codeparrot/code-explainer

1import gradio as gr2from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed, pipeline3 4 5title = "Code Explainer"6description = "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),\7            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."8 9EXAMPLE_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]"10EXAMPLE_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)"11EXAMPLE_3 = "def load_text(file)\n    with open(filename, 'r') as f:\n        text = f.read()\n    return text"12example = [13    [EXAMPLE_1, 32, 0.6, 42],14    [EXAMPLE_2, 16, 0.6, 42],15    [EXAMPLE_3, 11, 0.2, 42],16    ]17 18# change model to the finetuned one19tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small-code-to-text")20model = AutoModelForCausalLM.from_pretrained("codeparrot/codeparrot-small-code-to-text")21 22def make_doctring(gen_prompt):23    return gen_prompt + f"\n\n\"\"\"\nExplanation:"24 25def code_generation(gen_prompt, max_tokens, temperature=0.6, seed=42):26    set_seed(seed)27    pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)28    prompt = make_doctring(gen_prompt)29    generated_text = pipe(prompt, do_sample=True, top_p=0.95, temperature=temperature, max_new_tokens=max_tokens)[0]['generated_text']30    return generated_text31 32 33iface = gr.Interface(34    fn=code_generation, 35    inputs=[36        gr.Code(lines=10, label="Python code"),37        gr.inputs.Slider(38            minimum=8,39            maximum=256,40            step=1,41            default=8,42            label="Number of tokens to generate",43        ),44        gr.inputs.Slider(45            minimum=0,46            maximum=2.5,47            step=0.1,48            default=0.6,49            label="Temperature",50        ),51        gr.inputs.Slider(52            minimum=0,53            maximum=1000,54            step=1,55            default=42,56            label="Random seed to use for the generation"57        )58    ],59    outputs=gr.Code(label="Predicted explanation", lines=10),60    examples=example,61    layout="horizontal",62    theme="peach",63    description=description,64    title=title65)66iface.launch()67