import gradio as gr from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed, pipeline title = "🎅 Santa Explains Code" description = "This space converts Python code into English text that explains its function using [SantaCoder-Code-To-Text](https://huggingface.co/loubnabnl/santacoder-code-to-text), \ a code generation model that was fine-tuned on the [github-jupyter-code-to-text](https://huggingface.co/datasets/codeparrot/github-jupyter-code-to-text) dataset. \ This dataset includes Python code accompanied by docstrings that explain it. The data was sourced from Jupyter notebooks.\n\n\ Limitations: The model was fine-tuned on a small dataset from Jupyter Notebooks, so it can only explain simple, \ common functions that are found in these notebooks, in a similar fashion to the text in markdown cells. It might also be sensitive to function names and comments." EXAMPLE_0 = "def function(sequence):\n return [x for x in sequence if x % 2 == 0]" EXAMPLE_1 = "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_2 = "def load_text(file)\n with open(filename, 'r') as f:\n text = f.read()\n return text" EXAMPLE_3 = "net.zero_grad()\nloss.backward()" EXAMPLE_4 = "net.zero_grad()\nloss.backward()\n\nnoptimizer.step()" EXAMPLE_5 = "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 = [ [EXAMPLE_0, 32, 0.6, 42], [EXAMPLE_1, 34, 0.4, 42], [EXAMPLE_2, 11, 0.6, 42], [EXAMPLE_3, 30, 0.6, 42], [EXAMPLE_4, 46, 0.6, 42], [EXAMPLE_5, 32, 0.6, 42], ] tokenizer = AutoTokenizer.from_pretrained("loubnabnl/santacoder-code-to-text") model = AutoModelForCausalLM.from_pretrained("loubnabnl/santacoder-code-to-text", trust_remote_code=True) pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) 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) 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.Textbox(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.Textbox(label="Predicted explanation", lines=10), examples=example, layout="horizontal", description=description, title=title ) iface.launch()