app.py

import gradio as gr
import psutil
import torch

# Check for CUDA GPU availability and select appropriate memory type
def get_available_memory():
    if torch.cuda.is_available():  # Check if a CUDA-capable NVIDIA GPU is available
        total_memory = torch.cuda.get_device_properties(0).total_memory / (1024 ** 3)  # Convert to GB
        return total_memory, "GPU RAM"
    else:
        total_memory = psutil.virtual_memory().total / (1024 ** 3)  # Convert to GB
        return total_memory, "System RAM"

total_memory, memory_type = get_available_memory()

# Calculate the maximum instances the system can handle
max_instances = int(total_memory // 4.5)  # Each instance uses 4.5 GB

# Function to handle user input and display the bar, text, and warnings
def update_usage(num_instances):
    if num_instances > max_instances:
        num_instances = max_instances  # Snap back to the maximum valid value
        warning = f"⚠️ You tried to exceed the available {memory_type}. Max instances set to {max_instances}."
    else:
        warning = ""  # No warning if within limits

    usage = num_instances * 4.5
    usage_percentage = min((usage / total_memory) * 100, 100)

    # Progress bar HTML with a blue gradient
    bar_html = f"""
    <div style="width: 100%; background-color: #d6e6f2; border-radius: 12px; overflow: hidden; box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);">
        <div style="width: {usage_percentage}%; background: linear-gradient(to right, #1e3c72, #2a5298); height: 30px; transition: width 0.3s;"></div>
    </div>
    """
    memory_text = f"{usage:.2f} GB / {total_memory:.2f} GB ({memory_type})"
    
    return num_instances, memory_text, bar_html, warning

# Gradio Interface
with gr.Blocks() as demo:
    gr.Markdown(f"# Memory Usage Tracker ({memory_type})")

    slider = gr.Slider(minimum=1, maximum=max_instances, step=1, value=1, label="Number of Instances")
    memory_display = gr.Label(value=f"0.00 GB / {total_memory:.2f} GB ({memory_type})")
    progress_bar = gr.HTML(value="<div style='height: 30px;'></div>")  # Placeholder for progress bar
    warning_message = gr.HTML(value="")  # Placeholder for warning message

    def handle_slider_change(num_instances):
        # Update the UI elements when the slider value changes
        num_instances, memory_text, bar_html, warning = update_usage(num_instances)
        return num_instances, memory_text, bar_html, warning

    # Link the slider to the update function
    slider.change(handle_slider_change, inputs=[slider], outputs=[slider, memory_display, progress_bar, warning_message])

demo.launch()