import streamlit as st
import pandas as pd
import requests

# -------------------------------
# Streamlit Frontend for Superkart Sales Prediction
# -------------------------------

# Set the title of the Streamlit app
st.title("Superkart Sales Prediction App")

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


# Numeric inputs
Product_Weight = st.number_input("Product Weight (in grams)", min_value=0.0, value=500.0)
Product_Allocated_Area = st.number_input("Allocated Area (sq ft)", min_value=0.0, value=100.0)
Product_MRP = st.number_input("Product MRP", min_value=0.0, value=50.0)
Store_Establishment_Year = st.number_input("Store Establishment Year", min_value=1900, max_value=2025, value=2000)

# Categorical inputs
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low", "Medium", "High"])
Product_Type = st.selectbox("Product Type", ["Food", "Beverage", "Snack", "Other"])
Store_Size = st.selectbox("Store Size", ["Small", "Medium", "Large"])
Store_Location_City_Type = st.selectbox("City Type", ["Tier 1", "Tier 2", "Tier 3"])
Store_Type = st.selectbox("Store Type", ["Mall", "Standalone", "Supermarket", "Other"])

 

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

# Make prediction when the "Predict" button is clicked
if st.button("Predict Sales"):
    response = requests.post(
        "https://Anusha3-Superkart-Backend-Docker-space.hf.space/v1/sales",
        json=input_data.to_dict(orient='records')[0]
    )  # Send data to backend API

    if response.status_code == 200:
        prediction = response.json()['Predicted Sales']
        st.success(f"Predicted Sales: {prediction}")
    else:
        st.error("Error making prediction. Please check the backend logs.")

# 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 Sales"):
        response = requests.post(
            "https://Anusha3-Superkart-Backend-Docker-space.hf.space/v1/salesbatch",  files={"file": uploaded_file}   )  # Send file to backend 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.")