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