app.py

import gradio as gr
import sqlite3
import json
import numpy as np
from numpy.linalg import norm
from huggingface_hub import hf_hub_download
from sentence_transformers import SentenceTransformer
import os

# Get Hugging Face Token from Environment Variables
HF_TOKEN = os.environ.get("HF_TOKEN")
if not HF_TOKEN:
    raise ValueError("Missing Hugging Face API token. Please set HF_TOKEN as an environment variable in Hugging Face Secrets.")

# Load the Nomic-Embed Model from Hugging Face
EMBEDDING_MODEL = "nomic-ai/nomic-embed-text-v1.5"
embedder = SentenceTransformer(EMBEDDING_MODEL, trust_remote_code=True)

# Download database from Hugging Face Datasets if it does not exist
db_filename = "hpo_genes.db"
db_repo = "UoS-HGIG/hpo_genes"
db_path = os.path.join(os.getcwd(), db_filename)

if not os.path.exists(db_path):
    db_path = hf_hub_download(repo_id=db_repo, filename=db_filename, repo_type="dataset", use_auth_token=HF_TOKEN)


def find_best_hpo_match(finding, region, threshold):
    query_text = f"{finding} in {region}" if region else finding
    query_embedding = embedder.encode(query_text)

    conn = sqlite3.connect(db_path)
    cursor = conn.cursor()
    cursor.execute("SELECT hpo_id, hpo_name, embedding FROM hpo_embeddings")

    best_match, best_score = None, -1

    for hpo_id, hpo_name, embedding_str in cursor.fetchall():
        hpo_embedding = np.array(json.loads(embedding_str))
        similarity = np.dot(query_embedding, hpo_embedding) / (norm(query_embedding) * norm(hpo_embedding))

        if similarity > best_score:
            best_score = similarity
            best_match = {"hpo_id": hpo_id, "hpo_term": hpo_name}

    conn.close()
    return best_match if best_score >= threshold else None


def get_genes_for_hpo(hpo_id):
    conn = sqlite3.connect(db_path)
    cursor = conn.cursor()
    cursor.execute("SELECT genes FROM hpo_gene WHERE hpo_id = ?", (hpo_id,))
    result = cursor.fetchone()
    conn.close()
    return result[0].split(", ") if result else []


def hpo_mapper_ui(finding, region, threshold):
    if not finding:
        return "Please enter a pathological finding.", "", ""

    match = find_best_hpo_match(finding, region, threshold)

    if match:
        genes = get_genes_for_hpo(match["hpo_id"])
        return match["hpo_id"], match["hpo_term"], ", ".join(genes)

    return "No match found.", "", ""


demo = gr.Interface(
    fn=hpo_mapper_ui,
    inputs=[
        gr.Textbox(label="Pathological Finding"),
        gr.Textbox(label="Anatomical Region (optional)"),
        gr.Slider(0.0, 1.0, step=0.01, value=0.76, label="Similarity Threshold")
    ],
    outputs=[
        gr.Textbox(label="HPO ID"),
        gr.Textbox(label="HPO Term"),
        gr.Textbox(label="Disease genes annotated as being associated with this HPO term"),
    ],
    title="Human Phenotype Ontology (HPO) Mapper",
    description=(
        'Enter a pathological finding (e.g., "chronic inflammation") and anatomical region '
        '(e.g., "terminal ileum") to map it to the closest Human Phenotype Ontology (HPO) '
        'term and retrieve genes annotated as being associated with this HPO term.\n\n'
        '**References:**\n'
        'Kadhim, A. Z., Green, Z., Nazari, I., Baker, J., George, M., Heinson, A., Stammers, M., Kipps, C., Beattie, R. M., Ashton, J. J., & Ennis, S. (2025).\n'
        'Application of generative artificial intelligence to utilise unstructured clinical data for acceleration of inflammatory bowel disease research.\n'
        '*medRxiv*. [https://doi.org/10.1101/2025.03.07.25323569](https://doi.org/10.1101/2025.03.07.25323569)\n\n'
        'Gargano, M. A., Matentzoglu, N., Coleman, B., Addo-Lartey, E. B., Anagnostopoulos, A. V., Anderton, J., Avillach, P., Bagley, A. M., Bakštein, E., Balhoff, J. P., Baynam, G., Bello, S. M., Berk, M., Bertram, H., Bishop, S., Blau, H., Bodenstein, D. F., Botas, P., Boztug, K., Čady, J., … Robinson, P. N. (2024)\n' 
        'The Human Phenotype Ontology in 2024: phenotypes around the world.\n' 
        '*Nucleic Acids Research* [https://doi.org/10.1093/nar/gkad1005](https://doi.org/10.1093/nar/gkad1005)\n\n'
        'HPO to gene mappings obtained from [Jax](https://hpo.jax.org/data/annotations)'
    )
)

if __name__ == "__main__":
    demo.launch()