import torch, cv2, celldetection as cd
from skimage.data import coins
from matplotlib import pyplot as plt

# Load pretrained model
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = cd.fetch_model('ginoro_CpnResNeXt101UNet-fbe875f1a3e5ce2c', check_hash=True).to(device)
model.eval()

# Load input
img = coins()
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
print(img.dtype, img.shape, (img.min(), img.max()))

# Run model
with torch.no_grad():
    x = cd.to_tensor(img, transpose=True, device=device, dtype=torch.float32)
    x = x / 255  # ensure 0..1 range
    x = x[None]  # add batch dimension: Tensor[3, h, w] -> Tensor[1, 3, h, w]
    y = model(x)

# Show results for each batch item
contours = y['contours']
for n in range(len(x)):
    cd.imshow_row(x[n], x[n], figsize=(16, 9), titles=('input', 'contours'))
    cd.plot_contours(contours[n])
    plt.show()

import timm

timm.list_models(filter='*')  # explore available models

import segmentation_models_pytorch as smp

smp.encoders.get_encoder_names()  # explore available models

encoder = cd.models.SmpEncoder(encoder_name='mit_b5', pretrained='imagenet')

# U-Nets are available in 2D and 3D
import celldetection as cd

model = cd.models.ResNeXt50UNet(in_channels=3, out_channels=1, nd=3)

# Many MA-Nets are available in 2D and 3D
import celldetection as cd

encoder = cd.models.ConvNeXtSmall(in_channels=3, nd=3)
model = cd.models.MaNet(encoder, out_channels=1, nd=3)

# ConvNeXt Networks are available in 2D and 3D
import celldetection as cd

model = cd.models.ConvNeXtSmall(in_channels=3, nd=3)

# Residual Networks are available in 2D and 3D
import celldetection as cd

model = cd.models.ResNet50(in_channels=3, nd=3)

from gradio_client import Client

# Define inputs (local filename or URL)
inputs = 'https://raw.githubusercontent.com/scikit-image/scikit-image/main/skimage/data/coins.png'

# Set up client
client = Client("ericup/celldetection")

# Predict
overlay_filename, img_filename, h5_filename, csv_filename = client.predict(
    inputs,  # str: Local filepath or URL of your input image
    
    # Model name
    'ginoro_CpnResNeXt101UNet-fbe875f1a3e5ce2c',
    
    # Custom Score Threshold (numeric value between 0 and 1)
    False, .9,  # bool: Whether to use custom setting; float: Custom setting
    
    # Custom NMS Threshold
    False, .3142,  # bool: Whether to use custom setting; float: Custom setting
    
    # Custom Number of Sample Points
    False, 128,  # bool: Whether to use custom setting; int: Custom setting
    
    # Overlapping objects
    True,  # bool: Whether to allow overlapping objects
    
    # API name (keep as is)
    api_name="/predict"
)


# Example usage: Code below only shows how to use the results
from matplotlib import pyplot as plt
import celldetection as cd
import pandas as pd

# Read results from local temporary files
img = imread(img_filename)
overlay = imread(overlay_filename)  # random colors per instance; transparent overlap
properties = pd.read_csv(csv_filename)
contours, scores, label_image = cd.from_h5(h5_filename, 'contours', 'scores', 'labels')

# Optionally display overlay
cd.imshow_row(img, img, figsize=(16, 9))
cd.imshow(overlay)
plt.show()

# Optionally display contours with text
cd.imshow_row(img, img, figsize=(16, 9))
cd.plot_contours(contours, texts=['score: %d%%\narea: %d' % s for s in zip((scores * 100).round(), properties.area)])
plt.show()