qg_generation / app.py
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import numpy as np
import requests
import streamlit as st
import openai
def main():
st.title("Scientific Question Generation")
st.write("This application is designed to generate a question given a piece of scientific text.\
We include the output from four different models, the [BART-Large](https://huggingface.co/dhmeltzer/bart-large_askscience-qg) and [FLAN-T5-Base](https://huggingface.co/dhmeltzer/flan-t5-base_askscience-qg) models \
fine-tuned on the r/AskScience split of the [ELI5 dataset](https://huggingface.co/datasets/eli5) as well as the zero-shot output \
of the [FLAN-T5-XXL](https://huggingface.co/google/flan-t5-xxl) model and the [GPT-3.5-turbo](https://platform.openai.com/docs/models/gpt-3-5) model.\
\n \
For a more thorough discussion of question generation see this [report](https://wandb.ai/dmeltzer/Question_Generation/reports/Exploratory-Data-Analysis-for-r-AskScience--Vmlldzo0MjQwODg1?accessToken=fndbu2ar26mlbzqdphvb819847qqth2bxyi4hqhugbnv97607mj01qc7ed35v6w8) on EDA and this \
[report](https://api.wandb.ai/links/dmeltzer/7an677es) on our training procedure.\
\n \
**Disclaimer**: You may recieve an error message when you first run the model. We are using the Huggingface API to access the BART-Large and FLAN-T5 models, and the inference API takes around 20 seconds to load the model.\
In addition, the FLAN-T5-XXL model was recently updated on Huggingface and may give buggy outputs.\
")
checkpoints = ['dhmeltzer/bart-large_askscience-qg',
'dhmeltzer/flan-t5-base_askscience-qg',
'google/flan-t5-xxl']
headers = {"Authorization": f"Bearer {st.secrets['HF_token']}"}
openai.api_key = st.secrets['OpenAI_token']
def query(checkpoint, payload):
API_URL = f"https://api-inference.huggingface.co/models/{checkpoint}"
response = requests.post(API_URL,
headers=headers,
json=payload)
return response.json()
# User search
user_input = st.text_area("Question Generator",
"""Black holes are the most gravitationally dense objects in the universe.""")
if user_input:
for checkpoint in checkpoints:
model_name = checkpoint.split('/')[1]
if 'flan' in model_name.lower():
prompt = 'generate a question: ' + user_input
else:
prompt = user_input
output = query(checkpoint,{
"inputs": prompt,
"wait_for_model":True})
try:
output=output[0]['generated_text']
except:
st.write(output)
return
st.write(f'Model {model_name}: {output}')
model_engine = "gpt-3.5-turbo"
max_tokens = 50
prompt = f"generate a question: {user_input}"
response=openai.ChatCompletion.create(
model=model_engine,
messages=[
{"role": "system", "content": "You are a helpful assistant that generates questions from text."},
{"role": "user", "content": prompt},
])
output = response['choices'][0]['message']['content']
st.write(f'Model {model_engine}: {output}')
if __name__ == "__main__":
main()
#[0]['generated_text']