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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
from io import BytesIO
from huggingface_hub import hf_hub_download
def main(input_file_path, species):
# clone and cd into UCE repo
os.system('git clone https://github.com/minwoosun/UCE.git')
os.chdir('UCE')
##############
# 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()
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