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()