app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, set_seed
import random

tokenizer = AutoTokenizer.from_pretrained("breadlicker45/dough-instruct-base-001")
tokenizer.padding_side = 'left'
model = AutoModelForCausalLM.from_pretrained("breadlicker45/dough-instruct-base-001")

def generate_text(prompt):
    set_seed((random.randint(1, 10000)))
    generator = pipeline('text-generation', model=model, tokenizer=tokenizer, do_sample=True)
    text = """Q:
""" + prompt + """
A:"""
    answer = generator(text, max_length=50, min_length=10, temperature=0.8, top_p=0.9, do_sample=True)
    lst = answer[0]['generated_text']
    out = lst.replace(text, '''''')
    return out

input_text = gr.inputs.Textbox(lines=5, label="Input Text")
output_text = gr.outputs.Textbox(label="Generated Text")

gr.Interface(generate_text, input_text, output_text).launch()