Spaces:
Runtime error
Runtime error
File size: 2,996 Bytes
20de685 8673c26 20de685 8673c26 20de685 8673c26 20de685 8673c26 20de685 8673c26 20de685 8673c26 20de685 8673c26 20de685 8673c26 20de685 8673c26 20de685 8673c26 20de685 8673c26 20de685 8673c26 20de685 | 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 | import streamlit as st
import pandas as pd
import joblib
# Load the trained regression model
def load_model():
return joblib.load("/content/deployment_files/sales_prediction_model_v1_0.joblib")
model = load_model()
# Set the title of the Streamlit app
st.title("Welcome to SuperKart Sales Forecasting")
# Section for online prediction
st.subheader("Online Sales Prediction")
# Collect user input for store features
# Numeric inputs
Product_Weight = st.number_input("Product Weight (in kg)", value=0.0)
Product_Allocated_Area = st.number_input("Product Allocated Area (sq ft)", value=0.0)
Product_MRP = st.number_input("Product MRP (in ₹)", value=0.0)
Store_Establishment_Year = st.number_input("Store Establishment Year", value=2000, step=1)
# Categorical inputs
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low", "Medium", "High"])
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"])
Store_Size = st.selectbox("Store Size", ["Small", "Medium", "Large"])
Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
# 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]
})
# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
response = requests.post(
"https://Shabn-SuperKart-Sales-Prediction.hf.space/v1/sales", json=input_data.to_dict(orient="records")[0])
if response.status_code == 200:
prediction = response.json()["Predicted_Sales_Total"]
st.success(f"Predicted Sales Total: {prediction}")
else:
st.error("Error making prediction. Please check the input data.")
# 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://Shabn-SuperKart-Sales-Prediction.hf.space/v1/salesbatch", files={'file': uploaded_file})
if response.status_code == 200:
predictions = response.json()
st.success('Batch prediction completed successfully!')
st.write(pd.DataFrame(predictions))
else:
st.error('Error making batch prediction. Please check the file format and try again.')
|