89 lines
3.5 KiB
Python
89 lines
3.5 KiB
Python
import json
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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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chat_config = {'max_tokens': 1000, 'temperature': 0.2}
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section_keys = ['reading', 'listening', 'writing', 'speaking', 'level']
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grade_top_limit = 9
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tools = [{
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"type": "function",
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"function": {
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"name": "save_evaluation_and_suggestions",
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"description": "Saves the evaluation and suggestions requested by input.",
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"parameters": {
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"type": "object",
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"properties": {
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"evaluation": {
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"type": "string",
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"description": "A comment on the IELTS section grade obtained in the specific section and what it could mean without suggestions.",
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},
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"suggestions": {
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"type": "string",
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"description": "A small paragraph text with suggestions on how to possibly get a better grade than the one obtained.",
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},
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},
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"required": ["evaluation", "suggestions"],
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},
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}
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}]
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def calculate_grading_summary(body):
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extracted_sections = extract_existing_sections_from_body(body, section_keys)
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ret = []
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for section in extracted_sections:
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openai_response_dict = calculate_section_grade_summary(section)
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ret = ret + [{'code': section['code'], 'name': section['name'], 'grade': section['grade'],
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'evaluation': openai_response_dict['evaluation'],
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'suggestions': openai_response_dict['suggestions']}]
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return {'sections': ret}
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def calculate_section_grade_summary(section):
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res = openai.ChatCompletion.create(
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model="gpt-3.5-turbo",
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max_tokens=chat_config['max_tokens'],
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temperature=chat_config['temperature'],
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tools=tools,
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messages=[
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{
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"role": "user",
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"content": "You are a IELTS test section grade evaluator. You will receive a IELTS test section name and the grade obtained in the section. You should offer a comment on this grade with also suggestions on how to possibly get a better grade.",
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},
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{
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"role": "user",
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"content": "Section: " + str(section['name']) + " Grade: " + str(section['grade']),
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},
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{"role": "user", "content": "Speak in third person."},
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{"role": "user", "content": "Please save the evaluation and suggestions generated."}
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])
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return parse_openai_response(res)
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def parse_openai_response(response):
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if 'choices' in response and len(response['choices']) > 0 and 'message' in response['choices'][
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0] and 'tool_calls' in response['choices'][0]['message'] and isinstance(
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response['choices'][0]['message']['tool_calls'], list) and len(
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response['choices'][0]['message']['tool_calls']) > 0 and \
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response['choices'][0]['message']['tool_calls'][0]['function']['arguments']:
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return json.loads(response['choices'][0]['message']['tool_calls'][0]['function']['arguments'])
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else:
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return {'evaluation': "", 'suggestions': ""}
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def extract_existing_sections_from_body(my_dict, keys_to_extract):
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if 'sections' in my_dict and isinstance(my_dict['sections'], list) and len(my_dict['sections']) > 0:
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return list(filter(
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lambda item: 'code' in item and item['code'] in keys_to_extract and 'grade' in item and 'name' in item,
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my_dict['sections']))
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