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import os | |
import json | |
from collections import deque | |
from dotenv import load_dotenv | |
import gradio as gr | |
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 | |
# Conduct interview and handle user input | |
def conduct_interview(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 | |
) | |
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.") | |
interview_data = [] | |
current_question_index = [0] # Use a list to hold the index | |
initial_message = ("👋 Hi there, I'm Sarah, your friendly AI HR assistant! " | |
"I'll guide you through a series of interview questions to learn more about you. " | |
"Take your time and answer each question thoughtfully.") | |
def interview_step(user_input, history): | |
if user_input.lower() in ["exit", "quit"]: | |
history.append((None, "The interview has ended at your request. Thank you for your time!")) | |
return history, "" | |
question_text = questions[current_question_index[0]] | |
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 in a warm and conversational way, offering natural follow-ups if needed.") | |
messages = [ | |
SystemMessage(content=system_prompt), | |
HumanMessage(content=combined_prompt) | |
] | |
response = chat.invoke(messages) | |
response_content = response.content.strip() | |
conversation_history.append({"question": question_text, "answer": user_input}) | |
interview_data.append({"question": question_text, "answer": user_input}) | |
history.append((user_input, None)) | |
history.append((None, response_content)) | |
if current_question_index[0] + 1 < len(questions): | |
current_question_index[0] += 1 | |
next_question = f"Alright, let's move on. {questions[current_question_index[0]]}" | |
history.append((None, next_question)) | |
return history, "" | |
else: | |
history.append((None, "That wraps up our interview. Thank you so much for your responses—it's been great learning more about you!")) | |
return history, "" | |
return interview_step, initial_message | |
# Gradio interface | |
def main(): | |
QUESTIONS_FILE_PATH = "questions.json" # Ensure you have a questions.json file with your interview questions | |
try: | |
questions = read_questions_from_json(QUESTIONS_FILE_PATH) | |
interview_func, initial_message = conduct_interview(questions) | |
css = """ | |
.contain { display: flex; flex-direction: column; } | |
.gradio-container { height: 100vh !important; } | |
#component-0 { height: 100%; } | |
.chatbot { flex-grow: 1; overflow: auto; height: 100px; } | |
.chatbot .wrap.svelte-1275q59.wrap.svelte-1275q59 {flex-wrap : nowrap !important} | |
.user > div > .message {background-color : #dcf8c6 !important} | |
.bot > div > .message {background-color : #f7f7f8 !important} | |
""" | |
with gr.Blocks(css=css) as demo: | |
gr.Markdown(""" | |
<h1 style='text-align: center; margin-bottom: 1rem'>👋 Welcome to Your AI HR Interview Assistant</h1> | |
""") | |
start_btn = gr.Button("Start Interview", variant="primary") | |
gr.Markdown(""" | |
<p style='text-align: center; margin-bottom: 1rem'>I will ask you a series of questions. Please answer honestly and thoughtfully. When you are ready, click "Start Interview" to begin.</p> | |
""") | |
chatbot = gr.Chatbot(label="Interview Chat", elem_id="chatbot", height=650) | |
user_input = gr.Textbox(label="Your Response", placeholder="Type your answer here...", lines=1) | |
with gr.Row(): | |
submit_btn = gr.Button("Submit", variant="primary") | |
clear_btn = gr.Button("Clear Chat") | |
def start_interview(): | |
history = [] | |
history.append((None, initial_message)) | |
history.append((None, "Let's begin! Here's your first question: " + questions[0])) | |
return history, "" | |
def clear_interview(): | |
return [], "" | |
def interview_step(user_response, history): | |
return interview_func(user_response, history) | |
def on_enter_submit(history, user_response): | |
if not user_response.strip(): | |
return history, "" | |
return interview_step(user_response, history) | |
start_btn.click(start_interview, inputs=[], outputs=[chatbot, user_input]) | |
submit_btn.click(interview_step, inputs=[user_input, chatbot], outputs=[chatbot, user_input]) | |
user_input.submit(on_enter_submit, inputs=[chatbot, user_input], outputs=[chatbot, user_input]) | |
clear_btn.click(clear_interview, inputs=[], outputs=[chatbot, user_input]) | |
demo.launch() | |
except Exception as e: | |
print(f"Error: {e}") | |
if __name__ == "__main__": | |
main() | |