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