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import os
import json
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI # Correct import from langchain-openai
from langchain.schema import HumanMessage, SystemMessage # For creating structured chat messages
# Load environment variables
load_dotenv()
# Function to read questions from JSON
# The JSON is expected to contain a list of dictionaries or strings.
def read_questions_from_json(file_path):
"""
Reads questions from a JSON file.
"""
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 generate interview questions using LLM and collect user answers
def conduct_interview_with_llm(questions, language="English"):
"""
Generates interview questions using the LLM and collects user responses.
"""
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 = []
print("\n--- Technical Interview Started ---\n")
for index, question_text in enumerate(questions):
# Create the system and user prompts
system_prompt = f"You are Sarah, a compassionate and empathetic HR professional conducting a technical interview in {language}."
messages = [
SystemMessage(content=system_prompt),
HumanMessage(content=f"Generate the next interview question based on the context and previous history. Current question number: {index + 1}/{len(questions)}.")
]
try:
# Generate a question from the LLM
print(f"Generating question {index + 1}...")
response = chat.invoke(messages)
llm_generated_question = response.content.strip()
print(f"Q{index + 1}: {llm_generated_question}")
# Collect the user’s answer
user_answer = input("Your answer: ").strip()
interview_data.append({"question": llm_generated_question, "answer": user_answer})
except Exception as e:
print(f"Error with OpenAI API: {e}")
interview_data.append({"question": "An error occurred while generating the question.", "answer": "No answer recorded."})
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):
"""
Saves the questions and answers to a text file.
"""
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:
# Read questions from JSON file
questions = read_questions_from_json(QUESTIONS_FILE_PATH)
# Conduct the interview
interview_results = conduct_interview_with_llm(questions, language="English")
# Save the interview to a text file
save_interview_to_file(interview_results, INTERVIEW_FILE_PATH)
except Exception as e:
print(f"Error: {e}")
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