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# import streamlit as st
# import os
# import json
# from openai import AzureOpenAI
# from model import invoke, create_models, configure_settings, load_documents_and_create_index, \
# create_chat_prompt_template, execute_query
#
# client = AzureOpenAI(azure_endpoint = "https://personalityanalysisfinetuning.openai.azure.com/",api_key=os.environ.get("AZURE_OPENAI_KEY"), api_version="2024-02-01")
#
#
# example_profile = {
# "Team": [
# {
# "name": "JAMES ARTHUR",
# "main_profile": {
# "VISION": {
# "score": 76
# },
# "IDEATION": {
# "score": 73
# },
# "OPPORTUNISM": {
# "score": 78
# },
# "DRIVE": {
# "score": 80
# },
# "RESILIENCE": {
# "score": 75
# }
# },
# "red_flag": {
# "HUBRIS": {
# "score": 80
# },
# "MERCURIAL": {
# "score": 28
# },
# "DOMINANT": {
# "score": 70
# },
# "MACHIAVELLIAN": {
# "score": 50
# }
# }
# },
# {
# "name": "LOUSIE HART",
# "main_profile": {
# "VISION": {
# "score": 55
# },
# "IDEATION": {
# "score": 60
# },
# "OPPORTUNISM": {
# "score": 65
# },
# "DRIVE": {
# "score": 70
# },
# "RESILIENCE": {
# "score": 72
# }
# },
# "red_flag": {
# "HUBRIS": {
# "score": 55
# },
# "MERCURIAL": {
# "score": 25
# },
# "DOMINANT": {
# "score": 67
# },
# "MACHIAVELLIAN": {
# "score": 30
# }
# }
# },
# {
# "name": "SIMONE LEVY",
# "main_profile": {
# "VISION": {
# "score": 30
# },
# "IDEATION": {
# "score": 45
# },
# "OPPORTUNISM": {
# "score": 20
# },
# "DRIVE": {
# "score": 50
# },
# "RESILIENCE": {
# "score": 32
# }
# },
# "red_flag": {
# "HUBRIS": {
# "score": 20
# },
# "MERCURIAL": {
# "score": 15
# },
# "DOMINANT": {
# "score": 18
# },
# "MACHIAVELLIAN": {
# "score": 25
# }
# }
# },
# {
# "name": "Uri Lef",
# "main_profile": {
# "VISION": {
# "score": 70
# },
# "IDEATION": {
# "score": 68
# },
# "OPPORTUNISM": {
# "score": 73
# },
# "DRIVE": {
# "score": 65
# },
# "RESILIENCE": {
# "score": 30
# }
# },
# "red_flag": {
# "HUBRIS": {
# "score": 55
# },
# "MERCURIAL": {
# "score": 72
# },
# "DOMINANT": {
# "score": 68
# },
# "MACHIAVELLIAN": {
# "score": 50
# }
# }
# }
# ]
# }
#
# # Function to generate a completion using OpenAI API
# # def generate_one_completion(message, temperature):
# # response = client.chat.completions.create(
# # model="personality_gpt4o",
# # temperature=temperature,
# # max_tokens=1000, # Adjust based on desired response length
# # frequency_penalty=0.2, # To avoid repetition
# # presence_penalty=0.2, # To introduce new topics
# # messages= message,
# # stream=False
# # )
# #
# # return response
#
# import json
#
# def generate_prompt_from_profile(profile, version="TeamSummary"):
# with open('prompts.json') as f:
# prompt_sets = json.load(f)['Prompts']
# prompt_templates = prompt_sets[version]
#
# try:
# team_members = profile['Team']
#
# team_member_profiles = []
# for member in team_members:
# profile = f"{member['name']}: Main Profile - VISION: {member['main_profile']['VISION']['score']}, " \
# f"IDEATION: {member['main_profile']['IDEATION']['score']}, " \
# f"OPPORTUNISM: {member['main_profile']['OPPORTUNISM']['score']}, " \
# f"DRIVE: {member['main_profile']['DRIVE']['score']}, " \
# f"RESILIENCE: {member['main_profile']['RESILIENCE']['score']}. " \
# f"Red Flags - HUBRIS: {member['red_flag']['HUBRIS']['score']}, " \
# f"MERCURIAL: {member['red_flag']['MERCURIAL']['score']}, " \
# f"DOMINANT: {member['red_flag']['DOMINANT']['score']}, " \
# f"MACHIAVELLIAN: {member['red_flag']['MACHIAVELLIAN']['score']}."
