import streamlit as st import openai from dotenv import load_dotenv import os # Load the OpenAI API Key api_key = st.text_input('Enter your OpenAI API Key', type="password") # Set the OpenAI API key if api_key: openai.api_key = api_key # English-translated version of the questions (MBTI-related questions) questions = [ {"text": "Do you enjoy being spontaneous and keeping your options open?", "trait": "P"}, {"text": "Do you prefer spending weekends quietly at home rather than going out?", "trait": "I"}, {"text": "Do you feel more energized when you are around people?", "trait": "E"}, {"text": "Do you easily set and meet deadlines?", "trait": "J"}, {"text": "Are your decisions often influenced by how they will affect others emotionally?", "trait": "F"}, {"text": "Do you like discussing symbolic or metaphorical interpretations of a story?", "trait": "N"}, {"text": "Do you strive to maintain harmony in group settings, even if it means compromising?", "trait": "F"}, {"text": "When a friend is upset, is your first instinct to offer emotional support rather than solutions?", "trait": "F"}, {"text": "In arguments, do you focus more on being rational than on people's feelings?", "trait": "T"}, {"text": "When you learn something new, do you prefer hands-on experience over theory?", "trait": "S"}, {"text": "Do you often think about how today's actions will affect the future?", "trait": "N"}, {"text": "Are you comfortable adapting to new situations as they happen?", "trait": "P"}, {"text": "Do you prefer exploring different options before making a decision?", "trait": "P"}, {"text": "At parties, do you start conversations with new people?", "trait": "E"}, {"text": "When faced with a problem, do you prefer discussing it with others?", "trait": "E"}, {"text": "When making decisions, do you prioritize logic over personal considerations?", "trait": "T"}, {"text": "Do you find solitude more refreshing than social gatherings?", "trait": "I"}, {"text": "Do you prefer having a clear plan and dislike unexpected changes?", "trait": "J"}, {"text": "Do you find satisfaction in finishing tasks and making final decisions?", "trait": "J"}, {"text": "Do you tend to process your thoughts internally before speaking?", "trait": "I"}, {"text": "Are you more interested in exploring abstract theories and future possibilities?", "trait": "N"}, {"text": "When planning a vacation, do you prefer to have a detailed plan?", "trait": "S"}, {"text": "Do you often rely on objective criteria to assess situations?", "trait": "T"}, {"text": "Do you focus more on details and facts in your surroundings?", "trait": "S"} ] # Function to calculate MBTI scores based on responses def calculate_weighted_mbti_scores(responses): weights = { "Strongly Agree": 2, "Agree": 1, "Neutral": 0, "Disagree": -1, "Strongly Disagree": -2 } scores = {'E': 0, 'I': 0, 'S': 0, 'N': 0, 'T': 0, 'F': 0, 'J': 0, 'P': 0} for i, response in enumerate(responses): weight = weights.get(response, 0) trait = questions[i]["trait"] if trait in scores: scores[trait] += weight return scores # Function to determine MBTI type based on weighted scores def classic_mbti_weighted(responses): scores = calculate_weighted_mbti_scores(responses) mbti_type = "" for trait_pair in ['EI', 'SN', 'TF', 'JP']: trait1, trait2 = trait_pair if scores[trait1] >= scores[trait2]: mbti_type += trait1 else: mbti_type += trait2 return mbti_type # Streamlit component to display the quiz and handle responses def show_mbti_quiz(): st.title('FlexTemp Personality Test') # Step 1: Input name participant_name = st.text_input("Enter your name") if participant_name: responses = [] st.subheader(f"Hello {participant_name}, let's start the quiz!") for i, question in enumerate(questions): response = st.radio( question["text"], ["Strongly Agree", "Agree", "Neutral", "Disagree", "Strongly Disagree"] ) if response: responses.append(response) if len(responses) == len(questions): # Add a button to generate personality information if st.button("Generate Personality Trait Information"): st.subheader("Your MBTI Personality Type:") mbti_type_classic = classic_mbti_weighted(responses) st.write(f"Your MBTI type based on weighted answers: {mbti_type_classic}") # You can add LLM-based prediction if needed here (example OpenAI-based model) if api_key: # Run the LLM (GPT-4, for example) model to generate a personality type. prompt = f""" Determine a person's personality type based on their answers to the following Myers-Briggs Type Indicator (MBTI) questions: The person has answered the following questions: {', '.join([f"{question['text']} {response}" for question, response in zip(questions, responses)])} What is the MBTI personality type based on these answers? """ try: response = openai.ChatCompletion.create( model="gpt-4o", messages=[{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}] ) mbti_type_llm = response['choices'][0]['message']['content'] st.write(f"Your MBTI type according to AI: {mbti_type_llm}") except Exception as e: st.error(f"Error occurred: {e}") else: st.warning("Please answer all the questions!") # Main function to display the app def main(): if api_key: show_mbti_quiz() else: st.info("Please enter your OpenAI API Key to begin the quiz.") if __name__ == "__main__": main()