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

# Configure Streamlit page
st.set_page_config(
    page_title="SuperKart Sales Prediction",
    page_icon="🛒",
    layout="wide"
)

# Add custom CSS
st.markdown("""
    <style>
    .stButton>button {
        width: 100%;
        background-color: #4CAF50;
        color: white;
        padding: 10px;
        border: none;
        border-radius: 4px;
        cursor: pointer;
    }
    .stButton>button:hover {
        background-color: #45a049;
    }
    </style>
    """, unsafe_allow_html=True)

st.title("SuperKart Sales Prediction")
st.write("Upload your CSV file to get sales predictions")

# File uploader
uploaded_file = st.file_uploader("Choose a CSV file", type="csv")

if uploaded_file is not None:
    try:
        # Display the uploaded data
        df = pd.read_csv(uploaded_file)
        st.write("Preview of uploaded data:")
        st.dataframe(df.head())
        
        # Add a button for predictions
        if st.button("Get Predictions", key="predict_button"):
            with st.spinner("Getting predictions..."):
                # Prepare the file for API request
                files = {"file": ("data.csv", uploaded_file.getvalue(), "text/csv")}
                
                try:
                    # Make request to the backend API
                    response = requests.post("https://huggingface.co/spaces/abhishek-kumar/superkart_sales_backend/predict", files=files)
                    
                    if response.status_code == 200:
                        predictions = response.json()["predictions"]
                        
                        # Add predictions to the dataframe
                        df["Predicted_Sales"] = predictions
                        
                        st.success("Predictions generated successfully!")
                        st.write("Predictions:")
                        st.dataframe(df)
                        
                        # Download button for results
                        csv = df.to_csv(index=False)
                        st.download_button(
                            label="Download predictions",
                            data=csv,
                            file_name="predictions.csv",
                            mime="text/csv"
                        )
                    else:
                        st.error(f"Error getting predictions from the API. Status code: {response.status_code}")
                except Exception as e:
                    st.error(f"Error: {str(e)}")
    except Exception as e:
        st.error(f"Error reading the CSV file: {str(e)}")