Create app.py

app.py

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+import os
+import requests
+import gradio as gr
+
+from pydantic.v1 import BaseModel, Field
+from langchain_openai import ChatOpenAI
+from langchain.agents.output_parsers.openai_tools import OpenAIToolsAgentOutputParser
+from langchain.agents import AgentExecutor, create_openai_functions_agent
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.messages import AIMessage, HumanMessage
+from langchain.tools import StructuredTool
+
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+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 get_all_employees() -> str:
+    """
+    A function to get a list of all employees from database. 
+    Returns: 
+        str: A list of all employees in json.
+    """
+    return repo_get_all_employees_from_database()
+
+def get_employees(number_of_employees: int, start_time: str, duration_hours: int) -> str:
+    """
+    A function to get a required number_of_employees that are available from tart_time during specified duration_hours.
+    Args:
+        number_of_employees (int): Required number of employees.
+        start_time (str): Required start time.
+        duration_hours (int): Required duration of the availability in hours. 
+    Returns: 
+        str: Employees list in json.
+    """
+    method_return_value_stub = '''
+    {
+      "employees": [
+        {
+          "id": 100,
+          "name": "Lana Kane"
+        }
+      ]
+    }
+    '''
+    return method_return_value_stub
+
+class GetAllEmployees(BaseModel):
+    """
+    Pydantic arguments schema for get_all_employees function
+    """
+
+class GetEmployees(BaseModel):
+    """
+    Pydantic arguments schema for get_employees function
+    """
+    number_of_employees: int = Field(..., description="Required number of employees")
+    start_time: str = Field(..., description="Required start time")
+    duration_hours: int = Field(..., description="Required duration of the availability in hours")
+
+llm = ChatOpenAI(temperature=1.0, model_name="gpt-3.5-turbo", openai_api_key=OPENAI_API_KEY)
+
+tools = [
+    StructuredTool.from_function(
+        func=get_all_employees,
+        args_schema=GetAllEmployees,
+        description="A function to get a list of all employees from database."
+    ),
+    StructuredTool.from_function(
+        func=get_employees,
+        args_schema=GetEmployees,
+        description="A function to get a required number_of_employees that are available from tart_time during specified duration_hours."
+    )
+]
+
+system_content = """
+    You are an AI Interview Team Assistant that is developed by "Godel Technologies Europe" corporation.
+    You help to choose employees who can 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.
+    """
+
+def predict(message, history):
+    chat_history = []
+
+    for human, assistant in history:
+        chat_history.extend([HumanMessage(content=human), AIMessage(content=assistant)])
+
+    prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", system_content),
+        MessagesPlaceholder(variable_name="chat_history"),
+        ("user", message),
+        MessagesPlaceholder(variable_name="agent_scratchpad"),
+    ]
+    )
+
+    agent = create_openai_functions_agent(llm, tools, prompt)
+    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+    gpt_response = agent_executor.invoke({"input": message, "chat_history": chat_history})
+    gpt_output = gpt_response["output"]
+    chat_history.extend([HumanMessage(content=message), AIMessage(content=gpt_output)])
+    return gpt_output
+
+examples = [
+    "Who are you?",
+    "What is your purpose?",
+    "List all employees",
+    "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
+'''
+
+gr.ChatInterface(predict, examples=examples, description=description).launch()