import streamlit as st
import pandas as pd
import requests
import io

# Set the title of the Streamlit app
st.title("Product Store Sales Prediction")

# --- Online Prediction Section ---
st.subheader("Online Prediction")
st.markdown("Enter the details for a single product to get a sales prediction.")

# Create a two-column layout for better organization
col1, col2 = st.columns(2)

with col1:
    # Product-related inputs
    st.text_input("Product ID", "FD_123")
    product_weight = st.number_input("Product Weight (grams)", min_value=0.0, value=150.5, step=0.1)
    product_sugar_content = st.selectbox("Product Sugar Content", ["Low Sugar", "Regular", "High Sugar"])
    product_allocated_area = st.number_input("Product Allocated Area (sq. cm)", min_value=0.0, value=500.0, step=10.0)
    product_type = st.selectbox("Product Type", [
        "Dairy", "Soft Drinks", "Meat", "Fruits and Vegetables", "Household",
        "Baking Goods", "Snack Foods", "Frozen Foods", "Breakfast",
        "Health and Hygiene", "Hard Drinks", "Canned", "Breads", "Starchy Foods",
        "Others", "Seafood"
    ])
    product_mrp = st.number_input("Product MRP (in $)", min_value=0.0, value=150.0)

with col2:
    # Store-related inputs
    st.text_input("Store ID", "STR_001")
    store_establishment_year = st.number_input("Store Establishment Year", min_value=1900, max_value=2024, step=1, value=2010)
    store_size = st.selectbox("Store Size", ["Small", "Medium", "High"])
    store_location_city_type = st.selectbox("Store Location City Type", ["City Tier 1", "City Tier 2", "City Tier 3"])
    store_type = st.selectbox("Store Type", ["Supermarket Type1", "Supermarket Type2", "Grocery Store", "Supermarket Type3"])

# Button to trigger the prediction
if st.button("Predict Sales"):
    # Collect user input into a dictionary
    input_data = {
        '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,
        'Store_Type': store_type
    }

    # Display the collected data for confirmation
    st.write("---")
    st.write("**Input Data for Prediction:**")
    st.json(input_data)

    # --- API Call ---
    # NOTE: Replace the URL with your actual API endpoint.
    API_URL_SINGLE = "https://vaishaliagarwal-ProductPricePredictionBackend.hf.space/v1/predict_sales"
    try:
        # Send data to the prediction API
        response = requests.post(API_URL_SINGLE, json=input_data)
        response.raise_for_status()  # Raise an exception for bad status codes (4xx or 5xx)

        if response.status_code == 200:
            prediction = response.json()
            # Assuming the API returns a dictionary like: {'Predicted_Sales': 5500.75}
            predicted_sales = prediction.get('Predicted_Sales', 'N/A')
            st.success(f"**Predicted Total Sales:** ${predicted_sales:,.2f}")
        else:
            st.error(f"Error: Received status code {response.status_code}")
            st.json(response.json())

    except requests.exceptions.RequestException as e:
        st.error(f"API request failed: {e}")
        st.warning("Please ensure the backend API is running and the URL is correct.")


# --- Batch Prediction Section ---
st.subheader("Batch Prediction")
st.markdown("Upload a CSV file with multiple product details to get batch sales predictions.")

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

if uploaded_file is not None:
    # Display a preview of the uploaded data
    try:
        df_upload = pd.read_csv(uploaded_file)
        st.write("**Uploaded Data Preview:**")
        st.dataframe(df_upload.head())

        # Button to trigger batch prediction
        if st.button("Predict for Batch"):
            # --- API Call for Batch Prediction ---
            # NOTE: Replace the URL with your actual batch prediction API endpoint.
            API_URL_BATCH = "https://vaishaliagarwal-ProductPricePredictionBackend.hf.space/v1/predict_sales_batch"

            # Reset file pointer to the beginning before sending
            uploaded_file.seek(0)
            
            # The file needs to be sent in a 'files' dictionary
            files = {"file": (uploaded_file.name, uploaded_file, "text/csv")}
            
            try:
                with st.spinner('Processing batch prediction...'):
                    response = requests.post(API_URL_BATCH, files=files)
                    response.raise_for_status()

                    if response.status_code == 200:
                        # Assuming the API returns a JSON array of predictions
                        predictions = response.json()
                        st.success("Batch predictions completed successfully!")

                        # Convert predictions to a DataFrame for better display
                        df_predictions = pd.DataFrame(predictions)
                        st.write("**Prediction Results:**")
                        st.dataframe(df_predictions)

                        # Provide an option to download the results
                        @st.cache_data
                        def convert_df_to_csv(df):
                            return df.to_csv(index=False).encode('utf-8')

                        csv = convert_df_to_csv(df_predictions)
                        st.download_button(
                           label="Download Predictions as CSV",
                           data=csv,
                           file_name='sales_predictions.csv',
                           mime='text/csv',
                        )
                    else:
                        st.error(f"Error: Received status code {response.status_code}")
                        st.json(response.json())

            except requests.exceptions.RequestException as e:
                st.error(f"API request failed: {e}")
                st.warning("Please ensure the backend API is running and the URL is correct.")

    except Exception as e:
        st.error(f"An error occurred while processing the file: {e}")