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app.py

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+import gradio as gr
+import os 
+import numpy as np 
+import librosa
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+from transformers import AutoFeatureExtractor
+from sklearnex import patch_sklearn, unpatch_sklearn
+patch_sklearn()
+import xgboost as xgb
+import keras_nlp
+preprocessor = keras_nlp.models.GemmaPreprocessor.from_preset(
+    "gemma_2b_en", sequence_length=64
+)
+model = keras_nlp.models.GemmaBackbone.from_preset("gemma_2b_en")
+
+
+def greet(name):
+  x = preprocessor(name)
+  lab = model(x)
+  return lab
+
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()

requirements.txt

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+keras-nlp
+tensorflow
+sklearnex
+keras