[fix] Add Hugging Face Space metadata

README.md

@@ -1,117 +1,9 @@
-# 🧬 SynthDataGen: AI-Powered Synthetic Dataset Generator
-
-<img src="assets/logo.jpg" alt="DataSynth_logo" width="200">
-
-<a href="https://synthdatagen-app.onrender.com/">👀 <b>Live Demo</b></a>
-
-📷 <b>Screenshots</b>
-
-<a href="screenshot_1.png"><img src="assets/screenshot_1.png" width="400"></a>
-<a href="screenshot_2.png"><img src="assets/screenshot_2.png" width="335"></a>
-
-
-## 📖 Overview  
-**SynthDataGen** is an AI-powered tool that creates **realistic, fake data** for any project. You don’t need to collect real information—instead, just tell SynthDataGen what kind of data you want, and it will **quickly generate** it. Thanks to its **easy-to-use web interface** built with Gradio, **anyone** can start making custom datasets right away.
-
-### 🔑 **Key Features**  
-- The app can generate **various types of datasets**, such as **tables**, **time-series data**, or **text content**.
-- The output can be saved in different **formats**, including **JSON**, **CSV**, **Parquet**, or **Markdown**.
-- **AI models** like **GPT** and **Claude** are used to automatically create the dataset based on the task.
-- A short **description of the desired dataset** is all that's needed to trigger the generation process.
-- A **download link** is provided once the dataset is ready, making it easy to save and use.
-- The **interface updates options automatically** and includes helpful **examples for inspiration**. 
-
-### 🎯 **How It Works**  
-1️⃣ Describe the dataset to generate by entering a short business problem or topic.
-
-2️⃣ Select the dataset type, output format, AI model, and number of samples.
-
-3️⃣ Download the generated dataset once it's ready — clean, structured, and ready to use..  
-
-### 🤔 **Why Choose SynthDataGen?**  
-- ⏰ **Time Saver**: Automatically creates tables, time-series, or text data—no need to gather real data yourself.  
-- ⚙️ **Flexible and Accessible**: Supports multiple formats (JSON, CSV, Parquet, Markdown) with a beginner-friendly interface.  
-- 🤖 **Powered by GPT & Claude**: Uses two top AI models to produce realistic synthetic data for prototyping or research.  
-
-### 🔧 **SynthDataGen Customization**
-SynthDataGen is fully customizable through Python code. You can easily modify:
-- ✏️ **System prompt** to control how the AI models generate code  
-- 🤖 Easily add **new frontier** or **open-source models** (e.g., LLaMA, DeepSeek, Qwen), or integrate any model from **Hugging Face libraries** and **inference endpoints**.
-- 📊 **Dataset types**, by adding new categories like image metadata, dialogue transcripts ...
-- 📁 **Output formats**, such as YAML, XML ...
-- 🎨 **Interface styling**, including layout, colors, and themes  
-
-### 🏗️ **Architecture** 
-
-<a href="func_architecture.png"><img src="assets/func_architecture.png"></a>
-<a href="tech_architecture.png"><img src="assets/tech_architecture.png"></a>
-
-## ⚙️ Setup & Installation  
-
-**1. Clone the Repository**  
-```bash
-git clone https://github.com/lisek75/synthdatagen_app.git
-cd synthdatagen_app
-```
-
-**2. Install Dependencies**  
-
-```bash
-conda env create -f synthdatagen_env.yml 
-conda activate synthdatagen
-```
-**3. Configure API Keys & Endpoints**
-
-Create `.env` file with the following variables:
-```python
-OPENAI_API_KEY = your_openai_api_key
-ANTHROPIC_API_KEY = your_anthropic_api_key
-```
-Ensure that the `.env` file remains **secure** and is not shared publicly.
-
-
-## 🚀 Running the Gradio App  
-
-**Run the Application Locally**  
-```bash
-python app.py
-```
-
-**Run the Application with Docker**  
-
-To run the app using Docker, you can either build the image yourself or use the pre-built image from Docker Hub.
-
-- Build and run the app locally:
-Build the image from the provided Dockerfile using your own Docker Hub username:
-```bash
-docker build -t <user-dockerhub-username>/synthdatagen:v1.0 .
-docker run -d --name synthdatagen-container -p 7860:7860 --env-file .env <user-dockerhub-username>/synthdatagen:v1.0
-```
-This will build the Docker image and run the app in a container.
-
-- Run the app directly from Docker Hub:
-Pull the pre-built image from the Docker Hub repository (⚠️make sure to use the latest version tag from Docker Hub).
-Check: https://hub.docker.com/r/lizk75/synthdatagen/tags 
-
-```bash
-docker pull lizk75/synthdatagen:v1.0
-docker run -d --name synthdatagen-container -p 7860:7860 --env-file .env lizk75/synthdatagen:v1.0
-```
-
-
-## 🧑‍💻 Usage Guide  
-- You can launch the app directly from:
-    - The **demo link** provided at the top of this README.
-    - Or by executing it **locally** using the command `python app.py` from Visual Studio or any other IDE.
-- **Describe your dataset** by entering a clear business problem or topic.
-- Select the **dataset type** and **output format**.
-- Choose an **AI model** (GPT or Claude).
-- Set the desired **number of samples**.
-- Click **Create Dataset** and download the generated file.
-
-
-## 📓 Google Colab
-A **notebook version** is available for users who prefer running the app in a notebook environment. The notebook includes additional **open-source models ** that require a **GPU**, which is why it's recommended to run it on Google Colab or a local machine with GPU support.
-
-https://github.com/lisek75/nlp_llms_notebook/blob/main/07_data_generator.ipynb
-
+---
+title: SynthDataGen
+emoji: 🧬
+colorFrom: indigo
+colorTo: pink
+sdk: docker
+app_file: Dockerfile
+pinned: false
+---