Update app.py

app.py

@@ -1,39 +1,45 @@
 import streamlit as st
-from streamlit_chat import message as st_message
-from transformers import BlenderbotTokenizer
-from transformers import BlenderbotForConditionalGeneration
-
-
-@st.experimental_singleton
-def get_models():
-    # it may be necessary for other frameworks to cache the model
-    # seems pytorch keeps an internal state of the conversation
-    model_name = "facebook/blenderbot-400M-distill"
-    tokenizer = BlenderbotTokenizer.from_pretrained(model_name)
-    model = BlenderbotForConditionalGeneration.from_pretrained(model_name)
-    return tokenizer, model
-
-
-if "history" not in st.session_state:
-    st.session_state.history = []
-
-st.title("Hello Chatbot")
-
-
-def generate_answer():
-    tokenizer, model = get_models()
-    user_message = st.session_state.input_text
-    inputs = tokenizer(st.session_state.input_text, return_tensors="pt")
-    result = model.generate(**inputs)
-    message_bot = tokenizer.decode(
-        result[0], skip_special_tokens=True
-    )  # .replace("<s>", "").replace("</s>", "")
-
-    st.session_state.history.append({"message": user_message, "is_user": True})
-    st.session_state.history.append({"message": message_bot, "is_user": False})
-
-
-st.text_input("Talk to the bot", key="input_text", on_change=generate_answer)
-
-for chat in st.session_state.history:
-    st_message(**chat)  # unpacking
+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()