Spaces:
Sleeping
Sleeping
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import gradio as grad | |
| codegen_tkn = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono") | |
| mdl = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-mono") | |
| def codegen(intent): | |
| # give input as text which reflects intent of the program. | |
| #text = " write a function which takes 2 numbers as input and returns the larger of the two" | |
| input_ids = codegen_tkn(intent, return_tensors="pt").input_ids | |
| gen_ids = mdl.generate(input_ids, max_length=128) | |
| response = codegen_tkn.decode(gen_ids[0], skip_special_tokens=True) | |
| return response | |
| output=grad.Textbox(lines=1, label="Generated Python Code", placeholder="") | |
| inp=grad.Textbox(lines=1, label="Place your intent here") | |
| grad.Interface(codegen, inputs=inp, outputs=output).launch() |