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# streamlit_app.py
import streamlit as st
import pandas as pd
import requests
# ----------------- CONFIGURATION -----------------
# CRITICAL FIX: Setting the path to /api/predict.
# The backend Flask app listens on '/predict', but the HF Space infrastructure
# requires the /api/ prefix for external routing to the Docker container.
API_URL = "https://aibtus.ut.aiml.hf.space/api/predict"
# Define static categorical options (MUST match preprocessing categories)
CATEGORIES = {
'Product_Sugar_Content': ['no sugar', 'low sugar', 'regular'],
'Store_Size': ['Low', 'Medium', 'High'],
'Store_Location_City_Type': ['Tier 1', 'Tier 2', 'Tier 3'],
'Store_Type': ['Supermarket Type 1', 'Supermarket Type 2', 'Supermarket Type 3', 'Food Mart'],
'Product_Type': ['Snack Foods', 'Dairy', 'Baking Goods', 'Health and Hygiene', 'Frozen Foods', 'Others',
'Soft Drinks', 'Household', 'Meat', 'Fruits and Vegetables', 'Breads', 'Breakfast',
'Hard Drinks', 'Starchy Foods', 'Seafood', 'Canned'],
'Product_Category_Simplified': ['FD', 'DR', 'NC']
}
FEATURE_COLS = [
'Product_Weight', 'Product_Sugar_Content', 'Product_Allocated_Area',
'Product_Type', 'Product_MRP', 'Store_Size',
'Store_Location_City_Type', 'Store_Type', 'Store_Age',
'Product_Category_Simplified'
]
# ----------------- APP INTERFACE -----------------
st.set_page_config(page_title="SuperKart Sales Forecaster", layout="wide")
st.title(" SuperKart Sales Forecasting Tool")
with st.form("sales_prediction_form"):
st.header("Product Attributes")
col1, col2, col3 = st.columns(3)
product_weight = col1.number_input("Product Weight (kg)", min_value=1.0, max_value=20.0, value=10.0, step=0.1)
product_mrp = col2.number_input("Product MRP (₹)", min_value=10.0, max_value=300.0, value=150.0, step=1.0)
product_allocated_area = col3.number_input("Product Allocated Area", min_value=0.0, max_value=1.0, value=0.07, step=0.01)
product_type = col1.selectbox("Product Type", options=CATEGORIES['Product_Type'])
product_sugar_content = col2.selectbox("Product Sugar Content", options=CATEGORIES['Product_Sugar_Content'])
product_category_simplified = col3.selectbox("Product Category (FD/DR/NC)", options=CATEGORIES['Product_Category_Simplified'])
st.header("Store Attributes")
col4, col5, col6 = st.columns(3)
store_type = col4.selectbox("Store Type", options=CATEGORIES['Store_Type'])
store_location_city_type = col5.selectbox("Store Location City Type", options=CATEGORIES['Store_Location_City_Type'])
store_size = col6.selectbox("Store Size", options=CATEGORIES['Store_Size'])
store_age = st.slider("Store Age (Years)", min_value=5, max_value=35, value=10)
submitted = st.form_submit_button("Forecast Sales")
if submitted:
input_data = {
'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_Size': store_size,
'Store_Location_City_Type': store_location_city_type,
'Store_Type': store_type,
'Store_Age': float(store_age),
'Product_Category_Simplified': product_category_simplified
}
payload = [input_data]
try:
st.info(f"Sending request to API at: {API_URL}...")
# FIX: verify=False flag kept to prevent the SSLError from recurring.
response = requests.post(API_URL, json=payload, timeout=15, verify=False)
if response.status_code == 200:
result = response.json()
if result['status'] == 'success':
st.success(f" **Forecast Complete!**")
st.metric(label="Predicted Sales Revenue", value=f"₹{result['predicted_sales_revenue']:,.2f}")
st.balloons()
else:
st.error(f"Prediction API Error: {result.get('error', 'Unknown error')}")
else:
st.error(f"**API Connection Error** (Status {response.status_code}): Check API URL and Docker logs.")
except requests.exceptions.Timeout:
st.error("**Connection Failed:** Request timed out (15s). The Docker API might be slow or unreachable.")
except Exception as e:
st.error(f"An unexpected error occurred: {e}")
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