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import keras_nlp
from keras_nlp.models import GemmaCausalLM
import warnings
warnings.filterwarnings('ignore')
import os
#set the envirenment
os.environ["KERAS_BACKEND"] = "jax" # Or "torch" or "tensorflow".
os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"]="1.00"
# Load your Hugging Face model and tokenizer
model_name = "soufyane/gemma_data_science"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = keras_nlp.models.CausalLM.from_preset(f"hf://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()
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