hf deployment

.gitignore

@@ -8,13 +8,5 @@ __pycache__/
 # ignore environment variables
 .env
 
-# ignore old multi-page files (no longer used)
-pages/
-utils/chartbot_dataset_layout.py
-utils/components.py
-
-# ignore design html file
-design.html
-
 # ignore temporary files created
 temp*

README.md

@@ -1,60 +1,93 @@
-# ChaRtBot - Data Visualization Assistant
+---
+title: ChaRtBot
+emoji: 📊
+sdk: docker
+app_file: app.py
+pinned: true
+---
 
-A single-page Dash application that helps users visualize their CSV data using natural language prompts powered by AI.
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
-## Features
+# Dash on Spaces
 
-- **Simple, Clean UI**: Modern design with gradient backgrounds and smooth animations
-- **CSV File Upload**: Easy drag-and-drop or click-to-upload functionality (CSV files only)
-- **Natural Language Prompts**: Describe the visualization you want in plain English
-- **AI-Powered Visualizations**: Automatically generates Plotly charts based on your data and prompts
-- **Download Charts**: Export generated visualizations as standalone HTML files
-- **Error Handling**: Comprehensive error handling for file uploads, API calls, and data processing
+![Gapminder Dashboard Screenshot](screenshot.png)
 
-## How to Use
+With Dash Open Source, you can create data apps on your laptop in pure Python, no JavaScript required.
 
-1. **Start the App**:
-   ```bash
-   source .venv/bin/activate
-   python app.py
-   ```
+Get familiar with Dash by building a [sample app](https://dash.plotly.com/tutorial) with open source. Scale up with [Dash Enterprise](https://plotly.com/dash/) when your Dash app is ready for department or company-wide consumption. Or, launch your initiative with Dash Enterprise from the start to unlock developer productivity gains and hands-on acceleration from Plotly's team.
 
-2. **Upload Your Data**: Click "Choose file" and select a CSV file
+## Deploy Dash on Spaces
 
-3. **Enter Your Prompt**: Describe the visualization you want, e.g.:
-   - "Create a bar chart showing the top 10 values"
-   - "Make a scatter plot with X vs Y"
-   - "Show me a pie chart of category distribution"
+To get started with Dash on Spaces, click the button below:
 
-4. **Submit**: Click the Submit button to generate your visualization
+<a href="http://huggingface.co/new-space?template=plotly/dash-app-template" target="_blank">
+    <img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/deploy-to-spaces-lg.svg" alt="">
+</a>
 
-5. **Download** (Optional): Download the generated chart as an HTML file
+This will start building your Space using Plotly's Dash Docker template. If successful, you should see a similar application to the [Dash template app](https://huggingface.co/spaces/dash/dash-app-template).
 
-## Technical Details
+## Customizing your Dash app
 
-### Structure
-- **app.py**: Main application file with all callbacks and layout
-- **utils/prompt.py**: LLM integration for generating visualization code
-- **utils/helpers.py**: Helper functions for processing data and displaying results
-- **assets/custom.css**: Custom styling matching the design specifications
+If you have never built with Dash before, we recommend getting started with our [Dash in 20 minutes tutorial](https://dash.plotly.com/tutorial).
 
-### Callbacks
-1. **File Upload**: Validates CSV files, stores data in memory
-2. **Submit**: Validates prompt and data, calls LLM, generates visualizations
-3. **Download**: Provides HTML export functionality
+When you create a Dash Space, you'll get a few key files to help you get started:
 
-### Error Handling
-- CSV file validation
-- Missing prompt validation
-- Missing file validation
-- API error handling
-- Code execution error handling
+### 1. app.py
 
-## Requirements
+This is the main app file that defines the core logic of your project. Dash apps are often structured as modules, and you can optionally seperate your layout, callbacks, and data into other files, like `layout.py`, etc.
 
-See requirements in the Python environment. Main dependencies:
-- Dash
-- Plotly
-- Pandas
-- LangChain (for LLM integration)
-- Dash Mantine Components
+Inside of `app.py` you will see:
+
+1. `from dash import Dash, html`
+   We import the `Dash` object to define our app, and the `html` library, which gives us building blocks to assemble our project.
+
+2. `app = Dash()`
+   Here, we define our app. Layout, server, and callbacks are _bound_ to the `app` object.
+
+3. `server = app.server`
+   Here, we define our server variable, which is used to run the app in production.
+
+4. `app.layout = `
+   The starter app layout is defined as a list of Dash components, an indivdual Dash component, or a function that returns either.
+
+   The `app.layout` is your initial layout that will be updated as a single-page application by callbacks and other logic in your project.
+
+5. `if __name__ == '__main__': app.run(debug=True)`
+   If you are running your project locally with `python app.py`, `app.run(...)` will execute and start up a development server to work on your project, with features including hot reloading, the callback graph, and more.
+
+   In production, we recommend `gunicorn`, which is a production-grade server. Debug features will not be enabled when running your project with `gunicorn`, so this line will never be reached.
+
+### 2. Dockerfile
+
+The Dockerfile for a Dash app is minimal since Dash has few system dependencies. The key requirements are:
+
+- It installs the dependencies listed in `requirements.txt` (using `uv`)
+- It creates a non-root user for security
+- It runs the app with `gunicorn` using `gunicorn app:server --workers 4`
+
+You may need to modify this file if your application requires additional system dependencies, permissions, or other CLI flags.
+
+### 3. requirements.txt
+
+The Space will automatically install dependencies listed in the `requirements.txt` file. At minimum, you must include `dash` and `gunicorn` in this file. You will want to add any other required packages your app needs.
+
+The Dash Space template provides a basic setup that you can extend based on your needs.
+
+## Additional Resources and Support
+
+- [Dash documentation](https://dash.plotly.com)
+- [Dash GitHub repository](https://github.com/plotly/dash)
+- [Dash Community Forums](https://community.plotly.com)
+- [Dash Enterprise](https://plotly.com/dash)
+- [Dash template Space](https://huggingface.co/spaces/plotly/dash-app-template)
+
+## Troubleshooting
+
+If you encounter issues:
+
+1. Make sure your notebook runs locally in app mode using `python app.py`
+2. Check that all required packages are listed in `requirements.txt`
+3. Verify the port configuration matches (7860 is the default for Spaces)
+4. Check Space logs for any Python errors
+
+For more help, visit the [Plotly Community Forums](https://community.plotly.com) or [open an issue](https://github.com/plotly/dash/issues).

requirements.txt

@@ -1,3 +1,4 @@
+gunicorn
 annotated-types==0.7.0
 anyio==4.12.0
 blinker==1.9.0