app.py

import gradio as gr
from gradio import Interface, Textbox, Markdown,Slider
from transformers import AutoModelForCausalLM , AutoTokenizer
import torch

openelm_270m_instruct = AutoModelForCausalLM.from_pretrained("apple/OpenELM-270M-Instruct", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-hf")

def generate(newQuestion,num):
    tokenized_prompt = tokenizer(newQuestion)
    tokenized_prompt = torch.tensor(
        tokenized_prompt['input_ids'],
    )
    tokenized_prompt = tokenized_prompt.unsqueeze(0)

    # Generate
    output_ids = openelm_270m_instruct.generate(
        tokenized_prompt,
        max_length=int(num),
        pad_token_id=0,
    )
    output_text = tokenizer.decode(
        output_ids[0].tolist(),
        skip_special_tokens=True
    )

    return output_text


developer_info = """
this space is developed by Ahmadreza Anaami \n
feel free to set via Api key too \n
apple/OpenELM-270M-Instruct
"""


def greet(name,num):
    return generate(name,num)

iface = gr.Interface(
    fn=greet,
    inputs=[Textbox(label="Enter Text Here:", type="text"),Textbox(label="number of generated tokens:", type="text")],
    outputs=[Textbox(label="generated answer:")],
    title="OpenELM-270M-Instruct",
    # Markdown(developer_info, elem_id="dev-info"),  # Place Markdown directly
    description = developer_info,
    css="""
        /* Style the developer info section (optional) */
        #dev-info {
            font-size: 0.8rem;
            color: #888;  /* Adjust color as desired */
            margin-top: 1rem;
            text-align: center;
        }
        /* Style the input area (optional) */
        .gr-input text {
            padding: 10px;
            border-radius: 5px;
            font-size: 1rem;
        }
        /* Style the score label (optional) */
        .gr-output.gr-slider label {
            font-weight: bold;
        }
    """,
)

iface.launch()