Modify readme and app

week3/community-contributions/juan_synthetic_data/README.md

@@ -1,6 +1,8 @@
 # Synthetic Data Generator
 **NOTE:** This is a copy of the repository https://github.com/Jsrodrigue/synthetic-data-creator.
 
+# Synthetic Data Generator
+
 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.
 
 > **🎓 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.
@@ -61,7 +63,7 @@ An intelligent synthetic data generator that uses OpenAI models to create realis
 - Python 3.12+
 - OpenAI account with API key
 
-### Installation with pip
+### Option 1: Using pip
 ```bash
 # Create virtual environment
 python -m venv venv
@@ -71,11 +73,11 @@ source venv/bin/activate  # On Windows: venv\Scripts\activate
 pip install -r requirements.txt
 ```
 
-### Installation with uv
+### Option 2: Using uv
 ```bash
 # Clone the repository
 git clone https://github.com/Jsrodrigue/synthetic-data-creator.git
-cd synthetic_data
+cd synthetic-data-creator
 
 # Install dependencies
 uv sync
@@ -100,8 +102,15 @@ OPENAI_API_KEY=your_api_key_here
 ## 🎯 Usage
 
 ### Start the application
+
+You can run the app either with **Python** or with **uv** (recommended if you installed dependencies using `uv sync`):
+
 ```bash
+# Option 1: using Python
 python app.py
+
+# Option 2: using uv (no need to activate venv manually)
+uv run app.py
 ```
 
 The script will print a local URL (e.g., http://localhost:7860) — open that link in your browser.

week3/community-contributions/juan_synthetic_data/app.py

@@ -1,13 +1,17 @@
-import gradio as gr
-import os
 import atexit
-from src.IO_utils import cleanup_temp_files
+import os
-from src.data_generation import generate_and_evaluate_data
-from src.plot_utils import display_reference_csv
-from dotenv import load_dotenv
-import openai
-from src.constants import PROJECT_TEMP_DIR, SYSTEM_PROMPT, USER_PROMPT
 
+import gradio as gr
+import openai
+from dotenv import load_dotenv
+
+from src.constants import PROJECT_TEMP_DIR, SYSTEM_PROMPT, USER_PROMPT
+from src.data_generation import generate_and_evaluate_data
+from src.IO_utils import cleanup_temp_files
+from src.plot_utils import display_reference_csv
+
+
+def main():
     # ==========================================================
     # Setup
     # ==========================================================
@@ -45,28 +49,34 @@ with gr.Blocks() as demo:
                 # System prompt in collapsible
                 with gr.Accordion("System Prompt (click to expand)", open=False):
                     system_prompt_input = gr.Textbox(
-                    label="System Prompt",
+                        label="System Prompt", value=SYSTEM_PROMPT, lines=20
-                    value=SYSTEM_PROMPT,
-                    lines=20
                     )
 
                 # User prompt box
-            user_prompt_input = gr.Textbox(label="User Prompt", value=USER_PROMPT, lines=5)
+                user_prompt_input = gr.Textbox(
+                    label="User Prompt", value=USER_PROMPT, lines=5
+                )
 
                 # Model selection
                 model_select = gr.Dropdown(
                     label="OpenAI Model",
                     choices=["gpt-4o-mini", "gpt-4.1-mini"],
-                value="gpt-4o-mini"
+                    value="gpt-4o-mini",
                 )
 
                 # Reference CSV upload
-            reference_input = gr.File(label="Reference CSV (optional)", file_types=[".csv"])
+                reference_input = gr.File(
+                    label="Reference CSV (optional)", file_types=[".csv"]
+                )
 
                 # Examples
                 gr.Examples(
-                examples=["data/sentiment_reference.csv","data/people_reference.csv","data/wine_reference.csv"],
+                    examples=[
-                inputs=reference_input
+                        "data/sentiment_reference.csv",
+                        "data/people_reference.csv",
+                        "data/wine_reference.csv",
+                    ],
+                    inputs=reference_input,
                 )
 
                 # Generate button
@@ -87,7 +97,6 @@ with gr.Blocks() as demo:
             with gr.Tab("Generated Table"):
                 output_df = gr.DataFrame(label="Generated Data")
 
-
             # ------------------------------
             # Tab 4: Evaluation
             # ------------------------------
@@ -106,8 +115,8 @@ with gr.Blocks() as demo:
                     label="Column Visualizations",
                     show_label=True,
                     columns=2,
-                height='auto',
+                    height="auto",
-                interactive=True
+                    interactive=True,
                 )
 
             # Hidden state for internal use
@@ -118,14 +127,30 @@ with gr.Blocks() as demo:
         # ======================================================
         generate_btn.click(
             fn=generate_and_evaluate_data,
-        inputs=[system_prompt_input, user_prompt_input, temp_dir_state, reference_input, model_select],
+            inputs=[
-        outputs=[output_df, download_csv, evaluation_df, generated_state, images_gallery]
+                system_prompt_input,
+                user_prompt_input,
+                temp_dir_state,
+                reference_input,
+                model_select,
+            ],
+            outputs=[
+                output_df,
+                download_csv,
+                evaluation_df,
+                generated_state,
+                images_gallery,
+            ],
         )
 
         reference_input.change(
             fn=display_reference_csv,
             inputs=[reference_input],
-        outputs=[reference_display]
+            outputs=[reference_display],
         )
 
     demo.launch(debug=True)
+
+
+if __name__ == "__main__":
+    main()