app.py

import gradio as gr
from transformers import T5ForConditionalGeneration, AutoTokenizer, TrainingArguments

tokenizer = AutoTokenizer.from_pretrained('evanthebouncy/cad-llm')
model = T5ForConditionalGeneration.from_pretrained('evanthebouncy/cad-llm')

temp = 1.0

def generate_samples_with_temp(txt_n):
    txt, n = txt_n.split('|')
    n_samples = int(n)
    to_tokenizer = [txt for i in range(n_samples)]
    outputs = model.generate(tokenizer(to_tokenizer, return_tensors='pt', padding=True).input_ids, do_sample=True, max_length=128, temperature = temp)
    results = tokenizer.batch_decode(outputs, skip_special_tokens=True)
    return '\n'.join(results)


iface = gr.Interface(fn=generate_samples_with_temp, inputs="text", outputs="text")
iface.launch()