import keras_nlp
from keras_nlp.models import GemmaCausalLM
import warnings
import gradio as gr
warnings.filterwarnings('ignore')
import os

from huggingface_hub import from_pretrained_keras

model = from_pretrained_keras("soufyane/gemma_data_science")


def process_text_gemma(input_text):
    response = model.generate(f"question: {input_text}", max_length=256)
    return response


def main(input_text):
    return process_text_gemma(input_text[0])

gr.Interface(
    fn=main,
    inputs=["text"],
    outputs=["text"],
    title="Gemma Data Science Model",
    description="This is a text-to-text model for data science tasks.",
    live=True
).launch()