Update app.py

app.py

@@ -2,7 +2,6 @@ import streamlit as st
 import openai
 import json
 import os
-from fpdf import FPDF
 from dotenv import load_dotenv
 
 # Load the OpenAI API Key
@@ -95,28 +94,6 @@ def save_personality_to_output_json(username, mbti_type_classic, mbti_type_llm):
     with open("Output.json", "w") as json_file:
         json.dump(output_data, json_file, indent=4)
 
-# Function to generate PDF
-def generate_pdf(mbti_type_classic, mbti_type_llm, participant_name):
-    pdf = FPDF()
-    pdf.set_auto_page_break(auto=True, margin=15)
-    pdf.add_page()
-
-    pdf.set_font("Arial", size=16, style="B")
-    pdf.cell(200, 10, txt="FlexTemp Personality Test Results", ln=True, align="C")
-
-    pdf.ln(10)
-    pdf.set_font("Arial", size=12)
-
-    pdf.cell(200, 10, txt=f"Name: {participant_name}", ln=True)
-    pdf.cell(200, 10, txt=f"Your MBTI type based on weighted answers: {mbti_type_classic}", ln=True)
-    pdf.cell(200, 10, txt=f"Your MBTI type according to AI: {mbti_type_llm}", ln=True)
-
-    # Save the PDF to a file
-    pdf_output = f"{participant_name}_mbti_results.pdf"
-    pdf.output(pdf_output)
-
-    return pdf_output
-
 # Streamlit component to display the quiz and handle responses
 def show_mbti_quiz():
     st.title('FlexTemp Personality Test')
@@ -144,7 +121,6 @@ def show_mbti_quiz():
                 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)
-                mbti_type_llm = ""
                 if api_key:
                     # Run the LLM (GPT-4, for example) model to generate a personality type.
                     prompt = f"""
@@ -155,7 +131,7 @@ def show_mbti_quiz():
                     """
                     try:
                         response = openai.ChatCompletion.create(
-                            model="gpt-4",
+                            model="gpt-4o",
                             messages=[{"role": "system", "content": "You are a helpful assistant."},
                                       {"role": "user", "content": prompt}]
                         )
@@ -168,17 +144,6 @@ def show_mbti_quiz():
                 save_responses_to_json(participant_name, responses)
                 save_personality_to_output_json(participant_name, mbti_type_classic, mbti_type_llm)
 
-                # Generate and provide PDF download
-                pdf_output = generate_pdf(mbti_type_classic, mbti_type_llm, participant_name)
-                with open(pdf_output, "rb") as pdf_file:
-                    st.download_button(
-                        label="Download your MBTI results (PDF)",
-                        data=pdf_file,
-                        file_name=pdf_output,
-                        mime="application/pdf"
-                    )
-
-                # Provide options to download the JSON files
                 with open("Output.json", "r") as json_file:
                     json_data = json_file.read()
 
@@ -206,7 +171,7 @@ def show_mbti_quiz():
 def main():
     # Add instructions to the sidebar
     with st.sidebar.expander("How This App Works", expanded=False):
-        st.write(""" 
+        st.write("""
             ### FlexTemp Personality Test
             This app is designed to help you determine your MBTI personality type based on your answers to a series of questions. The process works as follows:
             1. **Weighted MBTI Scoring**: 
@@ -224,4 +189,4 @@ def main():
         st.info("Please enter your OpenAI API Key to begin the quiz.")
 
 if __name__ == "__main__":
-    main()
+    main()