Create app.py

app.py

@@ -0,0 +1,44 @@
+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()