import json from dotenv import load_dotenv from openai import OpenAI from audio import numpy_audio_to_bytes from prompts import coding_interviewer_prompt, grading_feedback_prompt load_dotenv() # TODO: don't use my key client = OpenAI() def init_bot(problem=""): chat_history = [ {"role": "system", "content": coding_interviewer_prompt}, {"role": "system", "content": f"The candidate is solving the following problem: {problem}"}, ] return chat_history def get_problem(requirements, difficulty, topic, model, client=client): prompt_system = "You are ChatGPT acting as a coding round interviewer for a big-tech company. " full_prompt = f"Generate a {difficulty} {topic} problem in. Follow additional requirements: {requirements}. The problem should be solvable within 30 minutes." response = client.chat.completions.create( model=model, messages=[ {"role": "system", "content": prompt_system}, {"role": "user", "content": full_prompt}, ], temperature=1.5, ) question = response.choices[0].message.content.strip() chat_history = init_bot(question) return question, chat_history def end_interview(chat_history, model, client=client): transcript = [] for message in chat_history[1:]: role = message["role"] content = f"{role.capitalize()}: {message['content']}" transcript.append(content) response = client.chat.completions.create( model=model, messages=[ {"role": "system", "content": grading_feedback_prompt}, {"role": "user", "content": "Interview transcript:" + "\n\n".join(transcript)}, {"role": "user", "content": "Grade the interview based on the transcript provided and give a feedback."}, ], temperature=0.5, ) feedback = response.choices[0].message.content.strip() return feedback def send_request(code, previous_code, message, chat_history, chat_display, model, client=client): if code != previous_code: chat_history.append({"role": "user", "content": f"My latest code: {code}"}) chat_history.append({"role": "user", "content": message}) response = client.chat.completions.create(model=model, response_format={"type": "json_object"}, messages=chat_history) json_reply = response.choices[0].message.content.strip() try: data = json.loads(json_reply) reply = data["reply_to_candidate"] except json.JSONDecodeError as e: print("Failed to decode JSON:", str(e)) reply = "There was an error processing your request." chat_history.append({"role": "assistant", "content": json_reply}) chat_display.append([message, str(reply)]) return chat_history, chat_display, "", code def transcribe_audio(audio, client=client): transcription = client.audio.transcriptions.create( model="whisper-1", file=("temp.wav", numpy_audio_to_bytes(audio[1]), "audio/wav"), response_format="text" ) return transcription def text_to_speech(text, client=client): response = client.audio.speech.create(model="tts-1", voice="alloy", input=text) return response.content def read_last_message(chat_display): last_message = chat_display[-1][1] audio = text_to_speech(last_message) return audio