---
language: 
  - code
library_name: transformers
tags:
  - code-generation
  - code-repair
  - auto-fix
  - programming
  - huggingface
  - gradio
  - fastapi
datasets:
  - custom
model-index:
  - name: AutoCodeFix-AI
    results: []
pretty_name: AutoCodeFix AI
pipeline_tag: text-generation
---


# 🧠 AutoCodeFix-AI: Transformer-Powered Automatic Code Repair

**AutoCodeFix-AI** is an advanced yet lightweight AI system that automatically **detects, explains, and fixes code errors** in Python and C-style languages using transformer-based models such as **StarCoder2** and **CodeT5+**.  

It provides:
- ⚙️ A **FastAPI backend** for scalable inference  
- 🎛️ A **Gradio web demo** for live interaction and testing  
- 📚 Notebooks for model finetuning and evaluation  

---

### 🧩 Key Features

✅ Automated code error detection and correction  
✅ Natural-language explanations of fixes  
✅ Transformer-based fine-tuning (StarCoder2 / CodeT5+)  
✅ Ready-to-use REST API (`FastAPI`)  
✅ Interactive Hugging Face demo via `Gradio`  
✅ Modular and lightweight (~40 MB project)  

---

### 📁 Project Structure
```
autocodefix-ai/
│
├── README.md
├── requirements.txt
├── setup.py
├── LICENSE
│
├── data/
│   ├── raw/
│   ├── processed/
│   └── samples/
│
├── notebooks/
│   ├── 01_data_exploration.ipynb
│   ├── 02_preprocessing.ipynb
│   ├── 03_model_finetuning.ipynb
│   ├── 04_evaluation.ipynb
│   └── 05_inference_demo.ipynb
│
├── src/
│   ├── config/
│   │   ├── model_config.yaml
│   │   └── training_args.yaml
│   ├── data/
│   │   ├── data_loader.py
│   │   └── preprocessing.py
│   ├── model/
│   │   ├── model_builder.py
│   │   ├── train_model.py
│   │   ├── evaluate_model.py
│   │   └── inference.py
│   ├── utils/
│   │   ├── logger.py
│   │   ├── metrics.py
│   │   └── helper.py
│   └── app/
│       ├── main.py              # FastAPI backend
│       └── gradio_demo.py       # Local Gradio UI
│
├── deployment/
│   ├── Dockerfile
│   ├── api_service.py
│   ├── gradio_service.py
│   └── huggingface_spaces/
│       └── app.py
│
└── tests/
    ├── test_data.py
    ├── test_model.py
    ├── test_inference.py
    └── test_api.py
```

---

### ⚙️ Installation

```bash
git clone https://huggingface.co/spaces/hmnshudhmn24/autocodefix-ai
cd autocodefix-ai
pip install -r requirements.txt
```

---

### 🧠 Model Support

| Model | Task | Source |
|-------|------|--------|
| 🪐 **StarCoder2** | Code completion & repair | [Hugging Face](https://huggingface.co/bigcode/starcoder2) |
| ⚙️ **CodeT5+** | Code understanding & generation | [Hugging Face](https://huggingface.co/Salesforce/codet5p) |

Models and configurations are defined in:
```
src/config/model_config.yaml
src/config/training_args.yaml
```

---

### 🧩 Usage

#### 🔹 Run the FastAPI Backend
```bash
uvicorn src.app.main:app --reload
```
Access it at: [http://localhost:8000/docs](http://localhost:8000/docs)

#### 🔹 Launch Gradio Web Demo
```bash
python deployment/huggingface_spaces/app.py
```
Then open the local or Hugging Face demo URL.

---

### 🧪 Example API Request

```bash
curl -X POST "http://localhost:8000/fix" -H "Content-Type: application/json" -d '{"code": "def add(a,b)
 return a+b"}'
```

**Response:**
```json
{
  "fixed_code": "def add(a, b):
    return a + b",
  "explanation": "Added missing colon and indentation."
}
```

---

### 📊 Training & Evaluation

1. **Data Exploration:** `notebooks/01_data_exploration.ipynb`  
2. **Preprocessing:** `notebooks/02_preprocessing.ipynb`  
3. **Model Finetuning:** `notebooks/03_model_finetuning.ipynb`  
4. **Evaluation:** `notebooks/04_evaluation.ipynb`  
5. **Inference Demo:** `notebooks/05_inference_demo.ipynb`

All experiment outputs are stored under:
```
experiments/logs/
experiments/checkpoints/
experiments/results/
```