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e65f2af e2adc86 e65f2af e2adc86 e65f2af e2adc86 e65f2af cabed4c e65f2af | 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 requests
# Set the title of the Streamlit app
st.title("Superkart Sales Forecasting")
# Section for online prediction
st.subheader("Sales Forecast")
# Define the input fields
product_weight = st.number_input("Product Weight", min_value=0.0)
sugar_content = st.selectbox("Sugar Content", ["Low Sugar", "Regular", "No Sugar", "reg"])
allocated_area = st.number_input("Allocated Area", min_value=0.0)
product_type = st.selectbox("Product Type", sorted([
"Frozen Foods", "Dairy", "Canned", "Baking Goods", "Health and Hygiene", "Snack Foods",
"Meat", "Household", "Fruits and Vegetables", "Breads", "Hard Drinks", "Soft Drinks",
"Breakfast", "Starchy Foods", "Seafood", "Others"
]))
product_mrp = st.number_input("Product MRP", min_value=0.0)
store_id = st.text_input("Store ID")
store_year = st.number_input("Store Establishment Year", min_value=1900, max_value=2100)
store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
city_type = st.selectbox("City Type", ["Tier 1", "Tier 2", "Tier 3"])
store_type = st.selectbox("Store Type", [
"Supermarket Type1", "Supermarket Type2", "Departmental Store", "Food Mart"
])
# Collect all inputs into a DataFrame
input_dict = {
"Product_Weight": product_weight,
"Product_Sugar_Content": sugar_content,
"Product_Allocated_Area": allocated_area,
"Product_Type": product_type,
"Product_MRP": product_mrp,
"Store_Id": store_id,
"Store_Establishment_Year": store_year,
"Store_Size": store_size,
"Store_Location_City_Type": city_type,
"Store_Type": store_type
}
print("Input dict______", input_dict)
input_df = pd.DataFrame([input_dict])
# Make prediction when the "Predict" button is clicked
if st.button("Predict Sales"):
response = requests.post("https://dutta2arnab-SuperKartSalesPredictionBackend.hf.space/v1/sales_forecast", json=input_df.to_dict(orient='records')[0]) # Send data to Flask API
if response.status_code == 200:
prediction = response.json()['Predicted Price (in dollars)']
st.success(f"Predicted Sales price (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 Sales Batch"):
response = requests.post("https://dutta2arnab-SuperKartSalesPredictionBackend.hf.space/v1/sales_forecast_batch", 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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