app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aburnazy/opt-350m-hy"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def generate_text(prompt, temperature, top_k, top_p, max_length):
    inputs = tokenizer.encode(prompt, return_tensors="pt")
    outputs = model.generate(inputs, max_length=max_length, temperature=temperature, top_k=top_k, top_p=top_p, do_sample=True)
    text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return text

iface = gr.Interface(
    fn=generate_text, 
    inputs=[
        gr.inputs.Textbox(lines=2, placeholder="Առավոտ էր: Արարատյան դաշտի լուսապայծառ "),
        gr.inputs.Slider(minimum=0, maximum=1, step=0.01, default=0.8, label='Temperature'),
        gr.inputs.Slider(minimum=0, maximum=100, step=1, default=20, label='Top K'),
        gr.inputs.Slider(minimum=0, maximum=1, step=0.01, default=0.1, label='Top P'),
        gr.inputs.Slider(minimum=10, maximum=1024, step=1, default=200, label='Max Length'),
    ], 
    outputs="text"
)

iface.launch()