import streamlit as st
import pandas as pd
import requests


# Set the title of the Streamlit app
st.title("Welcome to Sales Forecast Prediction Generator")

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


# Collect user input for property features
Product_Weight = st.number_input("Product Weight", min_value=4.0, max_value=22.0, step=1.0, value=12.0)
Product_Sugar_Content = st.selectbox("Product Sugar Content", ["Low Sugar", "No Sugar", "Regular"])
Product_Allocated_Area = st.number_input("Product Allocated Area", min_value=0.004, max_value=0.298, step=0.001, value=0.07)
Product_Type = st.selectbox("Product Type", ["Frozen Foods", "Dairy", "Canned", "Baking Goods", "Health and Hygiene", "Snack Foods", "Meat", "Household", "Hard Drinks", "Fruits and Vegetables", "Breads", "Soft Drinks", "Breakfast", "Others", "Starchy Foods", "Seafood"])
Product_MRP = st.number_input("Product MRP", min_value=4.0, max_value=22.0, step=1.0, value=12.0)
Store_Id = st.selectbox("Store Id", ["OUT004", "OUT003", "OUT002", "OUT001"])
Store_Establishment_Year = st.selectbox("Store Establishment Year", [1987, 1998, 1999, 2009])
Store_Size = st.selectbox("Store Size", ["Medium", "High", "Small"])
Store_Location_City_Type = st.selectbox("Store Location City Type", ["Tier 1", "Tier 2", "Tier 3"])
Store_Type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Grocery Type1", "Grocery Type2", "Supermarket Type3", "Supermarket Type4"])



# 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_Id': Store_Id,
    'Store_Establishment_Year': Store_Establishment_Year,
    '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"):
    response = requests.post("https://akarora93-SalesForecastPredictionBackend.hf.space/v1/product", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
    if response.status_code == 200:
        prediction = response.json()
        st.success(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://akarora93-SalesForecastPredictionBackend.hf.space/v1/productbatch", 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.")