You are building a purely front-end prototype (no backend) of a hyper-local retail dashboard. Use the existing fork at https://v0.dev/chat/fork-of-hyper-local-demand-forecasting-jn5Ea4y3iMh as your starting point and implement the following:1. Layout & Navigation - Split the page into two panels: - Main panel (left): Shows a dynamic heatmap of product sales density. - Seller panel (right): Hidden by default. When the user clicks the “Seller” tab/button, slide in this panel from the right.2. Seller Panel Features - Include a time picker (hour:minute) and a product dropdown list. - When the user selects a time and product and clicks “Add,” push that data into the dashboard state.3. Dynamic Heatmap - Whenever new seller data is added, update the heatmap in the main panel in real time. - Use a realistic demo dataset of geolocated sales events (you can seed it with synthetic points if needed).4. Sales & Revenue Graph - Below the heatmap, display a stacked line chart with two series: “Units Sold Today” and “Revenue Today.” - Dynamically update the chart whenever seller data is added.5. Graph Filters - Provide dropdown filters for: - Product (multi-select) - Time of day (e.g. morning/afternoon/evening or exact hours) - Month - Applying any filter should recompute both the heatmap and the stacked line chart immediately.6. Styling & Tech - Keep the UI “techy” and modern—use a dark theme, neon accents, crisp typography. - Prefer libraries you already have in the fork (e.g., D3.js, Chart.js, React components).7. Data Integration - Load an extensive dataset from kaggle or generate a demo extensive dataset when the app initializes. - Merge any new seller entries with this dataset in memory. - Ensure all analytics (heatmap, charts) are updated by both the initial data and any new entries.Deliver a fully interactive front-end prototype that meets these requirements. No actual backend or database—everything can live in browser state or use a static JSON file.

Implement the following changes-1) Beutify the slider.2) Add actual products and actual data from kaggle.3) Add year in Filter4) Make product as a dropdown5) Beautify the Heatmap, add x-axis and y-axis descriptions6) Add a separate filter for graph.7) Make every component screen dynamic8) Keep the homepage as th v1 version. The v2 version shall be shown as a different page on clicking View Analytics.9) Add legit products and dataset from Kaggle. Display those products in both dynamic heatmap as well as the seller side.10) Basic structure of heatmap should be similar to Leetcode heatmap, just beautify it.11) Add a feature to download the data analytics of a product or the entire store.12) Include AI to show steps or ideas to improve the supply chain, reduce wastage of products and to increase supply before demand increases. Use dataset which caters to this need.