ChatGPT generated app with many attempts

app.py

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+import gradio as gr
+import plotly.graph_objects as go
+import numpy as np
+import torch
+from torch.optim import SGD
+import torch.optim.lr_scheduler as lr_schedulers
+
+# List of scheduler names
+schedulers = [
+    "ConstantLR", "LinearLR", "ExponentialLR", "StepLR", "MultiStepLR",
+    "CosineAnnealingLR", "CyclicLR", "OneCycleLR", "ReduceLROnPlateau",
+    "CosineAnnealingWarmRestarts"
+]
+
+# Function to create a Plotly line plot
+def create_line_plot(x, y, title="Line Plot"):
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=x, y=y, mode="lines", name=title))
+    fig.update_layout(title=title, xaxis_title="Steps", yaxis_title="Learning Rate")
+    return fig
+
+# Generic function to get learning rate schedule for any scheduler
+def get_lr_schedule(scheduler_name, initial_lr=0.1, total_steps=100, scheduler_kwargs=None):
+    if scheduler_kwargs is None:
+        scheduler_kwargs = {}
+    
+    # Initialize a dummy optimizer with a parameter
+    optimizer = SGD([torch.nn.Parameter(torch.zeros(1))], lr=initial_lr)
+
+    # Dynamically get the scheduler class from the torch.optim.lr_scheduler module
+    scheduler_class = getattr(lr_schedulers, scheduler_name)
+
+    # Initialize the scheduler with the keyword arguments
+    scheduler = scheduler_class(optimizer, **scheduler_kwargs)
+    
+    # Collect the learning rate for each step
+    lr_schedule = []
+    for step in range(total_steps):
+        lr_schedule.append(scheduler.get_last_lr()[0])
+        scheduler.step()
+    
+    return lr_schedule
+
+# Wrapper function for Gradio that handles scheduler selection
+def interactive_plot(x_min, x_max, scheduler_name, scheduler_params):
+    # Define x as steps for visualization
+    steps = np.arange(int(x_max))
+
+    # Generate learning rate schedule for the selected scheduler using scheduler_params
+    lr_schedule = get_lr_schedule(scheduler_name=scheduler_name, initial_lr=0.1, total_steps=len(steps), scheduler_kwargs=scheduler_params)
+    
+    # Plot the learning rate schedule
+    title = f"Learning Rate Schedule with {scheduler_name}"
+    fig = create_line_plot(steps, lr_schedule, title=title)
+
+    return fig
+
+# Define Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Learning Rate Scheduler Plotter")
+    x_min = gr.Number(label="X Min", value=0)
+    x_max = gr.Number(label="X Max (Steps)", value=100)
+    
+    # Dropdown for scheduler selection
+    scheduler_dropdown = gr.Dropdown(choices=schedulers, label="Learning Rate Scheduler", value="ConstantLR")
+    
+    # Scheduler parameter inputs (e.g., for ConstantLR, factor and total_iters)
+    scheduler_params = gr.JSON(label="Scheduler Parameters", value={"factor": 0.5, "total_iters": 10})
+    
+    plot = gr.Plot(label="Learning Rate Graph")
+    
+    plot_button = gr.Button("Plot Graph")
+    plot_button.click(interactive_plot, inputs=[x_min, x_max, scheduler_dropdown, scheduler_params], outputs=plot)
+
+# Launch the Gradio app
+demo.launch()