app.py

import numpy as np
import requests
import streamlit as st
import openai
import json

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\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) for EDA on the r/AskScience dataset and this \
    [report](https://api.wandb.ai/links/dmeltzer/7an677es) for details on our training procedure.\
    \n\nThe two fine-tuned models (BART-Large and FLAN-T5-Base) are hosted on AWS using a combination of AWS Sagemaker, Lambda, and API gateway.\
    GPT-3.5 is called using the OpenAI API and the FLAN-T5-XXL model is hosted by HuggingFace and is called with their Inference API.\
    \n \n **Disclaimer**: When first running this application it may take approximately 30 seconds for the first two responses to load because of the cold start problem with AWS Lambda.\
    You may recieve also an error message when calling the FLAN-T5-XXL model since the Inference API takes around 20 seconds to load the model.\
    Both issues should disappear on any subsequent calls to the application.")
    
    AWS_checkpoints = {}
    AWS_checkpoints['BART-Large']='https://8hlnvys7bh.execute-api.us-east-1.amazonaws.com/beta/'
    AWS_checkpoints['FLAN-T5-Base']='https://gnrxh05827.execute-api.us-east-1.amazonaws.com/beta/'
    
    # Right now HF_checkpoints just consists of FLAN-T5-XXL but we may add more models later.
    HF_checkpoints = ['google/flan-t5-xxl']

    # Token to access HF inference API
    HF_headers = {"Authorization": f"Bearer {st.secrets['HF_token']}"}

    # Token to access OpenAI API
    openai.api_key = st.secrets['OpenAI_token']

    # Used to query models hosted on Huggingface
    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 name, url in AWS_checkpoints.items():
            headers={'x-api-key': st.secrets['aws-key']}

            input_data = json.dumps({'inputs':user_input})
            r = requests.get(url,data=input_data,headers=headers)
            output = r.json()[0]['generated_text']

            st.write(f'**{name}**: {output}')

        model_engine = "gpt-3.5-turbo"
        # Max tokens to produce
        max_tokens = 50

        # Prompt GPT-3.5 with an explicit question
        prompt = f"generate a question: {user_input}"

        # We give GPT-3.5 a message so it knows to generate questions from text.
        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_engine}**: {output}')

        
        for checkpoint in HF_checkpoints:
            
            model_name = checkpoint.split('/')[1]
    
            # For FLAN models we need to give them instructions explicitly.
            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_name}**: {output}')
        
if __name__ == "__main__":
    main()
#[0]['generated_text']