Create app.py

app.py

@@ -0,0 +1,140 @@
+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"
+
+model = gpt3_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()
+    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)
+    
+    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
+    switched = False
+    
+    if (switch_to.format(model=gpt3_turbo).lower() in message.lower()):
+        model = gpt3_turbo
+        switched = True
+
+    if (switch_to.format(model=gpt4).lower() in message.lower()):
+        model = gpt4
+        switched = True
+
+    if (switch_to.format(model=gpt4_turbo).lower() in message.lower()):
+        model = gpt4_turbo
+        switched = True
+
+    if (switched):
+        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)
+
+    partial_message = "🤖 {model}:\n\n".format(model=model)
+    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-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()