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Browse files- app.py +57 -0
- requirements.txt +2 -0
app.py
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import streamlit as st
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import torch
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from transformers import BartForConditionalGeneration, BartTokenizer
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# initialize model + tok variables
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model = None
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tok = None
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# Examples for each models
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examples = [
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["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."],
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["interview-length-tagged","Today's weather was really nice."],
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["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."]
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]
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# Descriptions for each models
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# descriptions = "Interview question remake is a model that..."
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# pass in Strings of model choice and input text for context
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def genQuestion(model_choice, context):
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# global descriptions
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if model_choice=="interview-question-remake":
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model = BartForConditionalGeneration.from_pretrained("hyechanjun/interview-question-remake")
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tok = BartTokenizer.from_pretrained("hyechanjun/interview-question-remake")
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# descriptions = "Interview question remake is a model that..."
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elif model_choice=="interview-length-tagged":
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model = BartForConditionalGeneration.from_pretrained("hyechanjun/interview-length-tagged")
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tok = BartTokenizer.from_pretrained("hyechanjun/interview-length-tagged")
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# descriptions = "Interview question tagged is a model that..."
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elif model_choice=="reverse-interview-question":
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model = BartForConditionalGeneration.from_pretrained("hyechanjun/reverse-interview-question")
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tok = BartTokenizer.from_pretrained("hyechanjun/reverse-interview-question")
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# descriptions = "Reverse interview question is a model that..."
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inputs = tok(context, return_tensors="pt")
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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)
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final_output = ''
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for i in range(4):
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final_output += [tok.decode(beam, skip_special_tokens=True, clean_up_tokenization_spaces=False) for beam in output][i] + "\n"
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return final_output
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# Title
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st.title("Interview AI Test Website")
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# Input field
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input = st.text_input('Context')
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option = st.selectbox(
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'Please select a model.',
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('interview-question-remake', 'interview-length-tagged', 'reverse-interview-question'))
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if st.button('Submit'):
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genQuestion(option, input)
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requirements.txt
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@@ -0,0 +1,2 @@
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transformers
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torch
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