Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,83 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import plotly.express as px
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+# 1. Page Configuration
+st.set_page_config(page_title="Store Sales Forecast", page_icon="🛒", layout="wide")
 
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
+# 2. Title and Description
+st.title("🛒 Store Sales Forecasting Dashboard")
+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.
+""")
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
+# 3. Load Data Function (Cached for performance)
+@st.cache_data
+def load_data():
+    # Load the CSV file we prepared earlier
+    df = pd.read_csv("dashboard_data.csv")
+    
+    # Ensure date column is in datetime format
+    if 'date' in df.columns:
+        df['date'] = pd.to_datetime(df['date'])
+    return df
 
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
+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()
 
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
+    # Family Selector
+    # Note: If you decoded labels back to text, this will show text. 
+    # If not, it will show the encoded ID numbers.
+    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)
+        
+        # Customize chart appearance
+        fig.update_layout(line_color='#00CC96', 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.")
 
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
+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}")
 
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# Footer / Credits
+st.markdown("---")
+st.markdown("Created with ❤️ using **Streamlit** and **XGBoost**.")