#!/usr/bin/env python
import streamlit as st

from constants import MODELS

st.set_page_config(layout="wide")

st.markdown(
    """
    # Download & Credits

    ## 1. Preprocessing pipelines

    - Downloading datasets: [nf-core/fetchngs (revision 1.10.0)](https://github.com/nf-core/fetchngs)
    - Aligning datasets: [brickmanlab/scrnaseq (revision: feature/smartseq)](https://github.com/brickmanlab/scrnaseq)
    - **Ensembl Genomes (models <= v1.1)**
        - Mouse: GRCm38 v102
        - Human: GRCh38 v110

    ## 2. Codebase
    
    - Data analysis: [brickmanlab/proks-salehin-et-al](https://github.com/brickmanlab/proks-salehin-et-al)
    - Web portal on HF: [brickmanlab/hf-preimplantation-portal](https://huggingface.co/spaces/brickmanlab/hf-preimplantation-portal/tree/main)
    - Web portal (deprecated): [brickmanlab/preimplantation-portal](https://github.com/brickmanlab/preimplantation-portal)
    
    ## 3. Raw and normalized counts
    
    Raw counts are stored in `layers['counts']` and normalized counts are stored in `.X`. 
    
    - **models <= v1.1**
        - [mouse](https://zenodo.org/records/13749348/files/01_mouse_reprocessed.h5ad)
        - [human](https://zenodo.org/records/13749348/files/32_human_adata.h5ad)

    ## 4. scVI/scANVI models

    Trained models with parameters were uploaded to [Hugging Face](https://huggingface.co/brickmanlab/preimplantation-models).
    """
)

text = ""
for specie in MODELS:
    text += f"- **{specie}**: "
    for version in MODELS[specie]:
        url = (
            f"https://huggingface.co/brickmanlab/{specie.lower()}-scanvi/tree/{version}"
        )
        text += f"[{version}]({url}), "
    text = text[:-2] + "\n"
st.markdown(text)

st.markdown(
    """
    ## 5. Credit

    > [!TIP]
    > Proks, M., Salehin, N. & Brickman, J.M. Deep learning-based models for preimplantation mouse and human embryos based on single-cell RNA sequencing. Nat Methods 22, 207–216 (2025). https://doi.org/10.1038/s41592-024-02511-3
    """
)