app.py

import gradio as gr
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load the model and tokenizer
model_name = "google/flan-t5-large"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

def concatenate_and_generate(text1, text2, reranking, temperature, top_p):
    concatenated_text = text1 + " " + text2
    inputs = tokenizer(concatenated_text, return_tensors="pt")
    
    # Generate the output with specified temperature and top_p
    output = model.generate(
        inputs["input_ids"], 
        do_sample=True, 
        temperature=temperature, 
        top_p=top_p,
        max_length=100
    )
    
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
    return generated_text

# Define Gradio interface
with gr.Blocks(theme="ParityError/Interstellar@0.0.1") as demo:
    gr.Markdown("# TinyStyler Demo")
    gr.Markdown("Style transfer the source text into the target style, given some example texts of the target style. You can adjust re-ranking and top_p to your desire to control the quality of style transfer. A higher re-ranking value will generally result in better results, at slower speed.")

    text1 = gr.Textbox(lines=3, placeholder="Enter the source text to transform into the target style...", label="Source Text")
    text2 = gr.Textbox(lines=5, placeholder="Enter example texts of the target style (one per line)...", label="Example Texts of the Target Style")
    reranking = gr.Slider(1, 10, value=5, step=1, label="Re-ranking")
    temperature = gr.Slider(0.0, 2.0, value=1.0, step=0.1, label="Temperature")
    top_p = gr.Slider(0.0, 1.0, value=1.0, step=0.1, label="Top-P")
    
    output = gr.Markdown(label="Output")
    
    btn = gr.Button("Generate")
    btn.click(concatenate_and_generate, [text1, text2, reranking, temperature, top_p], output)

demo.launch()