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from transformers import T5Tokenizer, T5ForConditionalGeneration
import gradio as gr
# model_name = "google/flan-t5-small"
# model_name = "google/flan-t5-base"
model_name = "google/flan-t5-large"
# model_name = "google/flan-t5-xl"
default_max_length = 200
print("Using `{}`.".format(model_name))
tokenizer = T5Tokenizer.from_pretrained(model_name)
print("T5Tokenizer loaded from pretrained.")
model = T5ForConditionalGeneration.from_pretrained(model_name, device_map="auto")
print("T5ForConditionalGeneration loaded from pretrained.")
def inference(max_length, input_text, history=[]):
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids, max_length=max_length, bos_token_id=0)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
history.append((input_text, result))
return history, history
with gr.Blocks() as demo:
with gr.Row():
gr.Markdown(
"<h1>Demo of {}</h1><p>See more at Hugging Face: <a href='https://huggingface.co/{}'>{}</a>.</p>".format(
model_name, model_name, model_name
)
)
max_length = gr.Number(
value=default_max_length, label="maximum length of response"
)
chatbot = gr.Chatbot(label=model_name)
state = gr.State([])
with gr.Row():
txt = gr.Textbox(
show_label=False, placeholder="Enter text and press enter"
).style(container=False)
txt.submit(fn=inference, inputs=[max_length, txt, state], outputs=[chatbot, state])
demo.launch()
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