import numpy as np import requests import streamlit as st import openai #def main(): st.title("Scientific Question Generation") 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.""") # Filters st.sidebar.markdown("**Filters**") temperature = st.sidebar.slider("Temperature", 0.0, 1.0, 0.0,.1) if user_input: for checkpoint in checkpoints: model_name = checkpoint.split('/')[1] if 'flan' in model_name.lower(): prompt = 'generate a question: ' + user_input output = query(checkpoint,{ "inputs": prompt, "temperature":temperature, "wait_for_model":True})[0]['generated_text'] else: prompt = user_input output = query(checkpoint,{ "inputs": prompt, "temperature":temperature, "wait_for_model":True})[0]['generated_text'] 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()