# team_member_profiles.append(profile)
#
# # Join the team member profiles into a single string
# team_member_profiles_str = "\n".join(team_member_profiles)
#
# prompt = "\n".join(prompt_templates).replace("{{TEAM_MEMBERS}}", team_member_profiles_str)
#
# print(prompt)
#
#
# except KeyError as e:
# return [{"role": "system", "content": f"Error processing profile data: missing {str(e)}"}]
#
# message = [
# {"role": "system", "content": prompt_sets["System"][0]},
# {"role": "user", "content": prompt}
# ]
# print(prompt)
#
# return message
#
#
# def display_profile_info(profile):
#
# st.markdown("### Profile Information:")
# team_members = profile["Team"]
# for member in team_members:
# st.sidebar.markdown(f"#### {member['name']}")
# main_profile = member["main_profile"]
# red_flag = member["red_flag"]
# st.sidebar.markdown("### Main Profile:")
# st.sidebar.markdown("\n".join([f"- **{attribute}**: {details['score']}" for attribute, details in main_profile.items()]))
# st.sidebar.markdown("### Red Flags:")
# st.sidebar.markdown("\n".join([f"- **{attribute}**: {details['score']}" for attribute, details in red_flag.items()]))
#
# # def validate_json(profile):
# # required_keys = ['Team']
# # for key in required_keys:
# # if key not in profile:
# # return False, f"Key '{key}' is missing."
# # if not isinstance(profile[key], dict):
# # return False, f"'{key}' should be a dictionary."
# # return True, "JSON structure is valid."
# def logout():
# st.session_state['authenticated'] = False
# st.session_state['profile'] = None
# st.session_state['show_chat'] = None
# st.session_state['analysis'] = None
# st.rerun()
#
#
# def main_app():
#
# sidebar_components()
#
# if st.button('Logout'):
# logout()
#
# # Streamlit app
# st.title('Metaprofiling\'s Career Insight Analyzer Demo')
#
#
# # Check if a profile is selected
# if st.session_state['profile']:
# profile = st.session_state['profile']
# display_profile_info(profile) # Display the profile information
#
# st.markdown("""
# ### Generation Temperature
# Adjust the 'Generation Temperature' to control the creativity of the AI responses.
# - A *lower temperature* (closer to 0.0) generates more predictable, conservative responses.
# - A *higher temperature* (closer to 1.0) generates more creative, diverse responses.
# """)
# # Temperature slider
# st.session_state['temperature'] = st.slider("",min_value=0.0, max_value=1.0, value=0.5, step=0.01)
#
# # Allow user to choose from different versions of the prompt
# st.session_state['version'] = st.selectbox("Select Prompt Version", ["TDOS"])
# # Generate and display prompt
#
# if st.button(f'Analyze Profile ({st.session_state["version"]})'):
# #with st.spinner('Generating completion...'):
# prompt = generate_prompt_from_profile(profile, version=st.session_state['version'])
#
# meta_eip_prefix = """# META: Entrepreneurial and Intrapreneurial Potential\nMETA evaluates five traits essential for
# entrepreneurial success: Vision, Ideation, Opportunism, Drive, and Resilience. It also measures four ‘Red
# Flags’ or derailers common to the entrepreneurial personality."""
#
# with st.chat_message("assistant"):
# stream = client.chat.completions.create(
# model="personality_gpt4o",
# temperature=st.session_state['temperature'],
# max_tokens=3000, # Adjust based on desired response length
# frequency_penalty=0.2, # To avoid repetition
# presence_penalty=0.2, # To introduce new topics
# messages= prompt,
# stream=True)
#
# if st.session_state['version'] == "METAEIP":
# st.write(meta_eip_prefix)
#
# response = st.write_stream(stream)
# #st.markdown(response_test_taker)
#
# st.session_state['analysis'] = response
# st.session_state['show_chat'] = True
# st.rerun()
#
# # display the response
# if st.session_state['analysis']:
# st.markdown(st.session_state['analysis'])
#
# else:
# st.write("Please upload a profile JSON file or use the example profile.")
#
#
# # Function to verify credentials and set the session state
# def verify_credentials():
# if st.session_state['username'] == os.getenv("username_app") and st.session_state['password'] == os.getenv("password_app"):
# st.session_state['authenticated'] = True
# else:
# st.error("Invalid username or password")
#
#
# # Login page
# def login_page():
#
# st.title("Welcome to Metaprofiling's Career Insight Analyzer Demo")
# st.write("This application provides in-depth analysis and insights into professional profiles. Please log in to continue.")
