Spaces:
Sleeping
Sleeping
File size: 4,732 Bytes
9922101 0fb1b94 70d1f02 0fb1b94 70d1f02 0fb1b94 70d1f02 0fb1b94 70d1f02 0fb1b94 70d1f02 0fb1b94 70d1f02 0fb1b94 70d1f02 0fb1b94 70d1f02 0fb1b94 70d1f02 0fb1b94 70d1f02 0fb1b94 | 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 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | import streamlit as st
import pandas as pd
import requests
import time
# Set the title of the Streamlit app
st.set_page_config(page_title="SuperKart Sales Forecast", layout="centered")
st.title("SuperKart Sales Prediction")
# Health check endpoint for Hugging Face
if st.query_params.get("healthcheck") == "true":
st.write("OK")
st.stop()
# Section for single prediction
st.subheader("Single Product-Store Prediction")
# Collect user input for product and store features
product_weight = st.number_input("Product Weight", min_value=0.1, max_value=20.0, value=12.65, step=0.1)
product_allocated_area = st.number_input("Product Allocated Area Ratio", min_value=0.0, max_value=1.0, value=0.07, step=0.01)
product_mrp = st.number_input("Product MRP", min_value=10, max_value=200, value=147, step=1)
product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "No Sugar"])
product_type = st.selectbox("Product Type", [
"Fruits and Vegetables", "Household", "Snack Foods", "Meat",
"Hard Drinks", "Dairy", "Canned", "Soft Drinks",
"Health and Hygiene", "Baking Goods", "Bread", "Breakfast",
"Frozen Foods", "Seafood", "Starchy Foods", "Others"
])
store_size = st.selectbox("Store Size", ["High", "Medium", "Low"])
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 Type 1", "Supermarket Type 2", "Food Mart"])
store_age = st.number_input("Store Age (years)", min_value=1, max_value=50, value=10, step=1)
product_category_code = st.number_input("Product Category Code", min_value=1, max_value=100, value=10, step=1)
# Convert user input into the format expected by the API
input_data = {
'Product_Weight': float(product_weight),
'Product_Allocated_Area': float(product_allocated_area),
'Product_MRP': int(product_mrp),
'Product_Sugar_Content': product_sugar_content,
'Product_Type': product_type,
'Store_Size': store_size,
'Store_Location_City_Type': store_location_city_type,
'Store_Type': store_type,
'Store_Age': int(store_age),
'Product_Category_Code': int(product_category_code)
}
# Make prediction when the "Predict" button is clicked
if st.button("Predict Sales"):
try:
# Replace with your actual backend URL
backend_url = "https://simnid-superkartsalesbackend.hf.space/v1/predict"
response = requests.post(backend_url, json=input_data)
if response.status_code == 200:
prediction = response.json()['Predicted Sales Total']
st.success(f"Predicted Sales Total: ${prediction:,.2f}")
else:
st.error(f"Error making prediction. Status code: {response.status_code}")
st.write(response.text)
except Exception as e:
st.error(f"Error connecting to API: {e}")
# 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"):
try:
# Replace with your actual backend batch URL
batch_backend_url = "https://simnid-superkartsalesbackend.hf.space/v1/predictbatch"
response = requests.post(batch_backend_url, files={"file": uploaded_file})
if response.status_code == 200:
predictions = response.json()
st.success("Batch predictions completed!")
# Display predictions in a nice format
predictions_df = pd.DataFrame.from_dict(predictions, orient='index', columns=['Predicted Sales'])
st.dataframe(predictions_df)
# Add download button
csv = predictions_df.to_csv(index=True)
st.download_button(
label="Download Predictions as CSV",
data=csv,
file_name="superkart_predictions.csv",
mime="text/csv"
)
else:
st.error(f"Error making batch prediction. Status code: {response.status_code}")
except Exception as e:
st.error(f"Error connecting to API: {e}")
# Add some information about the app
st.sidebar.header("About")
st.sidebar.info("""
This app predicts sales totals for SuperKart products using a machine learning model.
- **Single Prediction**: Enter details for one product-store combination
- **Batch Prediction**: Upload a CSV file with multiple records
""")
|