File size: 3,943 Bytes
691f8f6 d54d01e 691f8f6 454355d c1c357e 691f8f6 454355d aad3727 691f8f6 c1c357e d54d01e c1c357e d54d01e c1c357e 691f8f6 454355d 691f8f6 1262571 691f8f6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 |
import gradio as gr
import pandas as pd
import umap
import matplotlib.pyplot as plt
import os
import tempfile
import scanpy as sc
import argparse
import subprocess
import sys
from io import BytesIO
from huggingface_hub import hf_hub_download
def main(input_file_path, species):
# Get the current working directory
current_working_directory = os.getcwd()
# Print the current working directory
print("Current Working Directory:", current_working_directory)
# clone and cd into UCE repo
os.system('git clone https://github.com/minwoosun/UCE.git')
os.chdir('/home/user/app/UCE')
# Get the current working directory
current_working_directory = os.getcwd()
# Print the current working directory
print("Current Working Directory:", current_working_directory)
# Specify the path to the directory you want to add
new_directory = "/home/user/app/UCE"
# Add the directory to the Python path
sys.path.append(new_directory)
##############
# UCE #
##############
from evaluate import AnndataProcessor
from accelerate import Accelerator
# python eval_single_anndata.py --adata_path "./data/10k_pbmcs_proc.h5ad" --dir "./" --model_loc "minwoosun/uce-100m"
script_name = "/home/user/app/eval_single_anndata.py"
args = ["--adata_path", input_file_path, "--dir", "/home/user/app/UCE/", "--model_loc", "minwoosun/uce-100m"]
command = ["python", script_name] + args
try:
result = subprocess.run(command, capture_output=True, text=True, check=True)
print(result.stdout)
print(result.stderr)
except subprocess.CalledProcessError as e:
print(f"Error executing command: {e}")
##############
# UMAP #
##############
UMAP = True
if (UMAP):
adata = sc.read_h5ad('/home/user/app/UCE/10k_pbmcs_proc_uce_adata.h5ad')
labels = pd.Categorical(adata.obs["cell_type"])
reducer = umap.UMAP(n_neighbors=15, min_dist=0.1, n_components=2, random_state=42)
embedding = reducer.fit_transform(adata.obsm["X_uce"])
plt.figure(figsize=(10, 8))
# Create the scatter plot
scatter = plt.scatter(embedding[:, 0], embedding[:, 1], c=labels.codes, cmap='Set1', s=50, alpha=0.6)
# Create a legend
handles = []
for i, cell_type in enumerate(labels.categories):
handles.append(plt.Line2D([0], [0], marker='o', color='w', label=cell_type,
markerfacecolor=plt.cm.Set1(i / len(labels.categories)), markersize=10))
plt.legend(handles=handles, title='Cell Type')
plt.title('UMAP projection of the data')
plt.xlabel('UMAP1')
plt.ylabel('UMAP2')
# Save plot to a BytesIO object
buf = BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
# Read the image from BytesIO object
img = plt.imread(buf, format='png')
else:
img = None
print("no image")
# this need to be changed based on data file name
output_file = '/home/user/app/UCE/10k_pbmcs_proc_uce_adata.h5ad'
return img, output_file
if __name__ == "__main__":
# Define Gradio inputs and outputs
file_input = gr.File(label="Upload a .h5ad single cell gene expression file")
species_input = gr.Dropdown(choices=["human", "mouse"], label="Select species")
image_output = gr.Image(type="numpy", label="UMAP of UCE Embeddings")
file_output = gr.File(label="Download embeddings")
# Create the Gradio interface
demo = gr.Interface(
fn=main,
inputs=[file_input, species_input],
outputs=[image_output, file_output],
title="UCE 100M Demo",
description="Upload a .h5ad single cell gene expression file, and get a UMAP scatter plot along with the UMAP coordinates in a CSV file."
)
demo.launch()
|