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| import json | |
| import os | |
| import random | |
| import string | |
| import time | |
| from collections import defaultdict | |
| from dotenv import load_dotenv | |
| from openai import OpenAI | |
| from api.llm import LLMManager | |
| from utils.config import Config | |
| from resources.data import fixed_messages, topic_lists | |
| from resources.prompts import prompts | |
| from tests.testing_prompts import candidate_prompt | |
| config = Config() | |
| load_dotenv() | |
| def complete_interview(interview_type, exp_name, requirements="", difficulty="", topic="", model="gpt-3.5-turbo"): | |
| client = OpenAI(base_url="https://api.openai.com/v1") | |
| llm = LLMManager(config, prompts) | |
| llm_name = config.llm.name | |
| print(f"Starting evaluation interviewer LLM: {llm_name}, candidate_LLM: {model} interview_type: {interview_type}") | |
| # Select a random topic or difficulty if not provided | |
| topic = topic or random.choice(topic_lists[interview_type]) | |
| difficulty = difficulty or random.choice(["easy", "medium", "hard"]) | |
| problem_statement_text = llm.get_problem_full(requirements, difficulty, topic, interview_type) | |
| interview_data = defaultdict( | |
| lambda: None, | |
| { | |
| "interviewer_llm": llm_name, | |
| "candidate_llm": model, | |
| "inputs": { | |
| "interview_type": interview_type, | |
| "difficulty": difficulty, | |
| "topic": topic, | |
| "requirements": requirements, | |
| }, | |
| "problem_statement": problem_statement_text, | |
| "transcript": [], | |
| "feedback": None, | |
| "average_response_time_seconds": 0, | |
| }, | |
| ) | |
| # Initialize interviewer and candidate messages | |
| messages_interviewer = llm.init_bot(problem_statement_text, interview_type) | |
| chat_display = [[None, fixed_messages["start"]]] | |
| messages_candidate = [ | |
| {"role": "system", "content": candidate_prompt}, | |
| {"role": "user", "content": f"Your problem: {problem_statement_text}"}, | |
| {"role": "user", "content": chat_display[-1][1]}, | |
| ] | |
| response_times = [] | |
| previous_code = "" | |
| for _ in range(30): | |
| response = client.chat.completions.create( | |
| model=model, messages=messages_candidate, temperature=1, response_format={"type": "json_object"} | |
| ) | |
| response_json = json.loads(response.choices[0].message.content) | |
| code = response_json.get("code", "") | |
| candidate_message = response_json.get("message", "") | |
| if not code and not candidate_message: | |
| print("No message or code in response") | |
| continue | |
| messages_candidate.append({"role": "assistant", "content": response.choices[0].message.content}) | |
| if code: | |
| interview_data["transcript"].append(f"CANDIDATE CODE: {code}") | |
| elif candidate_message: | |
| interview_data["transcript"].append(f"CANDIDATE MESSAGE: {candidate_message}") | |
| chat_display.append([candidate_message, None]) | |
| # Check if the interview should finish | |
| if response_json.get("finished") and not response_json.get("question"): | |
| break | |
| send_time = time.time() | |
| messages_interviewer, chat_display, previous_code = llm.send_request_full(code, previous_code, messages_interviewer, chat_display) | |
| response_times.append(time.time() - send_time) | |
| messages_candidate.append({"role": "user", "content": chat_display[-1][1]}) | |
| interview_data["transcript"].append(f"INTERVIEWER MESSAGE: {chat_display[-1][1]}") | |
| interview_data["feedback"] = llm.end_interview_full(problem_statement_text, messages_interviewer, interview_type) | |
| interview_data["average_response_time_seconds"] = round(sum(response_times) / len(response_times), 2) if response_times else 0 | |
| current_time = time.strftime("%Y%m%d-%H%M%S") | |
| random_suffix = "".join(random.choices(string.ascii_letters + string.digits, k=10)) | |
| file_path = os.path.join("records", exp_name, f"{current_time}-{random_suffix}.json") | |
| os.makedirs(os.path.dirname(file_path), exist_ok=True) | |
| with open(file_path, "w") as file: | |
| json.dump(interview_data, file, indent=4) | |
| return file_path, interview_data | |