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from fastapi import FastAPI
from openai import OpenAI
import json
import os
app = FastAPI()
#client = OpenAI(api_key=OPENAI_API_KEY)
#org = os.getenv("org")
OPENAI_API_KEY = os.getenv("open_ai_key")
client = OpenAI(api_key=OPENAI_API_KEY) #, organization=org
description = """
### A FastAPI endpoint that takes a string as input and returns a list of questions along with their corresponding answers. This endpoint will be used to generate questions from Job Discriptions.
Details:
Input-1: A string containing the input text. (Type: String)
Input-2: Number of questions (Type: Integer)
--------------------------------------------
Output: A JSON response containing a list of questions and a corresponding list of answers.
"""
app = FastAPI(docs_url="/", description=description)
# def convert_format(input_dict):
# output_list = []
# for i in range(1, len(input_dict) // 2 + 1):
# question_key = f"Question {i}"
# answer_key = f"Answer {i}"
# if question_key in input_dict and answer_key in input_dict:
# output_list.append({"Question": input_dict[question_key], "Answer": input_dict[answer_key]})
# return output_list
@app.post("/get_questions")
async def getQuestions(job_description: str, no_of_questions: str):
response = client.chat.completions.create(
model="gpt-3.5-turbo-1106",
response_format={"type": "json_object"}, # To ENABLE JSON MODE
messages=[
{"role": "system",
"content": "You are a helpful assistant designed to output JSON in this format [question-text as key and its value as answer-text]"},
{"role": "user",
"content": f"Given the job description [{job_description}] create {no_of_questions} "
f"interview questions and their corresponding answers"}
]
)
result = response.choices[0].message.content
# Parse the JSON data
parsed_data = json.loads(result)
print(parsed_data)
# parsed_data = convert_format(parsed_data)
return parsed_data |