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_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_Sugar_Content = st.selectbox("Product sugar content",["Low Sugar","Regular","No Sugar"])
Store_Size = st.selectbox("store size", ["High","Medium","Small"])
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"])
Product_Age = st.number_input("Product_Age", min_value=0)
Product_Weight = st.number_input("Product_Weight", min_value=0.0)
Product_Allocated_Area = st.number_input("Product_Allocated_Area", min_value=0)
Product_MRP = st.number_input("Product_MRP", min_value=0)

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

# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
    response = requests.post("https://CRRSalesPredictionFrontend/v1/sales", json=input_data.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 Rental 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 Batch"):
        response = requests.post("https://CRRSalesPredictionFrontend/v1/salesbatch", 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.")