app.py

import gradio as gr
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

iface = gr.Interface(fn=genQuestion, inputs=[gr.inputs.Dropdown(["interview-question-remake", "interview-length-tagged", "reverse-interview-question"]), "text"], examples=examples, outputs="text")
iface.launch()