import openai from openai import OpenAI import streamlit as st from streamlit import session_state import os client = OpenAI() openai.api_key = os.getenv("OPENAI_API_KEY") def score(m,s): response = client.chat.completions.create( model="gpt-4-0125-preview", messages=[ { "role": "system", "content": "You are UPSC answers evaluater. You will be given model answer and student answer. Evaluate it by comparing with the model answer and give marks. Also provide 2 comments about the student answer in short. \n<>\nIt is 10 marks question. Give marks in the range of 0.5. (ex. 0,0.5,1...)\nPlease give marks generously. If the student answer body matches more than 60% with the model answer then give full marks for body. \nIf the student answer and model answer is not relevant then give 0 marks.\ngive output in json format. Give output in this format {\"total\":,\"comments\":[comment1, comment2]}\n<>" }, { "role": "user", "content": f"Model answer: {m}"}, { "role": "user", "content": f"Student answer: {s}" } ], temperature=0, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0 ) return response.choices[0].message.content from st_pages import Page, Section, show_pages, add_page_title,add_indentation st.set_page_config(page_title="Auto score Openai", page_icon="📈") st.markdown("

Welcome to Our App! 👋

", unsafe_allow_html=True) if 'result' not in session_state: session_state['result']= "" st.title("Auto score") text1= st.text_area(label= "Please write the model answer bellow", placeholder="What does the teacher say?") text2= st.text_area(label= "Please write the student answer bellow", placeholder="What does the student say?") def classify(text1,text2): session_state['result'] = score(text1,text2) st.text_area("result", value=session_state['result']) st.button("Classify", on_click=classify, args=[text1,text2])