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server-azure.py
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server-azure.py
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from flask import Flask, request, jsonify
from flask_cors import CORS, cross_origin
import openai
import requests
import json
from dotenv import load_dotenv
import os
# Load the .env file located in the project directory
load_dotenv()
AZURE_API_KEY = os.getenv('AZURE_API_KEY')
openai.api_type = "azure"
# replace with you api endpoint
openai.api_base = "https://YourOwn-OpenAI-EndPoint.openai.azure.com/"
openai.api_version = "2023-03-15-preview"
openai.api_key = AZURE_API_KEY
app = Flask(__name__)
CORS(app)
@app.route('/generate_test_case', methods=['POST'])
@cross_origin()
def generate_test_case():
defect_description = request.json['defect_description']
# Call GPT-3 API with the defect_description
prompt = f"請用台灣繁體中文回答問題,利用以下系統缺陷描述產出對應的測試案例: {defect_description}"
print(prompt)
# If you use a GPT-3 series model, use the following code to call api
'''
response = openai.Completion.create(
engine="text-davinci-003",
prompt=prompt,
temperature=0.5,
max_tokens=1000,
top_p=1,
frequency_penalty=0,
presence_penalty=0,
stop=None)
test_steps = response['choices'][0]['text'].strip()
'''
# If you use GPT 3.5 or GPT 4, use the following code to call api
response = openai.ChatCompletion.create(
engine="gpt-35-turbo", # engine="gpt-4",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
],
temperature=0.5,
max_tokens=1000,
top_p=1,
frequency_penalty=0,
presence_penalty=0,
stop=None)
test_steps = response['choices'][0]['message']['content'].strip()
print(test_steps)
return jsonify(test_steps=test_steps)
if __name__ == '__main__':
app.run()