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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()
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