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| 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}, | |
| ], | |
| ) | |
| 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."}, | |
| ], | |
| ) | |
| 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 | |