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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
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/UCE/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
dir_path = '/home/user/app/UCE/'
model_loc = 'minwoosun/uce-100m'
print(input_file_path)
print(dir_path)
print(model_loc)
# Verify adata_path is not None
if input_file_path is None or not os.path.exists(input_file_path):
raise ValueError(f"Invalid adata_path: {input_file_path}. Please check if the file exists.")
# Construct the command
command = [
'python',
'/home/user/app/UCE/eval_single_anndata.py',
'--adata_path', input_file_path,
'--dir', dir_path,
'--model_loc', model_loc
]
# Print the command for debugging
print("Running command:", command)
print("---> RUNNING UCE")
result = subprocess.run(command, capture_output=True, text=True, check=True)
print(result.stdout)
print(result.stderr)
print("---> FINSIH UCE")
##############
# UMAP #
##############
UMAP = True
if (UMAP):
# Set output file path
file_name_with_ext = os.path.basename(input_file_path)
file_name = os.path.splitext(file_name_with_ext)[0]
output_file = "/home/user/app/UCE/" + f"{file_name}_uce_adata.h5ad"
adata = sc.read_h5ad(output_file)
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")
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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