Update app.py

app.py

@@ -1,35 +1,33 @@
-import gradio as gr
-from gtts import gTTS
-from io import BytesIO
-import IPython.display as ipd
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+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):
-    input_ids = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)["input_ids"]
-    output_ids = model.generate(input_ids)
-    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    response = model.generate(f"question: {input_text}", max_length=256)
     return response
 
-def process_speech_gemma(audio):
-    response = process_text_gemma(audio)
-    tts = gTTS(text=response, lang='en')
-    fp = BytesIO()
-    tts.write_to_fp(fp)
-    fp.seek(0)
-    return ipd.Audio(fp.read(), autoplay=True)
 
 def main(input_text):
-    return process_text_gemma(input_text[0]), process_speech_gemma(input_text[0])
+    return process_text_gemma(input_text[0])
 
 gr.Interface(
     fn=main,
     inputs=["text"],
-    outputs=["text", "audio"],
+    outputs=["text"],
     title="Gemma Data Science Model",
     description="This is a text-to-text model for data science tasks.",
     live=True