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import streamlit as st
import torch
from transformers import BartForConditionalGeneration, BartTokenizer
# initialize model + tok variables
model = None
tok = None
# Examples for each models
examples = [
["interview-question-remake", "I have a cat named dolche and he's not very friendly with strangers. I've had him for 9 years now and it has been a pleasure to see him grow closer to us every year."],
["interview-length-tagged","Today's weather was really nice."],
["reverse-interview-question", "There are so many incredible musicians out there and so many really compelling big hits this year that it makes for a really interesting way to recap some of those big events."]
]
# Descriptions for each models
# descriptions = "Interview question remake is a model that..."
# pass in Strings of model choice and input text for context
def genQuestion(model_choice, context):
# global descriptions
if model_choice=="interview-question-remake":
model = BartForConditionalGeneration.from_pretrained("hyechanjun/interview-question-remake")
tok = BartTokenizer.from_pretrained("hyechanjun/interview-question-remake")
# descriptions = "Interview question remake is a model that..."
elif model_choice=="interview-length-tagged":
model = BartForConditionalGeneration.from_pretrained("hyechanjun/interview-length-tagged")
tok = BartTokenizer.from_pretrained("hyechanjun/interview-length-tagged")
# descriptions = "Interview question tagged is a model that..."
elif model_choice=="reverse-interview-question":
model = BartForConditionalGeneration.from_pretrained("hyechanjun/reverse-interview-question")
tok = BartTokenizer.from_pretrained("hyechanjun/reverse-interview-question")
# descriptions = "Reverse interview question is a model that..."
inputs = tok(context, return_tensors="pt")
output = model.generate(inputs["input_ids"], num_beams=4, max_length=64, min_length=9, num_return_sequences=4, diversity_penalty =1.0, num_beam_groups=4)
final_output = ''
for i in range(4):
final_output += [tok.decode(beam, skip_special_tokens=True, clean_up_tokenization_spaces=False) for beam in output][i] + "\n"
return final_output
# Title
st.title("Interview AI Test Website")
# Input field
input = st.text_input('Context')
option = st.selectbox(
'Please select a model.',
('interview-question-remake', 'interview-length-tagged', 'reverse-interview-question'))
if st.button('Submit'):
genQuestion(option, input)
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