app.py

import gradio as gr
from huggingface_hub import InferenceClient

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

def login_screen():
  username = gr.Textbox("Username: ").value
  password = gr.Textbox("Password: ", type="password").value
  if username == "admin" and password == "pass1234":
    return None  # Login successful, no output
  else:
    return "Incorrect credentials. Please try again."

def chat(message):
  if not hasattr(chat, 'authorized'):
    chat.authorized = None  # Flag for login status

  if chat.authorized is None:
    response = login_screen()
    if response is None:
      chat.authorized = True
      return "Welcome! Ask me anything about Manufacturing"
    else:
      return response
  else:
    # Call the actual job description generation function
    return generate_job_description(message, max_tokens, temperature, top_p)

def generate_job_description(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response

"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    generate_job_description,
    additional_inputs=[
        gr.Textbox(value="You are an expert in mechanical engineering, manufacturing and production", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
    title="Manufacturing expert!",
    description="This agent answers questions related to manufacturing. Ask specific questions. Happy making things happen. ", 
    analytics_enabled=True
)

if __name__ == "__main__":
    demo.launch()