| import gradio as gr |
| import torch |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| import spaces |
|
|
| |
| model_name = "Qwen/Qwen2.5-Coder-1.5B-Instruct" |
|
|
| def load_model(): |
| model = AutoModelForCausalLM.from_pretrained( |
| model_name, |
| torch_dtype=torch.float16, |
| device_map="auto", |
| low_cpu_mem_usage=True |
| ) |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| return model, tokenizer |
|
|
| model, tokenizer = load_model() |
|
|
| @spaces.GPU(duration=60) |
| def fix_code(input_code): |
| messages = [ |
| {"role": "system", "content": "You are a helpful coding assistant. Please analyze the following code, identify any errors, and provide the corrected version."}, |
| {"role": "user", "content": f"Please fix this code:\n\n{input_code}"} |
| ] |
| |
| text = tokenizer.apply_chat_template( |
| messages, |
| tokenize=False, |
| add_generation_prompt=True |
| ) |
| |
| model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
| generated_ids = model.generate( |
| **model_inputs, |
| max_new_tokens=1024, |
| temperature=0.7, |
| top_p=0.95, |
| ) |
| |
| generated_ids = [ |
| output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
| ] |
| response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
| |
| return response |
|
|
| iface = gr.Interface( |
| fn=fix_code, |
| inputs=gr.Code( |
| label="Input Code", |
| language="python", |
| lines=10 |
| ), |
| outputs=gr.Code( |
| label="Corrected Code", |
| language="python", |
| lines=10 |
| ), |
| title="Code Correction Tool", |
| description="Enter your code with errors, and the AI will attempt to fix it.", |
| examples=[ |
| ["def fibonacci(n):\n if n = 0:\n return 0\n elif n == 1\n return 1\n else:\n return fibonacci(n-1) + fibonacci(n-2)"], |
| ["for i in range(10)\n print(i"] |
| ] |
| ) |
|
|
| if __name__ == "__main__": |
| iface.launch() |