README.md

---
title: Code Generation with CodeT5
emoji: 😻
colorFrom: yellow
colorTo: green
sdk: gradio
sdk_version: 5.27.0
app_file: app.py
pinned: false
license: mit
hf_oauth: true
hf_oauth_scopes:
  - inference-api
short_description: 'Leverage CodeT5-base for code generation tasks.'
model_info:
  model_name: Salesforce/codet5-base
  model_type: Encoder-Decoder Transformer
  architecture: T5-based
  pretraining_tasks:
    - Denoising
    - Bimodal Dual Generation
  training_data:
    - CodeSearchNet
    - CodeXGLUE
  fine_tuning_tasks:
    - Code Summarization
    - Code Generation
    - Code Translation
  performance_benchmarks:
    - CodeXGLUE
  paper: 'CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation'
  publication_date: '2021-09-02'
  arxiv_url: 'https://arxiv.org/abs/2109.00859'
  github_url: 'https://github.com/salesforce/CodeT5'
  huggingface_url: 'https://huggingface.co/Salesforce/codet5-base'
---

# 🚀 Code Generation with CodeT5

Welcome to the **Code Generation with CodeT5** project! This repository demonstrates how to leverage the `Salesforce/codet5-base` model for generating Python code snippets based on textual prompts. The project utilizes Gradio for creating interactive web interfaces and is deployed on Hugging Face Spaces.

## 📚 Repository Contents

- **Model Configuration:**  
  Stored in `config.json`, this file defines the architecture and settings of the CodeT5 model.

- **Tokenizer Special Tokens:**  
  Located in `special_tokens_map.json`, it maps special tokens used during tokenization.

- **Training Hyperparameters:**  
  Found in `training_args.json`, this file contains parameters like learning rate, batch size, and number of epochs used during training.

- **Inference Code:**  
  The `app.py` script loads the model and provides an interface for code generation.

- **Dependencies:**  
  Listed in `requirements.txt`, these are the necessary packages for running the model.

- **Documentation:**  
  This `README.md` provides an overview and guide for setting up and using the repository.

## 🔧 Setup & Usage

### 1. Clone the Repository

Clone the repository to your local machine:

```bash
git clone https://github.com/your-username/codegen-model-repo.git
cd codegen-model-repo
```

### 2. Install Dependencies

Install the required packages using pip:

```bash
pip install -r requirements.txt
```

### 3. Run the Gradio App

Launch the Gradio app to start generating code:

```bash
streamlit run app.py
```

Access the app in your browser to input prompts and receive generated code snippets.

## 🌐 Deploying on Hugging Face Spaces

To deploy your Gradio app on Hugging Face Spaces:

1. **Create a New Space:**

   - Visit [Hugging Face Spaces](https://huggingface.co/spaces) and create a new Space.
   - Select Gradio as the SDK.

2. **Push Your Code:**

   - Initialize a Git repository in your project directory.
   - Commit your code and push it to the new Space's repository.

For a detailed walkthrough on deploying Gradio apps to Hugging Face Spaces, refer to this [tutorial](https://pyimagesearch.com/2024/12/30/deploy-gradio-apps-on-hugging-face-spaces/).

## 📄 License

This project is licensed under the MIT License.