app.py

import gradio as gr
import pandas as pd
import numpy as np
import umap
import json
import matplotlib.pyplot as plt
import os
# import tempfile
import scanpy as sc
# import argparse
import subprocess
import sys
# from evaluate import AnndataProcessor
# from accelerate import Accelerator
from io import BytesIO
from sklearn.linear_model import LogisticRegression
from huggingface_hub import hf_hub_download


def load_and_predict_with_classifier(x, model_path, output_path, save):

    # Load the model parameters from the JSON file
    with open(model_path, 'r') as f:
        model_params = json.load(f)

    # Reconstruct the logistic regression model
    model_loaded = LogisticRegression(multi_class='multinomial', solver='lbfgs', max_iter=1000)
    model_loaded.coef_ = np.array(model_params["coef"])
    model_loaded.intercept_ = np.array(model_params["intercept"])
    model_loaded.classes_ = np.array(model_params["classes"])

    # output predictions
    y_pred = model_loaded.predict(x)

    # Convert the array to a Pandas DataFrame
    if save:
        df = pd.DataFrame(y_pred, columns=["predicted_cell_type"])
        df.to_csv(output_path, index=False, header=False)
    
    return y_pred


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")

    ################################
    #   Cell-type classification   #
    ################################

    
    # Set output file path
    file_name_with_ext = os.path.basename(input_file_path)
    file_name = os.path.splitext(file_name_with_ext)[0]
    pred_file = "/home/user/app/UCE/" + f"{file_name}_predictions.csv"
    model_path = hf_hub_download(repo_id="minwoosun/uce-misc", filename="tabula_sapiens_v1_logistic_regression_model_weights.json")

    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)
    x = adata.obsm['X_uce']

    y_pred = load_and_predict_with_classifier(x, model_path, pred_file, save=True)
    
    ##############
    #    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, pred_file


if __name__ == "__main__":   

    css = """
    body {background-color: white; color: black;}
    .gradio-container {background-color: white; color: black;}
    .gr-file, .gr-image {background-color: #f0f0f0; color: black; border-color: black;}
    """

    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: black;">UCE 100M Demo 🦠</span>
            </div>
            <div style="text-align:center; margin-bottom:10px;">
                <span style="font-size:1.5em; font-weight:bold; color: black;">Universal Cell Embeddings: Zero-Shot Cell-Type Classification in Action!</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>
                <a href="https://colab.research.google.com/drive/1opud0BVWr76IM8UnGgTomVggui_xC4p0?usp=sharing">
                    <img src="https://jupyter.org/assets/logos/rectanglelogo-greytext-orangebody-greymoons.svg" alt="JupyterLab" style="display:inline-block; margin-right:10px; width:50px; height:auto;">
                </a>
            </div>
            <div style="text-align:left; margin-bottom:20px; color: black;">
                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: black;">
                <ol style="list-style:none; padding-left:0;">
                    <li>1. Upload your `.h5ad` file</li>
                    <li>2. Select the species</li>
                    <li>3. Click "Run" to 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: black;">
                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: black;">
                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")
        run_button = gr.Button("Run")
        
        # Arrange UMAP plot and file output side by side
        with gr.Row():
            image_output = gr.Image(type="numpy", label="UMAP of UCE Embeddings")
            file_output = gr.File(label="Download embeddings")
            pred_output = gr.File(label="Download predictions")
    
        # Add the components and link to the function
        run_button.click(
            fn=main, 
            inputs=[file_input, species_input], 
            outputs=[image_output, file_output, pred_output]
        )

    demo.launch()