set_page_config(page_title="SuperKart Store Sales Forecast", layout="centered")
st.title('📈 SuperKart Store Sales Forecast')
st.write('Enter product and store details to predict the sales total or upload a CSV for batch forecasting.')
# --- Online Prediction ---
st.header('🛒 Product and Store Details (Single Forecast)')
col1, col2 = st.columns(2)
with col1:
product_weight = st.number_input('Product Weight (kg)', min_value=0.0, format="%.2f")
product_mrp = st.number_input('Product MRP (₹)', min_value=0.0, format="%.2f")
product_sugar_content = st.selectbox('Product Sugar Content', ['Low Sugar', 'Regular', 'No Sugar'])
product_allocated_area = st.number_input('Product Allocated Area (0.0 - 1.0)', min_value=0.0, max_value=1.0, format="%.4f")
product_type = st.selectbox('Product Type', [
'Meat', 'Snack Foods', 'Hard Drinks', 'Dairy', 'Canned', 'Soft Drinks',
'Health and Hygiene', 'Baking Goods', 'Bread', 'Breakfast', 'Frozen Foods',
'Fruits and Vegetables', 'Household', 'Seafood', 'Starchy Foods', 'Others'
])
with col2:
store_id = st.selectbox('Store ID', ['OUT001', 'OUT002', 'OUT003', 'OUT004'])
store_establishment_year = st.number_input('Store Establishment Year', min_value=1980, max_value=2025, step=1)
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', ['Departmental Store', 'Supermarket Type1', 'Supermarket Type2', 'Food Mart'])
# Predict Button
if st.button('📊 Predict Sales Forecast'):
input_data = {
'Product_Weight': product_weight,
'Product_MRP': product_mrp,
'Product_Sugar_Content': product_sugar_content,
'Product_Allocated_Area': product_allocated_area,
'Product_Type': product_type,
'Store_Id': store_id,
'Store_Establishment_Year': store_establishment_year,
'Store_Size': store_size,
'Store_Location_City_Type': store_location_city_type,
'Store_Type': store_type
}
api_url = 'https://Yash0204-API-SuperKart-Backend.hf.space/v1/sales'
try:
response = requests.post(api_url, json=input_data)
if response.status_code == 200:
prediction_result = response.json()
predicted_sales = prediction_result.get('predicted_product_store_sales_total')
if predicted_sales is not None:
st.success(f'✅ Predicted Product Store Sales Total: ₹{predicted_sales:.2f}')
else:
st.error('❌ Prediction not found in the response.')
elif response.status_code == 400:
st.error(f'❌ API Error: Invalid input data. Details: {response.json().get("error", "Unknown error")}')
else:
st.error(f'❌ API Error: Status Code {response.status_code}. Details: {response.text}')
except requests.exceptions.RequestException as e:
st.error(f'❌ Connection Error: {e}')
except Exception as e:
st.error(f'❌ Unexpected Error: {e}')
st.markdown("---")
# --- Batch Forecast ---
st.header("📁 Batch Forecast using CSV Upload")
uploaded_file = st.file_uploader("Upload a CSV file containing product/store data", type=["csv"])
if uploaded_file is not None:
if st.button("📥 Predict Batch Sales Forecast"):
api_batch_url = "https://Yash0204-API-SuperKart-Backend.hf.space/v1/salesbatch"
try:
response = requests.post(api_batch_url, files={"file": uploaded_file})
if response.status_code == 200:
result = response.json()
df_result = pd.DataFrame(result)
st.success("✅ Batch predictions completed.")
st.dataframe(df_result)
else:
st.error(f'❌ Batch Prediction Error: {response.status_code} - {response.text}')
except requests.exceptions.RequestException as e:
st.error(f'❌ Connection Error: {e}')
except Exception as e:
st.error(f'❌ Unexpected Error: {e}')
st.info("ℹ️ Please ensure your backend API supports `/v1/sales` and `/v1/salesbatch` endpoints.")
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