app.py

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__":   

    css = """
    body {background-color: black; color: white;}
    .gradio-container {background-color: black; color: white;}
    input, button, select, textarea {background-color: #333; color: white;}
    """

    with gr.Blocks(css=css) as demo:
        gr.Markdown(
            '''
            <div style="text-align:center; margin-bottom:20px;">
                <span style="font-size:3em; font-weight:bold; color: white;">UCE 100M Demo</span>
            </div>
            <div style="text-align:center; margin-bottom:10px;">
                <span style="font-size:1.5em; font-weight:bold; color: white;">Universal Cell Embeddings: Explore Single Cell Data</span>
            </div>
            <div style="text-align:center; margin-bottom:20px;">
                <a href="https://github.com/minwoosun/UCE">
                    <img src="https://badges.aleen42.com/src/github.svg" alt="GitHub" style="display:inline-block; margin-right:10px;">
                </a>
                <a href="https://www.biorxiv.org/content/10.1101/2023.11.28.568918v1">
                    <img src="https://img.shields.io/badge/bioRxiv-2023.11.28.568918-green?style=plastic" alt="Paper" style="display:inline-block; margin-right:10px;">
                </a>
            </div>
            <div style="text-align:left; margin-bottom:20px; color: white;">
                Upload a `.h5ad` single cell gene expression file and select the species (Human/Mouse). 
                The demo will generate UMAP projections of the embeddings and allow you to download the embeddings for further analysis.
            </div>
            <div style="margin-bottom:20px; color: white;">
                <ol style="list-style:none; padding-left:0;">
                    <li>1. Upload your `.h5ad` file</li>
                    <li>2. Select the species</li>
                    <li>3. View the UMAP scatter plot</li>
                    <li>4. Download the UMAP coordinates</li>
                </ol>
            </div>
            <div style="text-align:left; line-height:1.8; color: white;">
                Please consider citing the following paper if you use this tool in your research:
            </div>
            <div style="text-align:left; line-height:1.8; color: white;">
                Rosen, Y., Roohani, Y., Agarwal, A., Samotorčan, L., Tabula Sapiens Consortium, Quake, S. R., & Leskovec, J. Universal Cell Embeddings: A Foundation Model for Cell Biology. bioRxiv. https://doi.org/10.1101/2023.11.28.568918
            </div>
            '''
        )

        # 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")
    
        # Add the components and link to the function
        gr.Button("Run").click(
            fn=main, 
            inputs=[file_input, species_input], 
            outputs=[image_output, file_output]
        )
    
    demo.launch()