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