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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() |