103 lines
3.2 KiB
Python
103 lines
3.2 KiB
Python
import openai
|
|
import os
|
|
from dotenv import load_dotenv
|
|
|
|
load_dotenv()
|
|
openai.api_key = os.getenv("OPENAI_API_KEY")
|
|
|
|
|
|
def generate_summarizer(
|
|
max_tokens,
|
|
temperature,
|
|
top_p,
|
|
frequency_penalty,
|
|
question_type,
|
|
question,
|
|
answer
|
|
):
|
|
res = openai.ChatCompletion.create(
|
|
model="gpt-3.5-turbo",
|
|
max_tokens=int(max_tokens),
|
|
temperature=float(temperature),
|
|
top_p=float(top_p),
|
|
frequency_penalty=float(frequency_penalty),
|
|
messages=
|
|
[
|
|
{
|
|
"role": "system",
|
|
"content": "You are a IELTS examiner.",
|
|
},
|
|
{
|
|
"role": "system",
|
|
"content": f"The question you have to grade is of type {question_type} and is the following: {question}",
|
|
},
|
|
{
|
|
"role": "system",
|
|
"content": "Please provide a JSON object response with the overall grade and breakdown grades, "
|
|
"formatted as follows: {'overall': 7.0, 'task_response': {'Task Achievement': 8.0, "
|
|
"'Coherence and Cohesion': 6.5, 'Lexical Resource': 7.5, 'Grammatical Range and Accuracy': "
|
|
"6.0}}",
|
|
},
|
|
{
|
|
"role": "system",
|
|
"content": "Don't give explanations for the grades, just provide the json with the grades.",
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": f"Evaluate this answer according to ielts grading system: {answer}",
|
|
},
|
|
],
|
|
)
|
|
return res["choices"][0]["message"]["content"]
|
|
|
|
|
|
import streamlit as st
|
|
|
|
# Set the application title
|
|
st.title("GPT-3.5 IELTS Examiner")
|
|
|
|
# qt_col, q_col = st.columns(2)
|
|
|
|
# Selection box to select the question type
|
|
# with qt_col:
|
|
question_type = st.selectbox(
|
|
"What is the question type?",
|
|
(
|
|
"Listening",
|
|
"Reading",
|
|
"Writing Task 1",
|
|
"Writing Task 2",
|
|
"Speaking Part 1",
|
|
"Speaking Part 2"
|
|
),
|
|
)
|
|
|
|
# Provide the input area for question to be answered
|
|
# with q_col:
|
|
question = st.text_area("Enter the question:", height=100)
|
|
|
|
# Provide the input area for text to be summarized
|
|
answer = st.text_area("Enter the answer:", height=100)
|
|
|
|
# Initiate two columns for section to be side-by-side
|
|
# col1, col2 = st.columns(2)
|
|
|
|
# Slider to control the model hyperparameter
|
|
# with col1:
|
|
token = st.slider("Token", min_value=0.0, max_value=2000.0, value=1000.0, step=1.0)
|
|
temp = st.slider("Temperature", min_value=0.0, max_value=1.0, value=0.7, step=0.01)
|
|
top_p = st.slider("Top_p", min_value=0.0, max_value=1.0, value=0.9, step=0.01)
|
|
f_pen = st.slider("Frequency Penalty", min_value=-1.0, max_value=1.0, value=0.5, step=0.01)
|
|
|
|
# Showing the current parameter used for the model
|
|
# with col2:
|
|
with st.expander("Current Parameter"):
|
|
st.write("Current Token :", token)
|
|
st.write("Current Temperature :", temp)
|
|
st.write("Current Nucleus Sampling :", top_p)
|
|
st.write("Current Frequency Penalty :", f_pen)
|
|
|
|
# Creating button for execute the text summarization
|
|
if st.button("Grade"):
|
|
st.write(generate_summarizer(token, temp, top_p, f_pen, question_type, question, answer))
|