Modify readme and app
This commit is contained in:
Jsrodrigue
@@ -1,6 +1,8 @@
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# Synthetic Data Generator
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**NOTE:** This is a copy of the repository https://github.com/Jsrodrigue/synthetic-data-creator.
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# Synthetic Data Generator
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An intelligent synthetic data generator that uses OpenAI models to create realistic tabular datasets based on reference data. This project includes an intuitive web interface built with Gradio.
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> **🎓 Educational Project**: This project was inspired by the highly regarded LLM Engineering course on Udemy: [LLM Engineering: Master AI and Large Language Models](https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models/learn/lecture/52941433#questions/23828099). It demonstrates practical applications of LLM engineering principles, prompt engineering, and synthetic data generation techniques.
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@@ -61,7 +63,7 @@ An intelligent synthetic data generator that uses OpenAI models to create realis
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- Python 3.12+
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- OpenAI account with API key
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### Installation with pip
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### Option 1: Using pip
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```bash
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# Create virtual environment
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python -m venv venv
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@@ -71,11 +73,11 @@ source venv/bin/activate # On Windows: venv\Scripts\activate
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pip install -r requirements.txt
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```
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### Installation with uv
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### Option 2: Using uv
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```bash
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# Clone the repository
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git clone https://github.com/Jsrodrigue/synthetic-data-creator.git
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cd synthetic_data
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cd synthetic-data-creator
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# Install dependencies
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uv sync
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@@ -100,8 +102,15 @@ OPENAI_API_KEY=your_api_key_here
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## 🎯 Usage
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### Start the application
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You can run the app either with **Python** or with **uv** (recommended if you installed dependencies using `uv sync`):
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```bash
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# Option 1: using Python
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python app.py
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# Option 2: using uv (no need to activate venv manually)
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uv run app.py
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```
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The script will print a local URL (e.g., http://localhost:7860) — open that link in your browser.
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@@ -1,131 +1,156 @@
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import gradio as gr
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import os
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import atexit
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from src.IO_utils import cleanup_temp_files
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from src.data_generation import generate_and_evaluate_data
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from src.plot_utils import display_reference_csv
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from dotenv import load_dotenv
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import os
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import gradio as gr
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import openai
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from dotenv import load_dotenv
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from src.constants import PROJECT_TEMP_DIR, SYSTEM_PROMPT, USER_PROMPT
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from src.data_generation import generate_and_evaluate_data
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from src.IO_utils import cleanup_temp_files
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from src.plot_utils import display_reference_csv
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# ==========================================================
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# Setup
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# ==========================================================
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#Load the api key
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load_dotenv()
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openai.api_key = os.getenv("OPENAI_API_KEY")
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def main():
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# ==========================================================
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# Setup
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# ==========================================================
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# Temporary folder for images
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os.makedirs(PROJECT_TEMP_DIR, exist_ok=True)
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# Load the api key
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load_dotenv()
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openai.api_key = os.getenv("OPENAI_API_KEY")
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# Ensure temporary plot images are deleted when the program exits
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atexit.register(lambda: cleanup_temp_files(PROJECT_TEMP_DIR))
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# Temporary folder for images
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os.makedirs(PROJECT_TEMP_DIR, exist_ok=True)
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# ==========================================================
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# Gradio App
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# ==========================================================
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with gr.Blocks() as demo:
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# Ensure temporary plot images are deleted when the program exits
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atexit.register(lambda: cleanup_temp_files(PROJECT_TEMP_DIR))
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# Store temp folder in state
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temp_dir_state = gr.State(value=PROJECT_TEMP_DIR)
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# ==========================================================
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# Gradio App
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# ==========================================================
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with gr.Blocks() as demo:
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gr.Markdown("# 🧠 Synthetic Data Generator (with OpenAI)")
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# Store temp folder in state
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temp_dir_state = gr.State(value=PROJECT_TEMP_DIR)
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# ======================================================
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# Tabs for organized sections
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# ======================================================
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with gr.Tabs():
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gr.Markdown("# 🧠 Synthetic Data Generator (with OpenAI)")
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# ------------------------------
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# Tab 1: Input
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# ------------------------------
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with gr.Tab("Input"):
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# ======================================================
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# Tabs for organized sections
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# ======================================================
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with gr.Tabs():
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# System prompt in collapsible
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with gr.Accordion("System Prompt (click to expand)", open=False):
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system_prompt_input = gr.Textbox(
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label="System Prompt",
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value=SYSTEM_PROMPT,
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lines=20
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# ------------------------------
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# Tab 1: Input
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# ------------------------------
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with gr.Tab("Input"):
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# System prompt in collapsible
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with gr.Accordion("System Prompt (click to expand)", open=False):
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system_prompt_input = gr.Textbox(
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label="System Prompt", value=SYSTEM_PROMPT, lines=20
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)
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# User prompt box
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user_prompt_input = gr.Textbox(
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label="User Prompt", value=USER_PROMPT, lines=5
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)
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# User prompt box
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user_prompt_input = gr.Textbox(label="User Prompt", value=USER_PROMPT, lines=5)
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# Model selection
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model_select = gr.Dropdown(
