app.py

import streamlit as st
import requests
import json

# Streamlit app title
st.title("SuperKart Sales Prediction")

# Form for user inputs
with st.form("prediction_form"):
    st.header("Enter Product and Store Details")

    # Numerical inputs with ranges based on dataset head and payload
    product_weight = st.number_input(
        "Product Weight (e.g., 9.57 to 16.54)",
        min_value=0.0,
        max_value=50.0,
        value=13.5,
        step=0.01
    )
    product_allocated_area = st.number_input(
        "Product Allocated Area (ratio, e.g., 0.01 to 0.144)",
        min_value=0.0,
        max_value=1.0,
        value=0.075,
        step=0.001
    )
    product_mrp = st.number_input(
        "Product MRP (e.g., 117.08 to 186.31)",
        min_value=0.0,
        max_value=500.0,
        value=155.0,
        step=0.01
    )
    store_establishment_year = st.number_input(
        "Store Establishment Year (e.g., 1987 to 2009)",
        min_value=1900,
        max_value=2025,
        value=2009,
        step=1,
        format="%d"
    )

    # Categorical inputs with options from data dictionary
    product_sugar_content = st.selectbox(
        "Product Sugar Content",
        ["Low Sugar", "Regular", "No Sugar"],
        index=0  # Default to "Low Sugar"
    )
    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"
        ],
        index=1  # Default to "Snack Foods"
    )
    store_size = st.selectbox(
        "Store Size",
        ["Low", "Medium", "High"],
        index=1  # Default to "Medium"
    )
    store_location_city_type = st.selectbox(
        "Store Location City Type",
        ["Tier 1", "Tier 2", "Tier 3"],
        index=1  # Default to "Tier 2"
    )
    store_type = st.selectbox(
        "Store Type",
        ["Departmental Store", "Supermarket Type 1", "Supermarket Type 2", "Food Mart"],
        index=2  # Default to "Supermarket Type2"
    )

    # Submit button
    submitted = st.form_submit_button("Predict Sales")

# API call when form is submitted
if submitted:
    # Prepare payload for API
    payload = {
        "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": int(store_establishment_year),  # Ensure integer
        "Store_Size": store_size,
        "Store_Location_City_Type": store_location_city_type,
        "Store_Type": store_type
    }

    # Send POST request to backend API
    model_url = "https://sahilsingla-SuperKartPredictionBackend.hf.space/v1/sales"
    try:
        response = requests.post(model_url, json=payload)
        response.raise_for_status()  # Raise exception for bad status codes
        result = response.json()
        prediction = result.get("prediction", None)

        if prediction is not None:
            st.success(f"Predicted Sales: ${float(prediction):.2f}")
        else:
            st.error("No prediction returned from API")
    except requests.exceptions.RequestException as e:
        st.error(f"Error contacting the API: {e}")
    except ValueError:
        st.error("Invalid response format from API")

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

st.write("Upload a CSV file with columns: Product_Weight, Product_Sugar_Content, Product_Allocated_Area, Product_Type, Product_MRP, Store_Establishment_Year, Store_Size, Store_Location_City_Type, Store_Type")

# Allow users to upload a CSV file
uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])

if uploaded_file is not None:
    if st.button("Predict Batch"):
        try:
            # Prepare file payload correctly
            files = {"file": (uploaded_file.name, uploaded_file, "text/csv")}

            # Send the POST request to the Flask API
            response = requests.post(
                "https://sahilsingla-SuperKartPredictionBackend.hf.space/v1/salesbatch",
                files=files
            )

            # Check response status
            if response.status_code == 200:
                predictions = response.json()
                st.success("Batch predictions completed!")
                st.write(predictions)
            else:
                st.error(f"Error: {response.status_code}")
                st.error(response.text)

        except Exception as e:
            st.error("An unexpected error occurred during prediction.")
            st.exception(e)