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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 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'] |