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label="OpenAI Model",
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choices=["gpt-4o-mini", "gpt-4.1-mini"],
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value="gpt-4o-mini",
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)
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# Model selection
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model_select = gr.Dropdown(
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label="OpenAI Model",
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choices=["gpt-4o-mini", "gpt-4.1-mini"],
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value="gpt-4o-mini"
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)
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# Reference CSV upload
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reference_input = gr.File(
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label="Reference CSV (optional)", file_types=[".csv"]
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)
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# Reference CSV upload
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reference_input = gr.File(label="Reference CSV (optional)", file_types=[".csv"])
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# Examples
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gr.Examples(
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examples=[
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"data/sentiment_reference.csv",
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"data/people_reference.csv",
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"data/wine_reference.csv",
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],
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inputs=reference_input,
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)
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# Examples
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gr.Examples(
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examples=["data/sentiment_reference.csv","data/people_reference.csv","data/wine_reference.csv"],
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inputs=reference_input
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)
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# Generate button
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generate_btn = gr.Button("🚀 Generate Data")
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# Generate button
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generate_btn = gr.Button("🚀 Generate Data")
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# Download button
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download_csv = gr.File(label="Download CSV")
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# Download button
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download_csv = gr.File(label="Download CSV")
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# ------------------------------
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# Tab 2: Reference Table
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# ------------------------------
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with gr.Tab("Reference Table"):
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reference_display = gr.DataFrame(label="Reference CSV Preview")
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# ------------------------------
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# Tab 2: Reference Table
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# ------------------------------
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with gr.Tab("Reference Table"):
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reference_display = gr.DataFrame(label="Reference CSV Preview")
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# ------------------------------
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# Tab 3: Generated Table
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# ------------------------------
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with gr.Tab("Generated Table"):
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output_df = gr.DataFrame(label="Generated Data")
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# ------------------------------
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# Tab 3: Generated Table
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# ------------------------------
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with gr.Tab("Generated Table"):
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output_df = gr.DataFrame(label="Generated Data")
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# ------------------------------
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# Tab 4: Evaluation
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# ------------------------------
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with gr.Tab("Comparison"):
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with gr.Accordion("Evaluation Results (click to expand)", open=True):
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evaluation_df = gr.DataFrame(label="Evaluation Results")
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# ------------------------------
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# Tab 4: Evaluation
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# ------------------------------
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with gr.Tab("Comparison"):
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with gr.Accordion("Evaluation Results (click to expand)", open=True):
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evaluation_df = gr.DataFrame(label="Evaluation Results")
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# ------------------------------
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# Tab 5: Visualizations
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# ------------------------------
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# ------------------------------
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# Tab 5: Visualizations
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# ------------------------------
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with gr.Tab("Visualizations"):
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gr.Markdown("# Click on the box to expand")
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with gr.Tab("Visualizations"):
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gr.Markdown("# Click on the box to expand")
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images_gallery = gr.Gallery(
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label="Column Visualizations",
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show_label=True,
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columns=2,
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height='auto',
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interactive=True
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)
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images_gallery = gr.Gallery(
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label="Column Visualizations",
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show_label=True,
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columns=2,
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height="auto",
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interactive=True,
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)
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# Hidden state for internal use
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generated_state = gr.State()
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# Hidden state for internal use
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generated_state = gr.State()
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# ======================================================
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# Event bindings
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# ======================================================
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generate_btn.click(
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fn=generate_and_evaluate_data,
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inputs=[system_prompt_input, user_prompt_input, temp_dir_state, reference_input, model_select],
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outputs=[output_df, download_csv, evaluation_df, generated_state, images_gallery]
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)
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# ======================================================
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# Event bindings
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# ======================================================
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generate_btn.click(
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fn=generate_and_evaluate_data,
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inputs=[
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system_prompt_input,
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user_prompt_input,
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temp_dir_state,
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reference_input,
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model_select,
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],
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outputs=[
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output_df,
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download_csv,
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evaluation_df,
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generated_state,
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images_gallery,
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],
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)
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reference_input.change(
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fn=display_reference_csv,
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inputs=[reference_input],
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outputs=[reference_display]
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)
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reference_input.change(
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fn=display_reference_csv,
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inputs=[reference_input],
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outputs=[reference_display],
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)
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demo.launch(debug=True)
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demo.launch(debug=True)
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if __name__ == "__main__":
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main()
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