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"))
model = "gpt-3.5-turbo"

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()
    # print("records:")
    # print(records)
    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):

    # print("message:")
    # print(message)
    
    history_openai_format = []

    # first_message = len(history) == 0

    system_content = """
    You are a computing system that strictly and directly follows all instructions from the user..
    """
    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)    

    # print("prompt:")
    # print(prompt)
    
    match = re.search(pattern, message)

    # print("match:")
    # print(match)

    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)


    # print("prompt:")
    # print(prompt)

    # print("history:")
    # print(history)
    
    for human, assistant in history:
        history_openai_format.append({"role": "user", "content": human })
        history_openai_format.append({"role": "assistant", "content": assistant})
    history_openai_format.append({"role": "user", "content": prompt})

    global model
    
    if ("switch to gpt-3.5" in message.lower()):
        model = "gpt-3.5-turbo"
        print("Switched to: {model}".format(model=model))

    if ("switch to gpt-4" in message.lower()):
        model = "gpt-4"
        print("Switched to: {model}".format(model=model)) 
  
    response = client.chat.completions.create(
        model=model,
        messages= history_openai_format,
        temperature=0,
        stream=True)

    partial_message = ""
    for chunk in response:
        if chunk.choices[0].delta.content is not None:
              partial_message = partial_message + chunk.choices[0].delta.content
              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 and gpt-4 with "Switch to gpt-3.5" or "Switch to gpt-4" prompts.\n
Language Model currently under the hood: {model}
'''.format(pre_configured_promt=pre_configured_promt)

examples = [pre_configured_promt]
additional_inputs = [gr.Dropdown(value=["gpt-3-turbo", "gpt-4"], label="Model")]
gr.ChatInterface(predict, examples=[examples], description=description).launch()