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| 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() |