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import os
import json
from collections import deque
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from langchain.schema import HumanMessage, SystemMessage
# Load environment variables
load_dotenv()
# Function to read questions from JSON
def read_questions_from_json(file_path):
if not os.path.exists(file_path):
raise FileNotFoundError(f"The file '{file_path}' does not exist.")
with open(file_path, 'r') as f:
questions_list = json.load(f)
if not questions_list:
raise ValueError("The JSON file is empty or has invalid content.")
return questions_list
# Function to handle user input and responses from the LLM
def handle_user_input(chat, system_prompt, conversation_history, question_text):
while True:
user_input = input(f"Your response: ").strip()
if user_input.lower() in ["exit", "quit"]:
print("Interview terminated as requested.")
return "[Interview Terminated by User]", True
history_content = "\n".join([f"Q: {entry['question']}\nA: {entry['answer']}" for entry in conversation_history])
combined_prompt = (f"{system_prompt}\n\nPrevious conversation history:\n{history_content}\n\n"
f"Current question: {question_text}\nUser's input: {user_input}\n\n"
"Respond naturally to any follow-up questions or requests for clarification, and let the user know when you're ready to proceed.")
messages = [
SystemMessage(content=system_prompt),
HumanMessage(content=combined_prompt)
]
response = chat.invoke(messages)
response_content = response.content.strip()
if "proceed" in response_content.lower() or "continue" in response_content.lower():
print("Understood. Let’s continue to the next question.")
return user_input, False
else:
print(f"LLM's Response: {response_content}")
#print("Whenever you're ready, just let me know if you want to move forward or discuss further.")
print("Whenever you're ready, just let me know, if you want to move to the next question tell me proceed, or provide further clarifications.")
# Function to conduct the interview
def conduct_interview_with_user_input(questions, language="English", history_limit=5):
openai_api_key = os.getenv("OPENAI_API_KEY")
if not openai_api_key:
raise RuntimeError("OpenAI API key not found. Please add it to your .env file as OPENAI_API_KEY.")
chat = ChatOpenAI(
openai_api_key=openai_api_key, model="gpt-4", temperature=0.7, max_tokens=750
)
interview_data = []
conversation_history = deque(maxlen=history_limit)
system_prompt = (f"You are Sarah, an empathetic HR interviewer conducting a technical interview in {language}. "
"Respond to user follow-up questions politely and concisely. If the user is confused, provide clear clarification.")
print("\n--- Technical Interview Started ---\n")
for index, question_text in enumerate(questions):
print(f"{index + 1}/{len(questions)}: {question_text}")
try:
user_answer, terminate = handle_user_input(chat, system_prompt, conversation_history, question_text)
if terminate:
break
conversation_history.append({"question": question_text, "answer": user_answer})
interview_data.append({"question": question_text, "answer": user_answer})
if index + 1 == len(questions):
print("Thank you for your time. This concludes the interview. We will prepare a report based on the gathered information.")
except Exception as e:
print(f"Error during the interview process: {e}")
interview_data.append({"question": question_text, "answer": "No answer recorded due to an error."})
print("\n--- Technical Interview Completed ---\n")
return interview_data
# Function to save interview to a text file
def save_interview_to_file(interview_data, file_path):
with open(file_path, 'w') as f:
for entry in interview_data:
f.write(f"Q: {entry['question']}\n")
f.write(f"A: {entry['answer']}\n\n")
print(f"Interview saved to {file_path}")
if __name__ == "__main__":
QUESTIONS_FILE_PATH = "questions.json"
INTERVIEW_FILE_PATH = "interview.txt"
try:
questions = read_questions_from_json(QUESTIONS_FILE_PATH)
interview_results = conduct_interview_with_user_input(questions, language="English")
save_interview_to_file(interview_results, INTERVIEW_FILE_PATH)
except Exception as e:
print(f"Error: {e}")
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