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| import torch | |
| import transformers | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| import gradio as gr | |
| tokenizer = AutoTokenizer.from_pretrained("AhmedSSoliman/MarianCG-CoNaLa-Large") | |
| model = AutoModelForSeq2SeqLM.from_pretrained("AhmedSSoliman/MarianCG-CoNaLa-Large") | |
| def generate_code(NL): | |
| inputs = tokenizer(NL, padding="max_length", truncation=True, max_length=512, return_tensors="pt") | |
| input_ids = inputs.input_ids | |
| attention_mask = inputs.attention_mask | |
| outputs = model.generate(input_ids, attention_mask=attention_mask) | |
| output_code = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return output_code | |
| iface = gr.Interface(fn=generate_code, inputs="text", outputs="text", | |
| examples=[["create array containing the maximum value of respective elements of array `[2, 3, 4]` and array `[1, 5, 2]"], | |
| ["check if all elements in list `mylist` are identical"], | |
| ["enable debug mode on flask application `app`"], | |
| ["getting the length of `my_tuple`"], | |
| ['find all files in directory "/mydir" with extension ".txt"']], | |
| title="MarianCG: A Code Generation Transformer Model Inspired by Machine Translation", | |
| description="This is a code generation model which can generate code from the natural language description") | |
| iface.launch() | |
| #iface.launch(share=True) | |
| #output_text = gr.outputs.Textbox() | |
| #gr.Interface(generate_code,"textbox", output_text, title="MarianCG model for Code Generation", description="MarianCG model for Code Generation").launch() |