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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-base")
# Define your models
models = {
"Luc Bat": AutoModelForCausalLM.from_pretrained(
"Libosa2707/vietnamese-poem-luc-bat-gpt2"
),
"Bay Chu": AutoModelForCausalLM.from_pretrained(
"Libosa2707/vietnamese-poem-bay-chu-gpt2"
),
"Tam Chu": AutoModelForCausalLM.from_pretrained(
"Libosa2707/vietnamese-poem-tam-chu-gpt2"
),
"Nam Chu": AutoModelForCausalLM.from_pretrained(
"Libosa2707/vietnamese-poem-nam-chu-gpt2"
),
}
def generate_poem(text, style):
# Choose the model based on the selected style
model = models[style]
# Tokenize the input line
input_ids = tokenizer.encode(text, return_tensors="pt")
# Generate text
output = model.generate(input_ids, max_length=100, do_sample=True, temperature=0.7)
# Decode the output
generated_text = tokenizer.decode(
output[:, input_ids.shape[-1] :][0], skip_special_tokens=True
)
text = text + generated_text
# Post-process the output
text = text.replace("<unk>", "\n")
pretty_text = ""
for idx, line in enumerate(text.split("\n")):
line = line.strip()
if not line:
continue
line = line[0].upper() + line[1:]
pretty_text += line + "\n"
return pretty_text
gradio_interface = gr.Interface(
fn=generate_poem,
inputs=[
gr.inputs.Textbox(lines=1, placeholder="First words of the poem"),
gr.inputs.Dropdown(
choices=["Luc Bat", "Bay Chu", "Tam Chu", "Nam Chu"], label="Style"
),
],
outputs="text",
)
gradio_interface.launch()
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