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import streamlit as st
import torch
from pandas import options
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
@st.cache
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\n"
return final_output
# Wide page layout (instead of having a narrower, one-column page layout)
st.set_page_config(layout="wide")
# Title
st.title("Interview AI Test Website")
# Adding a Session State to store stateful variables and for saving user's labels/tags for generated questions
if 'button_sent' not in st.session_state:
st.session_state.button_sent = False
# Input fields
input = st.text_input('Context') # user inputs context to construct a response (str)
maxl, minl = st.columns(2)
option = st.selectbox(
'Please select a model.',
('interview-question-remake', 'interview-length-tagged', 'reverse-interview-question'))
if option == 'interview-question-remake':
st.write("This is the re-fine-tuned base model for our interview AI. It returns strings terminating in a question mark (?).")
elif option == 'interview-length-tagged':
st.write("This is a length-tagged version of our interview AI. You can specify how long its responses should be (ranges of multiples of 10)")
elif option == 'reverse-interview-question':
st.write("This model asks a question that would have resulted in the context you provide (a.k.a. it traverses backward through the interview)")
# Column layout to display generated responses alongside tags
col1, col2 = st.columns((3, 1))
if st.button('Submit') or st.session_state.button_sent:
with st.spinner('Generating a response...'):
output = genQuestion(option, input)
print(output)
# st.write(output)
st.session_state.button_sent = True
col1.text_area(label="Generated Responses:", value=output, disabled=True, height=200)
# TODO:
# - disable multiselect widget when responses are being generated AND when a question is not selected to be tagged
# - connect tags with an individual question
# - save session state so tags associated with their respective questions can also be saved
# - write/store the saved state data to some database for future use?
# - brainstorm good names for tags/labels OR allow users to enter their own tag names if possible
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