File size: 3,122 Bytes
4f8607d 6ae0c6d 4f8607d 6ae0c6d 4f8607d 6ae0c6d 4f8607d 6ae0c6d 4f8607d 6ae0c6d 4f8607d 6ae0c6d 4f8607d 6ae0c6d 4f8607d 6ae0c6d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
import os
import gradio as gr
from huggingface_hub import HfApi, SpaceHardware
# Set up Hugging Face API token and Space ID
HF_TOKEN = os.getenv("HF_TOKEN") # Ensure your Hugging Face token is set as a secret
TRAINING_SPACE_ID = "your_space_id_here" # Replace with your actual space ID
# Initialize Hugging Face API
api = HfApi(token=HF_TOKEN)
# Function to check for a scheduled task (this is a placeholder for your actual task-checking logic)
def get_task():
# You can implement logic here to check for scheduled tasks
return None # For example, return None if no task is scheduled
# Function to add a new task (you can implement this depending on your use case)
def add_task(task):
# Logic to add a new task
return f"Task '{task}' added!"
# Function to mark the task as "DONE" (this is a placeholder)
def mark_as_done(task):
# Mark the task as done once it's completed
return f"Task '{task}' completed!"
# Function to simulate training the model (replace with actual training logic)
def train_and_upload(task):
# Implement your model training logic here
return f"Training model with task: {task}"
# Gradio function to simulate chat-like interface
def gradio_fn(task_input, history):
task = get_task()
if task is None:
# If no task, add a new task and request hardware
add_task_response = add_task(task_input)
api.request_space_hardware(repo_id=TRAINING_SPACE_ID, hardware=SpaceHardware.T4_MEDIUM)
# Add the new task response to the chat history
history.append(("Bot", add_task_response))
return "", history # Clear the input box and return updated history
else:
# If a task is available, check for hardware
runtime = api.get_space_runtime(repo_id=TRAINING_SPACE_ID)
if runtime.hardware == SpaceHardware.T4_MEDIUM:
# Fine-tune model on GPU if available
train_and_upload_response = train_and_upload(task)
mark_as_done_response = mark_as_done(task)
# Add responses to history
history.append(("Bot", train_and_upload_response))
history.append(("Bot", mark_as_done_response))
# Reset to CPU hardware after training
api.request_space_hardware(repo_id=TRAINING_SPACE_ID, hardware=SpaceHardware.CPU_BASIC)
else:
# If GPU hardware is not available, request it
api.request_space_hardware(repo_id=TRAINING_SPACE_ID, hardware=SpaceHardware.T4_MEDIUM)
history.append(("Bot", "Requesting GPU hardware..."))
return "", history # Clear the input box and return updated history
# Create the Gradio interface for chat
chat_interface = gr.Interface(
fn=gradio_fn,
inputs=[gr.Textbox(label="Enter task name", placeholder="Type your task here...", lines=1)],
outputs=[gr.Chatbot()],
live=True,
title="Task Manager Bot", # Optional: Title for the interface
description="Interact with the bot to manage tasks and trigger model training."
)
# Launch the Gradio interface
chat_interface.launch()
|