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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed, pipeline | |
| title = "Code Explainer" | |
| description = "This is a space to convert Python code into english text explaining what it does using [codeparrot-small-code-to-text](https://huggingface.co/codeparrot/codeparrot-small-code-to-text),\ | |
| a code generation model for Python finetuned on [github-jupyter-code-to-text](https://huggingface.co/datasets/codeparrot/github-jupyter-code-to-text) a dataset of Python code followed by a docstring explaining it, the data was originally extracted from Jupyter notebooks." | |
| EXAMPLE_1 = "def sort_function(arr):\n n = len(arr)\n \n # Traverse through all array elements\n for i in range(n):\n \n # Last i elements are already in place\n for j in range(0, n-i-1):\n \n # traverse the array from 0 to n-i-1\n # Swap if the element found is greater\n # than the next element\n if arr[j] > arr[j+1]:\n arr[j], arr[j+1] = arr[j+1], arr[j]" | |
| EXAMPLE_2 = "from sklearn import model_selection\nX_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size=0.2)" | |
| EXAMPLE_3 = "def load_text(file)\n with open(filename, 'r') as f:\n text = f.read()\n return text" | |
| example = [ | |
| [EXAMPLE_1, 32, 0.6, 42], | |
| [EXAMPLE_2, 16, 0.6, 42], | |
| [EXAMPLE_3, 11, 0.2, 42], | |
| ] | |
| # change model to the finetuned one | |
| tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small-code-to-text") | |
| model = AutoModelForCausalLM.from_pretrained("codeparrot/codeparrot-small-code-to-text") | |
| def make_doctring(gen_prompt): | |
| return gen_prompt + f"\n\n\"\"\"\nExplanation:" | |
| def code_generation(gen_prompt, max_tokens, temperature=0.6, seed=42): | |
| set_seed(seed) | |
| pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) | |
| prompt = make_doctring(gen_prompt) | |
| generated_text = pipe(prompt, do_sample=True, top_p=0.95, temperature=temperature, max_new_tokens=max_tokens)[0]['generated_text'] | |
| return generated_text | |
| iface = gr.Interface( | |
| fn=code_generation, | |
| inputs=[ | |
| gr.Code(lines=10, label="Python code"), | |
| gr.inputs.Slider( | |
| minimum=8, | |
| maximum=256, | |
| step=1, | |
| default=8, | |
| label="Number of tokens to generate", | |
| ), | |
| gr.inputs.Slider( | |
| minimum=0, | |
| maximum=2.5, | |
| step=0.1, | |
| default=0.6, | |
| label="Temperature", | |
| ), | |
| gr.inputs.Slider( | |
| minimum=0, | |
| maximum=1000, | |
| step=1, | |
| default=42, | |
| label="Random seed to use for the generation" | |
| ) | |
| ], | |
| outputs=gr.Code(label="Predicted explanation", lines=10), | |
| examples=example, | |
| layout="horizontal", | |
| theme="peach", | |
| description=description, | |
| title=title | |
| ) | |
| iface.launch() | |