#
# # Description and Instructions
# st.markdown("""
# ## How to Use This Application
# - Enter your username and password in the sidebar.
# - Click on 'Login' to access the application.
# - Once logged in, you will be able to upload and analyze professional profiles.
# """)
#
# st.sidebar.write("Login:")
# username = st.sidebar.text_input("Username")#, key='username')
# password = st.sidebar.text_input("Password", type="password")#, key='password')
#
# st.session_state['username'] = username
# st.session_state['password'] = password
# st.sidebar.button("Login", on_click=verify_credentials)
#
# def sidebar_components():
# with st.sidebar:
# if st.button('Reset'):
# st.session_state['profile'] = None
# st.session_state['show_chat'] = None
# st.session_state['analysis'] = None
# st.rerun()
#
# if not st.session_state['show_chat']:
# # Instructions for JSON format
# st.markdown("### JSON File Requirements:")
# st.markdown("1. Must contain Team as top-level keys.")
# st.markdown("2. Both keys should have dictionary values.")
#
# # File uploader
# st.markdown("### Upload a profile JSON file")
# uploaded_file = st.file_uploader("", type=['json'])
#
# if uploaded_file is not None:
# try:
# profile_data = json.load(uploaded_file)
# #valid, message = validate_json(profile_data)
# #if valid:
# st.session_state['profile'] = profile_data
# #else:
# #st.error(message)
# except json.JSONDecodeError:
# st.error("Invalid JSON file. Please upload a valid JSON file.")
#
# # Button to load example profile
# if st.button('Use Example Profile'):
# st.session_state['profile'] = example_profile
#
# # elif uploaded_file is not None:
# # st.session_state['profile'] = json.load(uploaded_file)
# else:
# st.sidebar.title("Chat with Our Career Advisor")
# st.sidebar.markdown("Hello, we hope you learned something about yourself in this report. This chat is here so you can ask any questions you have about your report! It’s also a great tool to get ideas about how you can use the information in your report for your personal development and achieving your current goals.")
