Update app.py

app.py

@@ -1,76 +1,3 @@
-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()
+gr.load("models/WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0").launch()