import streamlit as st
import pandas as pd
import requests

# Set the title of the Streamlit app
st.title("SuperKart Sales Predictor")

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

# Collect business input for features
Product_Weight = st.number_input("Product Weight", min_value=0.0, max_value=100.0, step=0.1)
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "No Sugar", "Regular"])
Product_Type = st.selectbox("Product Type", ["Perishable", "Non Perishable"])
Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.000, max_value=0.300, step=0.1)
Product_MRP = st.number_input("Product MRP", min_value=00.00, max_value=1000.00, step=0.1)
Store_Size = st.selectbox("Store Size", ["Small", "Medium", "High"])
Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
Store_Type = st.selectbox("Store Type", ["Departmental Store", "Food Mart", "Supermarket Type1", "Supermarket Type2"])
Store_Current_Age = st.number_input("Store Current Age", min_value=0, max_value=100, step=1)

# Convert user input into a DataFrame
business_df = pd.DataFrame({
    'Product_Weight': [Product_Weight],
    'Product_Sugar_Content': [Product_Sugar_Content],
    'Product_Type': [Product_Type],
    'Product_Allocated_Area': [Product_Allocated_Area],
    'Product_MRP': [Product_MRP],
    'Store_Size': [Store_Size],
    'Store_Location_City_Type': [Store_Location_City_Type],
    'Store_Type': [Store_Type],
    'Store_Current_Age': [Store_Current_Age]  # Changed key name
})

# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
    backend_url = "https://vrs1503-superkart-backend.hf.space/v1/predict"  # Ensure correct URL
    try:
        response = requests.post(backend_url, json=business_df.to_dict(orient="records")[0])
        response.raise_for_status()  # Raise an exception for bad status codes
        data = response.json()
        if 'prediction' in data:
            prediction = data['prediction'][0]  # Access the first element of the list
            st.success(f"Predicted Sales (in dollars): {prediction}")
        else:
            st.error(f"Error: 'prediction' key not found in response. Response: {data}")
    except requests.exceptions.RequestException as e:
        st.error(f"Error making prediction: {e}")

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

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

# Make predictions when the "Predict" button is clicked
if uploaded_file is not None:
    if st.button("Predict Batch"):  # Changed button name to avoid duplication
        backend_url = "https://vrs1503-superkart-backend.hf.space/v1/batch_predict" # Ensure correct URL
        try:
            response = requests.post(backend_url, files={"file": uploaded_file})
            response.raise_for_status()
            predictions = response.json()
            st.success("Batch predictions completed!")
            st.write(predictions)  # Display the predictions
        except requests.exceptions.RequestException as e:
            st.error(f"Error making batch prediction: {e}")