app.py

import gradio as gr
import numpy as np
import pandas as pd
from transformers import pipeline
import torch

model = "GeneZC/MiniChat-2-3B"
generator=pipeline(task='text-generation', model=model)


tones = {
    'natural': 'human, authentic',
    'fluency': 'readable, clarified',
    'formal': 'sophistocated',
    'academic': 'technical and scholarly',
    'simple': 'simple and easily understandable',
}


def generate(text, max_length):
    x=generator(text, max_length=max_length, num_return_sequences=1)
    return x


def respond(message, history, tone="natural", max_length=512):
    prompt = f"<s> [|User|]Paraphrase this text in a more {tones[tone]} way: {message} </s>[|Assistant|]"
    text = generate(prompt, max_length)
    print(text)
    text = text[0]["generated_text"]
    text = text.split("[|Assistant|]", 1)[1]
    return text
        

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Dropdown(
            ["natural", "fluency", "formal", "academic", "simple"], label="Tone", value="natural"
        ),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
    ],
)


if __name__ == "__main__":
    demo.launch()