#
#
#
# # List of question templates where {} will be replaced with the name
# question_templates = [
# "What are the main risks associated with {}’s profile?",
# "What are the implications of {}’s profile for working with others?",
# "What conclusions might we draw from his profile about {}’s style of leadership?",
# "Looking specifically at {}'s Red Flags, are there any particular areas of concern?",
# "Based on this profile, is {} better suited as a COO or a CEO?",
# "If speed of execution is important, based on his profile, how likely is {} to be able to achieve this?",
# "How is {} likely to react to business uncertainty and disruption?",
# "Based on his profile, what should a coaching plan designed for {} focus on?"
# ]
#
# # Formatting each question template with the name
# questions_list = [question.format("Test Taker") for question in question_templates]
#
# # Prepare the questions for Markdown display
# questions_markdown = "\n\n".join(
# [f"Q{index + 1}: {question}" for index, question in enumerate(questions_list)])
#
# # Code to display in the app
# st.sidebar.markdown("### Suggest Questions")
# st.sidebar.markdown(questions_markdown)
#
# # st.sidebar.text_area("Suggested Questions", value=questions.choices[0].message.content, height=200, disabled=True)
#
# user_input = st.sidebar.text_input("Ask a question about the profile analysis:")
#
# llm, embed_model = create_models()
# configure_settings(llm, embed_model)
# index = load_documents_and_create_index()
#
# if st.sidebar.button('Submit'):
# if user_input:
# # with open('prompts.json') as f:
# # prompt_sets = json.load(f)['Prompts']
# # instruction = prompt_sets['Question']
#
#
# # instruction = (
# # "You are a knowledgeable advisor providing insights based on the specific analysis provided earlier. "
# # "Your responses should around 100 words, directly relate to the user's question, drawing on relevant details from the analysis. "
# # "If the user's question does not pertain to the analysis or is beyond the scope of the information provided, "
# # "politely decline to answer, stating that the question is outside the analysis context. Focus on delivering "
# # "concise, accurate, insightful, and relevant information. \n\n"
# # "Question: " + user_input
# # )
#
# # message = generate_prompt_from_profile(st.session_state['profile'])
# # message.append({"role": "system", "content": st.session_state['analysis']})
# # message.append({"role": "user", "content": "\n".join(instruction).replace('{{QUESTION}}', user_input)})
#
# # with st.chat_message("assistant"):
# # stream = client.chat.completions.create(
# # model="personality_gpt4",
# # temperature=st.session_state['temperature'],
# # max_tokens=500, # Adjust based on desired response length
# # frequency_penalty=0.2, # To avoid repetition
# # presence_penalty=0.2, # To introduce new topics
# # messages=message,
# # stream=True
# # )
#
# chat_prompt_template = create_chat_prompt_template(st.session_state['analysis'])
# response = execute_query(index, chat_prompt_template, user_input)
#
# #response = st.write_stream(stream)
#
#
# # output = generate_one_completion(message,st.session_state['temperature'])
# #
# # #st.sidebar.text_area("Response", value=output.choices[0].message.content, height=200, disabled=True)
# st.sidebar.markdown(response)
#
# # Display the sidebar components based on the state
# if 'show_chat' not in st.session_state:
# st.session_state['show_chat'] = None
#
# if 'profile' not in st.session_state:
# st.session_state['profile'] = None
#
# if 'analysis' not in st.session_state:
# st.session_state['analysis'] = None
#
# if 'temperature' not in st.session_state:
# st.session_state['temperature'] = 0
#
# if 'version' not in st.session_state:
# st.session_state['version'] = ""
#
# # Initialize session state for username, password, and authentication
# if 'username' not in st.session_state:
# st.session_state['username'] = ''
# if 'password' not in st.session_state:
# st.session_state['password'] = ''
# if 'authenticated' not in st.session_state:
# st.session_state['authenticated'] = False
# # Show login or main app based on authentication
# if st.session_state['authenticated']:
# main_app()
# else:
# login_page()
import streamlit as st
import os
import json
from openai import AzureOpenAI
from model import invoke, create_models, configure_settings, load_documents_and_create_index, \
create_chat_prompt_template, execute_query
client = AzureOpenAI(azure_endpoint="https://personalityanalysisfinetuning.openai.azure.com/", api_key=os.environ.get("AZURE_OPENAI_KEY"), api_version="2024-02-01")
# Example profile (as before)
example_profile = {
"Team": [
{"name": "JAMES ARTHUR", "main_profile": {"VISION": {"score": 76}, "IDEATION": {"score": 73}, "OPPORTUNISM": {"score": 78}, "DRIVE": {"score": 80}, "RESILIENCE": {"score": 75}},
"red_flag": {"HUBRIS": {"score": 80}, "MERCURIAL": {"score": 28}, "DOMINANT": {"score": 70}, "MACHIAVELLIAN": {"score": 50}}},
{"name": "LOUSIE HART", "main_profile": {"VISION": {"score": 55}, "IDEATION": {"score": 60}, "OPPORTUNISM": {"score": 65}, "DRIVE": {"score": 70}, "RESILIENCE": {"score": 72}},
"red_flag": {"HUBRIS": {"score": 55}, "MERCURIAL": {"score": 25}, "DOMINANT": {"score": 67}, "MACHIAVELLIAN": {"score": 30}}},
{"name": "SIMONE LEVY", "main_profile": {"VISION": {"score": 30}, "IDEATION": {"score": 45}, "OPPORTUNISM": {"score": 20}, "DRIVE": {"score": 50}, "RESILIENCE": {"score": 32}},
"red_flag": {"HUBRIS": {"score": 20}, "MERCURIAL": {"score": 15}, "DOMINANT": {"score": 18}, "MACHIAVELLIAN": {"score": 25}}},
{"name": "Uri Lef", "main_profile": {"VISION": {"score": 70}, "IDEATION": {"score": 68}, "OPPORTUNISM": {"score": 73}, "DRIVE": {"score": 65}, "RESILIENCE": {"score": 30}},
"red_flag": {"HUBRIS": {"score": 55}, "MERCURIAL": {"score": 72}, "DOMINANT": {"score": 68}, "MACHIAVELLIAN": {"score": 50}}}
]
}
def verify_credentials():
if st.session_state['username'] == os.getenv("username_app") and st.session_state['password'] == os.getenv("password_app"):
st.session_state['authenticated'] = True
else:
st.error("Invalid username or password")
def login_page():
st.title("Welcome to Metaprofiling's Career Insight Analyzer Demo")
st.write("This application provides in-depth analysis and insights into professional profiles. Please log in to continue.")
