app.py

# Cell 1: Setup
import streamlit as st
from openai import OpenAI
import os

# Get your OpenAI API key from environment variables
api_key = os.getenv("OPENAI_API_KEY")  # Used in production - Uncomment this line when you deploy

# Cell 2: Title & Description
st.title('🤖 AI Data Interview Assistant')
st.markdown('I was made to you answer Data interview questions. This app demonstrates how to use OpenAI GPT-3.5 to answer data-related interview questions in a deployed envionment. Remember, always verify AI-generated responses.')

# Cell 3: Function to analyze text using OpenAI
def analyze_text(text):
  """
  This function sends a text prompt to the OpenAI API using the GPT-3.5 model.

  Arg
      text (str): The tech interview question to be answered.

  Returns:
      str: The response generated by the GPT-3.5 model.
  """

  # Ensure your OPENAI_API_KEY is set as an environment variable
  if not api_key:
      st.error("OpenAI API key is not set. Please set it in your environment variables.")
      return

  client = OpenAI(api_key=api_key)
  model = "gpt-3.5-turbo"  # Using the GPT-3.5 model

  # Instructions for the AI (adjust if needed)
  messages = [
      {"role": "system", "content": "You are an assistant who answers interview and technical questions for a data science related jobs. If a user enters a question that is not related to interview and technical questions for data science related jobs, apologize and say that you can only answer questions about interview and technical questions for data science related jobs."},
      {"role": "user", "content": f"Answer the following job interview question:\n{text}"}
  ]

  response = client.chat.completions.create(
      model=model,
      messages=messages,
      temperature=0  # Lower temperature for less random responses
  )
  return response.choices[0].message.content

# Cell 4: Streamlit UI
user_input = st.text_area("Enter question to answer:", "How should you maintain a deployed model?")

if st.button('Answer Interview Question'):
  with st.spinner('Answering...'):
      result = analyze_text(user_input)
      st.write(result)