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f1c5264 fb8a0ad bfa1029 fb8a0ad | 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 | import streamlit as st
import pandas as pd
import requests
# Set the Streamlit home directory to a writable location (/tmp)
os.environ["STREAMLIT_HOME"] = "/tmp"
# Set the title of the Streamlit app
st.title("SuperKart Sales Forecasting")
# Section for online prediction
st.subheader("Online Prediction")
# Collect user input for Product and Store features
product_weight = st.number_input("Product Weight (in Grams)", min_value=0.0, step=0.1, value=1.0)
product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "No Sugar", "Regular"])
product_allocated_area = st.number_input("Product Allocated Display Area (as proportion)", min_value=0.0, max_value=1.0, step=0.01, value=0.1)
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"]
)
product_mrp = st.number_input("Product MRP (in dollars)", min_value=0.0, step=0.01, value=10.0)
store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
store_location_city_type = st.selectbox("Store Location 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 (in years)", min_value=0, step=1, value=10)
# 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_Size': store_size,
'Store_Location_City_Type': store_location_city_type,
'Store_Type': store_type,
'Store_Age': store_age
}])
# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
response = requests.post("https://donnymv-superkartsalesforecasterbackend.hf.space/v1/sales/predict", json=input_data.to_dict(orient='records')[0]) # Send data to Flask API
if response.status_code == 200:
prediction = response.json()['Predicted Sales Revenue (in dollars)']
st.success(f"Predicted Sales Revenue (in dollars): {prediction}")
else:
st.error("Error making prediction.")
# 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://donnymv-superkartsalesforecasterbackend.hf.space/v1/salesbatch/predict", files={"file": uploaded_file}) # Send file to Flask API
if response.status_code == 200:
predictions = response.json()
st.success("Batch predictions completed!")
st.write(predictions) # Display the predictions
else:
st.error("Error making batch prediction.")
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