import numpy as np
import plotly.graph_objects as go

import torch
from tqdm.auto import tqdm

from point_e.diffusion.configs import DIFFUSION_CONFIGS, diffusion_from_config
from point_e.diffusion.sampler import PointCloudSampler
from point_e.models.download import load_checkpoint
from point_e.models.configs import MODEL_CONFIGS, model_from_config
from point_e.util.plotting import plot_point_cloud

import gradio as gr

# Select device (CUDA if available, otherwise CPU)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Initialize base model
print('Creating base model...')
base_name = 'base40M-textvec'
base_model = model_from_config(MODEL_CONFIGS[base_name], device)
base_model.eval()
base_diffusion = diffusion_from_config(DIFFUSION_CONFIGS[base_name])

# Initialize upsample model
print('Creating upsample model...')
upsampler_model = model_from_config(MODEL_CONFIGS['upsample'], device)
upsampler_model.eval()
upsampler_diffusion = diffusion_from_config(DIFFUSION_CONFIGS['upsample'])

# Load checkpoints
print('Downloading base checkpoint...')
base_model.load_state_dict(load_checkpoint(base_name, device))

print('Downloading upsampler checkpoint...')
upsampler_model.load_state_dict(load_checkpoint('upsample', device))

# Initialize sampler
sampler = PointCloudSampler(
    device=device,
    models=[base_model, upsampler_model],
    diffusions=[base_diffusion, upsampler_diffusion],
    num_points=[1024, 4096 - 1024],
    aux_channels=['R', 'G', 'B'],
    guidance_scale=[3.0, 0.0],
    model_kwargs_key_filter=('texts', ''),  # Do not condition the upsampler at all
)

# Function to create point clouds
def create_point_cloud(inp):
    # Generate progressive samples
    samples = None
    for x in tqdm(sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(texts=[inp]))):
        samples = x

    # Extract the point cloud
    pc = sampler.output_to_point_clouds(samples)[0]

    # Generate a Plotly figure for visualization
    fig = plot_point_cloud(pc, grid_size=3, fixed_bounds=((-0.75, -0.75, -0.75), (0.75, 0.75, 0.75)))

    # Convert Plotly figure to HTML for Gradio compatibility
    return fig.to_html(full_html=False)

# Create Gradio interface
demo = gr.Interface(
    fn=create_point_cloud,
    inputs="text",
    outputs=gr.HTML(),  # Gradio expects HTML for Plotly visualizations
    title="Point-E Demo - Convert Text to 3D Point Clouds",
    description="Generate and visualize 3D point clouds from textual descriptions using OpenAI's Point-E framework."
)

# Enable queuing and launch Gradio app
demo.queue(max_size=30)
demo.launch(debug=True)