update

app.py

@@ -1,479 +1,3 @@
-# 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
@@ -481,6 +5,10 @@ from openai import AzureOpenAI
 from model import invoke, create_models, configure_settings, load_documents_and_create_index, \
     create_chat_prompt_template, execute_query
 
+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."""
+
 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)
@@ -612,9 +140,7 @@ def main_app():
         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(
@@ -637,6 +163,7 @@ def main_app():
             st.rerun()
 
         if st.session_state['analysis']:
+            st.write(meta_eip_prefix)
             st.markdown(st.session_state['analysis'])
 
     else: