| | --- |
| | title: Reflection With Code Execuation Chart Generation |
| | emoji: 🌍 |
| | colorFrom: green |
| | colorTo: green |
| | sdk: gradio |
| | sdk_version: 6.5.1 |
| | app_file: app.py |
| | pinned: false |
| | license: apache-2.0 |
| | --- |
| | |
| | # Chart Generation with LLM Agents |
| |
|
| | A Gradio app that uses LLM agents with reflection pattern to generate and improve data visualizations. |
| |
|
| | ## Features |
| |
|
| | - 🤖 **LLM-Powered Chart Generation**: Uses GPT-4 or Claude to generate matplotlib code |
| | - 🔄 **Reflection Pattern**: Automatically improves charts by analyzing V1 and generating V2 |
| | - 📊 **Coffee Sales Dataset**: Pre-loaded dataset for demonstration |
| | - 🎨 **Clean UI**: User-friendly Gradio interface |
| |
|
| | ## How It Works |
| |
|
| | 1. **Generate V1**: LLM creates initial chart code based on your instruction |
| | 2. **Execute V1**: Code runs and generates first chart |
| | 3. **Reflect**: LLM analyzes V1 chart image and original code |
| | 4. **Generate V2**: LLM creates improved code based on feedback |
| | 5. **Execute V2**: Improved code runs and generates final chart |
| |
|
| | ## Setup |
| |
|
| | ### Local Development |
| |
|
| | 1. Install dependencies: |
| | ```bash |
| | pip install -r requirements.txt |
| | ``` |
| |
|
| | 2. Set up environment variables: |
| | ```bash |
| | export OPENAI_API_KEY="your-key-here" |
| | # OR |
| | export ANTHROPIC_API_KEY="your-key-here" |
| | ``` |
| |
|
| | 3. Run the app: |
| | ```bash |
| | python app.py |
| | ``` |
| |
|
| | ### HuggingFace Spaces Deployment |
| |
|
| | 1. Create a new Space on HuggingFace |
| | 2. Upload all files: |
| | - `app.py` |
| | - `utils.py` |
| | - `coffee_sales_local.csv` |
| | - `requirements.txt` |
| | - `README.md` (this file) |
| |
|
| | 3. Add secrets in Space settings: |
| | - `OPENAI_API_KEY` (if using OpenAI models) |
| | - `ANTHROPIC_API_KEY` (if using Anthropic models) |
| |
|
| | 4. Set Space SDK to **Gradio** |
| |
|
| | 5. The app will automatically deploy! |
| |
|
| | ## Usage |
| |
|
| | 1. Enter your chart instruction (e.g., "Create a plot comparing Q1 coffee sales in 2024 and 2025") |
| | 2. Select models for generation (V1) and reflection (V2) |
| | 3. Click "Generate Charts" |
| | 4. Compare V1 and V2 charts side by side |
| | 5. Review the reflection feedback and code |
| |
|
| | ## Dataset Schema |
| |
|
| | The coffee sales dataset includes: |
| | - `date` (M/D/YY) |
| | - `time` (HH:MM) |
| | - `cash_type` (card or cash) |
| | - `card` (string) |
| | - `price` (number) |
| | - `coffee_name` (string) |
| | - `quarter` (1-4) - auto-derived |
| | - `month` (1-12) - auto-derived |
| | - `year` (YYYY) - auto-derived |
| |
|
| | ## Model Recommendations |
| |
|
| | - **Generation (V1)**: Fast models like `gpt-4o-mini` or `gpt-4o` |
| | - **Reflection (V2)**: Strong reasoning models like `o1-mini`, `o1-preview`, or `claude-3-5-sonnet-20241022` |
| |
|
| | ## Requirements |
| |
|
| | - Python 3.8+ |
| | - OpenAI API key OR Anthropic API key |
| | - See `requirements.txt` for full dependency list |
| |
|
| | ## License |
| |
|
| | Educational project for demonstrating LLM agents with reflection pattern. |
| |
|
| | Note: |
| | The code is from Deepleraning.ai Agnetic AI Course |
| |
|