# Description and Instructions
st.markdown("""
## How to Use This Application
- Enter your username and password in the sidebar.
- Click on 'Login' to access the application.
- Once logged in, you will be able to upload and analyze professional profiles.
""")
st.sidebar.write("Login:")
username = st.sidebar.text_input("Username")#, key='username')
password = st.sidebar.text_input("Password", type="password")#, key='password')
st.session_state['username'] = username
st.session_state['password'] = password
st.sidebar.button("Login", on_click=verify_credentials)
# Update generate_prompt_from_profile to take selected team members
def generate_prompt_from_profile(profile, selected_members, version="TeamSummary"):
with open('prompts.json') as f:
prompt_sets = json.load(f)['Prompts']
prompt_templates = prompt_sets[version]
try:
team_member_profiles = []
for member in profile['Team']:
if member['name'] in selected_members:
profile_str = (f"{member['name']}: Main Profile - VISION: {member['main_profile']['VISION']['score']}, "
f"IDEATION: {member['main_profile']['IDEATION']['score']}, "
f"OPPORTUNISM: {member['main_profile']['OPPORTUNISM']['score']}, "
f"DRIVE: {member['main_profile']['DRIVE']['score']}, "
f"RESILIENCE: {member['main_profile']['RESILIENCE']['score']}. "
f"Red Flags - HUBRIS: {member['red_flag']['HUBRIS']['score']}, "
f"MERCURIAL: {member['red_flag']['MERCURIAL']['score']}, "
f"DOMINANT: {member['red_flag']['DOMINANT']['score']}, "
f"MACHIAVELLIAN: {member['red_flag']['MACHIAVELLIAN']['score']}.")
team_member_profiles.append(profile_str)
team_member_profiles_str = "\n".join(team_member_profiles)
prompt = "\n".join(prompt_templates).replace("{{TEAM_MEMBERS}}", team_member_profiles_str)
print(prompt)
except KeyError as e:
return [{"role": "system", "content": f"Error processing profile data: missing {str(e)}"}]
message = [
{"role": "system", "content": prompt_sets["System"][0]},
{"role": "user", "content": prompt}
]
return message
def display_profile_info(profile):
st.markdown("### Profile Information:")
team_members = profile["Team"]
for member in team_members:
st.sidebar.markdown(f"#### {member['name']}")
main_profile = member["main_profile"]
red_flag = member["red_flag"]
st.sidebar.markdown("### Main Profile:")
st.sidebar.markdown("\n".join([f"- **{attribute}**: {details['score']}" for attribute, details in main_profile.items()]))
st.sidebar.markdown("### Red Flags:")
st.sidebar.markdown("\n".join([f"- **{attribute}**: {details['score']}" for attribute, details in red_flag.items()]))
def logout():
st.session_state['authenticated'] = False
st.session_state['profile'] = None
st.session_state['show_chat'] = None
st.session_state['analysis'] = None
st.rerun()
def main_app():
sidebar_components()
if st.button('Logout'):
logout()
st.title("Metaprofiling's Career Insight Analyzer Demo")
if st.session_state['profile']:
profile = st.session_state['profile']
display_profile_info(profile)
st.markdown("""
### Generation Temperature
Adjust the 'Generation Temperature' to control the creativity of the AI responses.
- A *lower temperature* (closer to 0.0) generates more predictable, conservative responses.
- A *higher temperature* (closer to 1.0) generates more creative, diverse responses.
