import streamlit as st
import pandas as pd
import plotly.express as px

# 1. Page Configuration
st.set_page_config(page_title="Store Sales Forecast", page_icon="🛒", layout="wide")

# 2. Title and Description
st.title("🛒 Store Sales Forecasting")
st.markdown("""
This application visualizes the 16-day future sales predictions for retail stores in Ecuador. 
The forecasts are generated using an **XGBoost** machine learning model trained on historical data, 
incorporating features like seasonality, oil prices, and holidays.
""")

# 3. Load Data Function (Cached for performance)
@st.cache_data
def load_data():
    # Load the CSV file we prepared earlier
    df = pd.read_csv("src/dashboard_data.csv")
    
    # Ensure date column is in datetime format
    if 'date' in df.columns:
        df['date'] = pd.to_datetime(df['date'])
    return df

try:
    df = load_data()
    
    # --- SIDEBAR (FILTERS) ---
    st.sidebar.header("Filter Options")
    
    # Store Selector
    if 'store_nbr' in df.columns:
        store_list = sorted(df['store_nbr'].unique())
        selected_store = st.sidebar.selectbox("Select Store Number", store_list)
    else:
        st.error("Column 'store_nbr' not found in dataset.")
        st.stop()

    # Family Selector
    if 'family' in df.columns:
        family_list = sorted(df['family'].unique())
        selected_family = st.sidebar.selectbox("Select Product Category (Family)", family_list)
    
    # --- MAIN CONTENT ---
    
    # Filter data based on user selection
    filtered_data = df[(df['store_nbr'] == selected_store) & (df['family'] == selected_family)]
    
    # Sort by date for proper plotting
    if 'date' in filtered_data.columns:
        filtered_data = filtered_data.sort_values('date')
        
        # Plot Chart using Plotly
        st.subheader(f"📅 Forecast for Store #{selected_store} - Category: {selected_family}")
        
        fig = px.line(filtered_data, x='date', y='Predicted_Sales', 
                      title='16-Day Sales Prediction Trend',
                      labels={'Predicted_Sales': 'Predicted Sales Volume', 'date': 'Date'},
                      markers=True)
        
        # --- DÜZELTİLEN KISIM (FIXED PART) ---
        # Renk ayarını 'update_traces' içine aldık (Layout hatasını çözer)
        fig.update_traces(line_color='#00CC96') 
        
        # Layout ayarları sadece hover (üzerine gelince çıkan bilgi) için kaldı
        fig.update_layout(hovermode="x unified")
        # -------------------------------------
        
        st.plotly_chart(fig, use_container_width=True)
        
        # Show Data Table
        with st.expander("View Detailed Forecast Data"):
            st.dataframe(filtered_data[['id', 'date', 'store_nbr', 'family', 'Predicted_Sales']])
            
    else:
        st.warning("Date column is missing. Cannot plot the graph.")

except FileNotFoundError:
    st.error("Error: 'dashboard_data.csv' not found. Please upload the file to Hugging Face Files.")
except Exception as e:
    st.error(f"An unexpected error occurred: {e}")