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import torch
from transformers import AutoTokenizer, AutoModel, BloomTokenizerFast,BloomForCausalLM
import gradio as gr
modelo = 'bigscience/bloom-560m'
tokenizer = AutoTokenizer.from_pretrained(modelo)
model = BloomForCausalLM.from_pretrained(modelo)
def generator(prompt,max_length, temp):
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
gen_tokens = model.generate(
input_ids,
do_sample=True,
temperature=temp,
max_length=max_length,
)
gen_text = tokenizer.batch_decode(gen_tokens)[0]
return gen_text
def run(prompt, max_len, temp):
min_len = 1
output = generator(prompt,max_len, temp)
print(output)
return (output,"")
if __name__ == "__main__":
demo = gr.Blocks()
with demo:
gr.Markdown(modelo)
with gr.Row():
with gr.Column():
text = gr.Textbox(
label="Input",
value=" ", # should be set to " " when plugged into a real API
)
tokens = gr.Slider(1, 250, value=50, step=1, label="Tokens to generate")
temp = gr.Slider(0.1, 1.0, value=0.7, step=0.1, label="Temperature")
with gr.Row():
submit = gr.Button("Submit")
with gr.Column():
text_error = gr.Markdown(label="Log information")
text_out = gr.Textbox(label="Output")
submit.click(
run,
inputs=[text, tokens, temp],
outputs=[text_out, text_error],
)
demo.launch() |