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import transformers
import gradio as gr
import git
#Load arabert preprocessor
import git
git.Git("arabert").clone("https://github.com/aub-mind/arabert")
from arabert.preprocess import ArabertPreprocessor
arabert_prep = ArabertPreprocessor(model_name="bert-base-arabert", keep_emojis=False)
#Load Model
from transformers import EncoderDecoderModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("tareknaous/bert2bert-empathetic-response-msa")
model = EncoderDecoderModel.from_pretrained("tareknaous/bert2bert-empathetic-response-msa")
model.eval()
def generate_response(text, minimum_length, k, p, temperature):
text_clean = arabert_prep.preprocess(text)
inputs = tokenizer.encode_plus(text_clean,return_tensors='pt')
outputs = model.generate(input_ids = inputs.input_ids,
attention_mask = inputs.attention_mask,
do_sample = True,
min_length=minimum_length,
top_k = k,
top_p = p,
temperature = temperature)
preds = tokenizer.batch_decode(outputs)
response = str(preds)
response = response.replace("\'", '')
response = response.replace("[[CLS]", '')
response = response.replace("[SEP]]", '')
response = str(arabert_prep.desegment(response))
return response
gr.Interface(fn=generate_response,
inputs=[
gr.inputs.Textbox(),
gr.inputs.Slider(5, 20, step=1, label='Minimum Output Length'),
gr.inputs.Slider(0, 1000, step=10, label='Top-K'),
gr.inputs.Slider(0, 1, step=0.1, label='Top-P'),
gr.inputs.Slider(0, 3, step=0.1, label='Temperature'),
],
outputs="text").launch()
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