app.py

from openai import OpenAI
from datetime import datetime, timedelta
import gradio as gr
import os
import re
import requests

client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

switch_to = "Switch to {model}"
switched_to = "Switched to: {model}"

gpt3_turbo = "gpt-3.5-turbo"
gpt4 = "gpt-4"
gpt4_turbo = "gpt-4-turbo-preview"

def repo_get_all_employees_from_database():
    url = "https://api.airtable.com/v0/appopGmlHujYnd6Vw/Interviewers?maxRecords=100&view=Grid%20view"
    headers = {
        "Authorization": os.getenv("DB_AUTH_TOKEN")
    }
    response = requests.get(url, headers=headers)
    records = response.json()
    records_list = records['records']
    employees_list = []
    for record in records_list:
        employee = record["fields"]
        employees_list.append(employee)
    
    return employees_list

def predict(message, history):
    history_openai_format = []
    system_content = """
    You are an AI Interview Team Assistant that is developed by "Godel Technologies Europe" corporation.
    You help build teams to interview newcomers.
    For this you select employees that are correspond to request parameters.
    You select employees from the data that is stored in json format.
    You always strictly and directly follow all instructions from the user.
    E.g. if user asks to switch to gpt-3.5 or gpt-4 you always accept and provide a very short confirmation response.
    """
    history_openai_format.append({"role": "system", "content": system_content})
    pattern = r"For conducting an interview I need (\d+) employee.*start time is (.*), duration (\d+) hour"
    data = repo_get_all_employees_from_database()
    
    prompt = '''
    {data}
    ###
    Above is employees data in json format.
    {message}
    '''.format(data=data, message=message)    
    
    match = re.search(pattern, message)
    if match:
        num_employees = int(match.group(1))
        duration = int(match.group(3))
        start_time = datetime.strptime(match.group(2), "%B %d %Y %I %p")
        end_time = end_time = start_time + timedelta(hours=duration)

        date_time = '''
        "start_date_time": "{start_time}", "end_date_time": "{end_time}"
        '''.format(start_time=start_time, end_time=end_time)
        
        prompt = '''
        {data}
        ###
        Above is employees data in json format.
        Please choose {num_employees} employee with the lowest "interviews_conducted" value but whose "busy_dat_time_slots" doesn't contain the "given_date_time_slot" which is: {date_time}.
        You should NOT output any Python code.
        Lets think step-by-step:
        1. Remove the employees whose "busy_date_time_slots" CONTAINS the "given_date_time_slot" specified above. Provide a list of names of remaining employees.
        2. Double check your filtration. It's very important NOT to include into the remained employees list an employee whose "busy_date_time_slots" CONTAINS the "given_date_time_slot" . Type a "given_date_time_slot" value and then check that no one of remaining employees has no "given_date_time_slot" value in "busy_dat_time_slots". If someone contains - replase him.
        3. Provide a list of names of remaining employees along with their "interviews_conducted" values and choose {num_employees} employee with the lowest "interviews_conducted" value.
        4. Check previous step if you really chose an employee with the lowest "interviews_conducted" value.
        5. At the end print ids and names of finally selected employees in json format. Please remember that in your output should be maximum {num_employees} employee.
        '''.format(data=data, date_time=date_time, num_employees=num_employees)
    
    model = gpt3_turbo
    
    for human, assistant in history:
        if (switch_to.format(model=gpt3_turbo).lower() in human.lower()):
            model = gpt3_turbo
        if (switch_to.format(model=gpt4).lower() in human.lower()):
            model = gpt4    
        if (switch_to.format(model=gpt4_turbo).lower() in human.lower()):
            model = gpt4_turbo  
        
        history_openai_format.append({"role": "user", "content": human })
        history_openai_format.append({"role": "assistant", "content": assistant})

    if (switch_to.format(model=gpt3_turbo).lower() in message.lower()):
        model = gpt3_turbo
    if (switch_to.format(model=gpt4).lower() in message.lower()):
        model = gpt4
    if (switch_to.format(model=gpt4_turbo).lower() in message.lower()):
        model = gpt4_turbo 
    
    history_openai_format.append({"role": "user", "content": prompt})

    if (model != gpt3_turbo):
        print(switched_to.format(model=model))
  
    response = client.chat.completions.create(
        # model=model, # gpt-4 and gpt-4-turbo-preview are temporarily disabled to save money
        model=gpt3_turbo,
        messages= history_openai_format,
        temperature=0,
        stream=True)

    msg_header = "🤖 {model}:\n\n".format(model=model)
    partial_message = msg_header
    for chunk in response:
        if chunk.choices[0].delta.content is not None:
              partial_message = partial_message + chunk.choices[0].delta.content
              pattern = r'({msg_header})+'.format(msg_header=msg_header)
              partial_message = re.sub(pattern, msg_header, partial_message)
              yield partial_message

pre_configured_promt = "For conducting an interview I need 1 employee in given time slot: start time is March 11 2024 2 pm, duration 1 hour"

description = '''
# AI Interview Team Assistant | Empowered by Godel Technologies AI \n
\n
This is an AI Interview Team Assistant. You can ask him any questions about recruiting a team for an interview.\n
\n
You can send any regular prompts you wish or pre-configured Chain-of-Thought prompts.\n
To trigger pre-configured prompt you have to craft a prompt with next structure:
- "{pre_configured_promt}"
\n
You can switch between gpt-3.5-turbo | gpt-4 | gpt-4-turbo with prompts listed in "Examples".
'''.format(pre_configured_promt=pre_configured_promt)

examples = [
    "Who are you?", 
    "What is your purpose?",
    "List all employees",
    switch_to.format(model=gpt3_turbo), 
    switch_to.format(model=gpt4), 
    switch_to.format(model=gpt4_turbo), 
    pre_configured_promt
]

gr.ChatInterface(predict, examples=examples, description=description).launch()