update readme to about

src/synthetic_dataset_generator/app.py

@@ -1,7 +1,7 @@
 from synthetic_dataset_generator._tabbedinterface import TabbedInterface
 
 # from synthetic_dataset_generator.apps.eval import app as eval_app
-from synthetic_dataset_generator.apps.readme import app as readme_app
+from synthetic_dataset_generator.apps.about import app as about_app
 from synthetic_dataset_generator.apps.sft import app as sft_app
 from synthetic_dataset_generator.apps.textcat import app as textcat_app
 
@@ -21,8 +21,8 @@ button[role="tab"][aria-selected="true"]:hover {border-color: var(--button-prima
 image = """<br><img src="https://raw.githubusercontent.com/argilla-io/synthetic-data-generator/main/assets/logo.svg" alt="Synthetic Data Generator Logo" style="display: block; margin-left: auto; margin-right: auto; width: clamp(50%, 400px, 100%)"/>"""
 
 demo = TabbedInterface(
-    [textcat_app, sft_app, readme_app],
-    ["Text Classification", "Supervised Fine-Tuning", "README"],
+    [textcat_app, sft_app, about_app],
+    ["Text Classification", "Supervised Fine-Tuning", "About"],
     css=css,
     title=image,
     theme=theme,

src/synthetic_dataset_generator/apps/about.py

@@ -0,0 +1,16 @@
+import gradio as gr
+
+with gr.Blocks() as app:
+    gr.Markdown(
+        """
+        Synthetic data is artificially generated information that mimics real-world data. It allows overcoming data limitations by expanding or enhancing datasets.
+
+        Introducing the Synthetic Data Generator, a user-friendly application that takes a no-code approach to creating custom datasets with Large Language Models (LLMs). The best part: A simple step-by-step process, making dataset creation a non-technical breeze, allowing anyone to create datasets and models in minutes and without any code.
+
+        The synthetic data generator takes your custom prompt and returns a dataset for your use case, using a synthetic data pipeline. In the background this is powered by distilabel and the free Hugging Face text-generation API but we don’t need to worry about these complexities and we can focus on using the UI.
+
+
+        - Read more in [our announcement blog post](https://huggingface.co/blog/synthetic-data-generator)
+        - Find the library on [GitHub](https://github.com/argilla-io/synthetic-data-generator)
+        """
+    )

src/synthetic_dataset_generator/apps/readme.py

@@ -1,11 +0,0 @@
-import gradio as gr
-import requests
-
-with gr.Blocks() as app:
-    with gr.Row():
-        url = "https://raw.githubusercontent.com/argilla-io/synthetic-data-generator/refs/heads/main/README.md"
-        response = requests.get(url)
-        readme_content: str = response.text
-        readme_content = readme_content.split("## Introduction")[1]
-        readme_content = "## Introduction" + readme_content
-        gr.Markdown(readme_content)