Spaces:
Sleeping
Sleeping
File size: 3,255 Bytes
4c4f4ba a23f54b 4c4f4ba a23f54b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
import streamlit as st
import pandas as pd
import numpy as np
import requests
# Streamlit UI for Price Prediction
st.title("SuperKart Sales Predictor")
st.write("This tool predicts the sales based on various store parameters.")
st.subheader("Enter the store details(Single Predication):")
# Collect user input
product_weight = st.number_input("Product Weight (in kg)", min_value=1.0, max_value=30.0)
product_sugar = st.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
product_area = st.slider("Allocated Area (sq m)", min_value=0.0, max_value=1.0, step=0.01)
product_type = st.selectbox("Product Type", ["Fruits and Vegetables", "Snack Foods", "Frozen Foods", "Dairy", "Household","Baking Goods", "Canned", "Health and Hygiene", "Meat", "Breads","Hard Drinks", "Soft Drinks", "Seafood", "Starchy Foods", "Others"])
product_mrp = st.number_input("Product MRP", min_value=10.0, max_value=300.0)
store_year = st.number_input("Store Establishment Year", min_value=1980, max_value=2025)
store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
store_city = st.selectbox("City Type", ["Tier 1", "Tier 2", "Tier 3"])
store_type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Food Mart", "Departmental Store"])
# Prepare input
if st.button("Predict Sales"):
input_df = {
"Product_Weight": product_weight,
"Product_Sugar_Content": product_sugar,
"Product_Allocated_Area": product_area,
"Product_Type": product_type,
"Product_MRP": product_mrp,
"Store_Establishment_Year": 2025 - store_year, # we have modified this to get the store age
"Store_Size": store_size,
"Store_Location_City_Type": store_city,
"Store_Type": store_type
}
response = requests.post("https://harasar-SuperKartBackend.hf.space/v1/customer", json=input_df) # enter user name and space name before running the cell
if response.status_code == 200:
result = response.json()
churn_prediction = result["predicted_sales"] # Extract only the value
st.write(f"Based on the information provided, the sproject sales is likely to {churn_prediction}.")
else:
st.error("Error in API request")
#Batch Prediction
uploaded_file = st.file_uploader("Upload CSV file", type=["csv"])
if st.button("Predict for Batch"):
if uploaded_file is not None:
try:
# Convert uploaded file to a DataFrame
df = pd.read_csv(uploaded_file)
# Convert DataFrame to CSV bytes like your working script
csv_bytes = df.to_csv(index=False).encode('utf-8')
# Send POST request with raw bytes
response = requests.post(
"https://harasar-SuperKartBackend.hf.space/v1/customerbatch",
files={"file": ("SuperKart.csv", csv_bytes, "text/csv")}
)
if response.status_code == 200:
st.success("Batch prediction successful!")
st.write(response.json())
else:
st.error(f"Error {response.status_code}: {response.text}")
except Exception as e:
st.error(f"Upload failed: {e}")
else:
st.warning("Please upload a CSV file first.") |