import streamlit as st
import pandas as pd
import joblib

# Load the trained regression model
def load_model():
    return joblib.load("/content/deployment_files/sales_prediction_model_v1_0.joblib")

model = load_model()

# Set the title of the Streamlit app
st.title("Welcome to SuperKart Sales Forecasting")

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

# Collect user input for store features
# Numeric inputs
Product_Weight = st.number_input("Product Weight (in kg)", value=0.0)
Product_Allocated_Area = st.number_input("Product Allocated Area (sq ft)", value=0.0)
Product_MRP = st.number_input("Product MRP (in ₹)", value=0.0)
Store_Establishment_Year = st.number_input("Store Establishment Year", value=2000, step=1)

# Categorical inputs
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low", "Medium", "High"])
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_Size = st.selectbox("Store Size", ["Small", "Medium", "Large"])
Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])

# Convert user input into a DataFrame
input_data = pd.DataFrame({
    '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': [Store_Establishment_Year],
    'Store_Size': [Store_Size],
    'Store_Location_City_Type': [Store_Location_City_Type]
})

# Make prediction when the "Predict" button is clicked
if st.button("Predict"):
    response = requests.post(
        "https://Shabn-SuperKart-Sales-Prediction.hf.space/v1/sales", json=input_data.to_dict(orient="records")[0])
    if response.status_code == 200:
        prediction = response.json()["Predicted_Sales_Total"]
        st.success(f"Predicted Sales Total: {prediction}")
    else:
        st.error("Error making prediction. Please check the input data.")

# 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://Shabn-SuperKart-Sales-Prediction.hf.space/v1/salesbatch", files={'file': uploaded_file})
        if response.status_code == 200:
            predictions = response.json()
            st.success('Batch prediction completed successfully!')
            st.write(pd.DataFrame(predictions))
        else:
            st.error('Error making batch prediction. Please check the file format and try again.')