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import transformers
import gradio as gr
import git
import os
os.system("pip install --upgrade pip")
#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, 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_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
# title = 'Empathetic Response Generation in Arabic'
# description = 'This demo is for a BERT2BERT model trained for single-turn open-domain empathetic dialogue response generation in Modern Standard Arabic'
css = """
.rtlClass {direction:rtl !important}
"""
with gr.Blocks(css=css) as demo:
with gr.Column():
gr.Markdown("Empathetic Response Generation in Arabic")
chatbot = gr.Chatbot(elem_classes="rtlClass").style(height=400)
msg = gr.Textbox(placeholder="Ψ§Ψ±Ψ³Ω Ψ±Ψ³Ψ§ΩΨ©",show_label=False,elem_classes="rtlClass").style(container=False)
with gr.Column():
output_slider=gr.Slider(5, 20, step=1, label='Minimum Output Length')
top_p_slider=gr.Slider(0.7, 1, step=0.1, label='Top-P')
temperature_slider=gr.Slider(1, 3, step=0.1, label='Temperature')
clear = gr.Button("Clear Chat")
def respond(message,chat_history,output_slider,top_p_slider,temperature_slider):
bot_message = generate_response(message,output_slider,top_p_slider,temperature_slider)
chat_history.append((message, bot_message))
return "", chat_history
msg.submit(respond, [msg, chatbot,output_slider,top_p_slider,temperature_slider], [msg, chatbot])
clear.click(lambda: None, None, chatbot, queue=False)
demo.launch() |