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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() |