Update app.py

app.py

@@ -2,8 +2,6 @@ import streamlit as st
 import openai
 from dotenv import load_dotenv
 import os
-from PIL import Image, ImageDraw, ImageFont
-import io
 
 # Load the OpenAI API Key
 api_key = st.text_input('Enter your OpenAI API Key', type="password")
@@ -72,25 +70,6 @@ def classic_mbti_weighted(responses):
             mbti_type += trait2
     return mbti_type
 
-# Function to create an image from text
-def create_image_from_text(text):
-    # Create an empty image with white background
-    img = Image.new('RGB', (600, 400), color='white')
-    draw = ImageDraw.Draw(img)
-
-    # Load font (You can customize font and size)
-    font = ImageFont.load_default()
-    
-    # Draw the text on the image
-    draw.text((10, 10), text, fill='black', font=font)
-    
-    # Save to a buffer
-    buf = io.BytesIO()
-    img.save(buf, format="PNG")
-    buf.seek(0)
-    
-    return buf
-
 # Streamlit component to display the quiz and handle responses
 def show_mbti_quiz():
     st.title('FlexTemp Personality Test')
@@ -115,22 +94,27 @@ def show_mbti_quiz():
             if st.button("Generate Personality Trait Information"):
                 st.subheader("Your MBTI Personality Type:")
                 mbti_type_classic = classic_mbti_weighted(responses)
-                result_text = f"Your MBTI type based on weighted answers: {mbti_type_classic}"
-
-                # Display result text
-                st.write(result_text)
-
-                # Create an image of the result
-                image_buf = create_image_from_text(result_text)
-
-                # Display the generated image in Streamlit
-                st.image(image_buf)
-
-                # Optionally, save the image to disk
-                with open("personality_type_image.png", "wb") as f:
-                    f.write(image_buf.read())
-                st.success("Your result image has been saved as 'personality_type_image.png'.")
-
+                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!")