README.md

"""
# AI FixCode Model 🛠️

A Transformer-based code fixing model trained on diverse buggy → fixed code pairs. Built using [CodeT5](https://huggingface.co/Salesforce/codet5p-220m), this model identifies and corrects syntactic and semantic errors in source code.

## 📌 Model Details
- **Base Model**: `Salesforce/codet5p-220m`
- **Type**: Seq2Seq (Encoder-Decoder)
- **Trained On**: Custom dataset with real-world buggy → fixed examples.
- **Languages**: Python (initially), can be expanded to JS, Go, etc.

## 🔧 Intended Use
Input a buggy function or script and receive a syntactically and semantically corrected version.

**Example**:
```python
# Input:
def add(x, y)
 return x + y

# Output:
def add(x, y):
    return x + y
```

## 🧠 How it Works
The model learns from training examples that map erroneous code to corrected code. It uses token-level sequence generation to predict patches.

## 🚀 Inference
Use `transformers` pipeline or run via CLI:
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("YOUR_USERNAME/aifixcode-model")
tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/aifixcode-model")
input_code = "def foo(x):\n print(x"
inputs = tokenizer(input_code, return_tensors="pt")
out = model.generate(**inputs, max_length=512)
print(tokenizer.decode(out[0], skip_special_tokens=True))
```

## 📂 Dataset Format
```json
[
  {
    "input": "def add(x, y)\n return x + y",
    "output": "def add(x, y):\n    return x + y"
  }
]
```

## 🛡️ License
MIT License

## 🙏 Acknowledgements
Built using 🤗 HuggingFace Transformers + Salesforce CodeT5.
"""