app.py

import streamlit as st
import pandas as pd
import requests

# Set the title of the Streamlit app
st.title("SuperKart sales Prediction")

# Section for online prediction
st.subheader("Online Prediction")

# Collect user input for property features
Product_Sugar_Content = st.selectbox("Product_Sugar_Content", ["Low Sugar", "Regular", "No Sugar","reg"] )
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_Id = st.selectbox("Store_Id", [ "OUT001", "OUT002","OUT003"] )
Store_Size= st.selectbox("Store_Size", ["Medium", "High","Small"] )
Store_Location_City_Type = st.selectbox("Store_Location_City_Type", ["Tier 1", "Tier 2","Tier 3"] )
Store_Type = st.selectbox("Store_Type", ["Supermarket Type1", "Supermarket Type2","Departmental Store","Food Mart"] )
Product_Weight = st.number_input("Product_Weight", min_value=1, step=1, value=1)
Product_MRP = st.number_input("Product_MRP", min_value=1, step=1, value=1)


# Convert user input into a DataFrame
input_data = pd.DataFrame([{
    'Product_Sugar_Content': Product_Sugar_Content,
    'Product_Type': Product_Type,
    'Store_Id': Store_Id,
    'Store_Size': Store_Size,
    'Store_Type': Store_Type,
    'Product_Weight': Product_Weight,
    'Product_MRP': Product_MRP
    }])

# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
  response = requests.post("https://huggingface.co/spaces/mdsalmon159/SalesPredictionBackend.hf.space/v1/sales", json=input_data.to_dict(orient='records' ) [0])
  if response.status_code == 200:
    prediction = response. json() ['Predicted Price (in dollars)']
    st.success(f"Predicted Rental Price (in dollars): {prediction}")
  else:
    st.error("Error making prediction.")

# Section for batch prediction
st.subheader("Batch Prediction")

#allow user to upload csv file
uploaded_file = st.file_uploader("Upload a CSV file", 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://huggingface.co/spaces/mdsalmon159/SalesPredictionBackend.hf.space/v1/sales_batch", files={"file": uploaded_file}) # S
    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.")