""")
st.session_state['temperature'] = st.slider("", min_value=0.0, max_value=1.0, value=0.5, step=0.01)
st.session_state['version'] = st.selectbox("Select Prompt Version", ["TDOS"])
# Add a multiselect for team member selection
team_member_names = [member['name'] for member in profile['Team']]
selected_members = st.multiselect("Select Team Members to Include in the Analysis", team_member_names, default=team_member_names)
if st.button(f'Analyze Profile ({st.session_state["version"]})'):
prompt = generate_prompt_from_profile(profile, selected_members, version=st.session_state['version'])
meta_eip_prefix = """# META: Entrepreneurial and Intrapreneurial Potential\nMETA evaluates five traits essential for
entrepreneurial success: Vision, Ideation, Opportunism, Drive, and Resilience. It also measures four ‘Red
Flags’ or derailers common to the entrepreneurial personality."""
with st.chat_message("assistant"):
stream = client.chat.completions.create(
model="personality_gpt4o",
temperature=st.session_state['temperature'],
max_tokens=3000,
frequency_penalty=0.2,
presence_penalty=0.2,
messages=prompt,
stream=True
)
if st.session_state['version'] == "METAEIP":
st.write(meta_eip_prefix)
response = st.write_stream(stream)
st.session_state['analysis'] = response
st.session_state['show_chat'] = True
st.rerun()
if st.session_state['analysis']:
st.markdown(st.session_state['analysis'])
else:
st.write("Please upload a profile JSON file or use the example profile.")
def sidebar_components():
with st.sidebar:
if st.button('Reset'):
st.session_state['profile'] = None
st.session_state['show_chat'] = None
st.session_state['analysis'] = None
st.rerun()
if not st.session_state['show_chat']:
st.markdown("### JSON File Requirements:")
st.markdown("1. Must contain Team as top-level keys.")
st.markdown("2. Both keys should have dictionary values.")
uploaded_file = st.file_uploader("", type=['json'])
if uploaded_file is not None:
try:
profile_data = json.load(uploaded_file)
st.session_state['profile'] = profile_data
except json.JSONDecodeError:
st.error("Invalid JSON file. Please upload a valid JSON file.")
if st.button('Use Example Profile'):
st.session_state['profile'] = example_profile
else:
st.sidebar.title("Chat with Our Career Advisor")
st.sidebar.markdown("Hello, we hope you learned something about yourself in this report. This chat is here so you can ask any questions you have about your report! It’s also a great tool to get ideas about how you can use the information in your report for your personal development and achieving your current goals.")
question_templates = [
"What are the main risks associated with {}’s profile?",
"What are the implications of {}’s profile for working with others?",
"What conclusions might we draw from his profile about {}’s style of leadership?",
"Looking specifically at {}'s Red Flags, are there any particular areas of concern?",
"Based on this profile, is {} better suited as a COO or a CEO?",
"If speed of execution is important, based on his profile, how likely is {} to be able to achieve this?",
"How is {} likely to react to business uncertainty and disruption?",
"Based on his profile, what should a coaching plan designed for {} focus on?"
]
questions_list = [question.format("Test Taker") for question in question_templates]
questions_markdown = "\n\n".join([f"Q{index + 1}: {question}" for index, question in enumerate(questions_list)])
st.sidebar.markdown("### Suggest Questions")
st.sidebar.markdown(questions_markdown)
user_input = st.sidebar.text_input("Ask a question about the profile analysis:")
llm, embed_model = create_models()
configure_settings(llm, embed_model)
index = load_documents_and_create_index()
if st.sidebar.button('Submit'):
if user_input:
chat_prompt_template = create_chat_prompt_template(st.session_state['analysis'])
response = execute_query(index, chat_prompt_template, user_input)
st.sidebar.markdown(response)
if 'show_chat' not in st.session_state:
st.session_state['show_chat'] = None
if 'profile' not in st.session_state:
st.session_state['profile'] = None
if 'analysis' not in st.session_state:
st.session_state['analysis'] = None
if 'temperature' not in st.session_state:
st.session_state['temperature'] = 0
if 'version' not in st.session_state:
st.session_state['version'] = ""
if 'username' not in st.session_state:
st.session_state['username'] = ''
if 'password' not in st.session_state:
st.session_state['password'] = ''
if 'authenticated' not in st.session_state:
st.session_state['authenticated'] = False
if st.session_state['authenticated']:
main_app()
else:
login_page()
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