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| from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline | |
| import gradio as gr | |
| import tokenize | |
| import io | |
| pipeline = SummarizationPipeline( | |
| model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_large_source_code_summarization_python_multitask_finetune"), | |
| tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_large_source_code_summarization_python_multitask_finetune", skip_special_tokens=True), | |
| device=-1) | |
| def code_summarizer(code): | |
| def code_tokenizer(line): | |
| result= [] | |
| line = io.StringIO(line) | |
| for toktype, tok, start, end, line in tokenize.generate_tokens(line.readline): | |
| if (not toktype == tokenize.COMMENT): | |
| if toktype == tokenize.STRING: | |
| result.append("CODE_STRING") | |
| elif toktype == tokenize.NUMBER: | |
| result.append("CODE_INTEGER") | |
| elif (not tok=="\n") and (not tok==" "): | |
| result.append(str(tok)) | |
| return ' '.join(result) | |
| tokenized_code = code_tokenizer(code) | |
| summary = pipeline(tokenized_code) | |
| return summary[0]['summary_text'] | |
| def call_examples(): | |
| examples = [['''def findAverage(list): sum = 0. for x in list: sum = sum + x average = sum / len(list) return average'''], | |
| ['''def findMax(list): max = list[0] for x in list: if x > max: max = x return max'''], | |
| ['''def findRange(list): return max(list)-min(list)''']] | |
| return examples | |
| gr.Interface(fn=code_summarizer, | |
| inputs=gr.inputs.Textbox( | |
| lines=5, | |
| default='', | |
| placeholder='Insert a Python code here', | |
| label='PYTHON CODE'), | |
| outputs=gr.outputs.Textbox( | |
| type='auto', | |
| label='CODE SUMMARY'), | |
| title='Code Summarizer From CodeTrans', | |
| description='Summarize any Python code', | |
| article='Pretrained model on programming language python using the t5 large model architecture. This model is trained on tokenized python code functions: it works best with tokenized python functions. This CodeTrans model is based on the t5-large model. It has its own SentencePiece vocabulary model. It used multi-task training on 13 supervised tasks in the software development domain and 7 unsupervised datasets. It is then fine-tuned on the source code summarization task for the python code snippets.', | |
| theme='dark-peach', | |
| live=True, | |
| examples=call_examples(), | |
| allow_flagging='never').launch(inbrowser=True) |