Update app.py

app.py

@@ -6,28 +6,14 @@ from numpy.linalg import norm
 from huggingface_hub import hf_hub_download
 from sentence_transformers import SentenceTransformer
 import os
-import subprocess
-from huggingface_hub import login
 
-
-# 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.")
-
-
-# Set Hugging Face API key for OntoGPT
-subprocess.run(["runoak", "set-apikey", "-e", "huggingface-key", HF_TOKEN], check=True)
-
+    raise ValueError("Missing Hugging Face API token. Please set HF_TOKEN as an environment variable.")
 
-# Define OntoGPT model
-ONTOGPT_MODEL = "huggingface/WizardLM/WizardCoder-Python-34B-V1.0"
-
-# Load the Nomic-Embed Model
 EMBEDDING_MODEL = "nomic-ai/nomic-embed-text-v1.5"
 embedder = SentenceTransformer(EMBEDDING_MODEL, trust_remote_code=True)
 
-# Download database from Hugging Face if not exists
 db_filename = "hpo_genes.db"
 db_repo = "UoS-HGIG/hpo_genes"
 db_path = os.path.join(os.getcwd(), db_filename)
@@ -35,94 +21,73 @@ 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):
-    """Finds the best HPO match using semantic similarity."""
-    query_text = f"{finding} in {region}"
-    query_embedding = embedder.encode(query_text)
+    query = f"{finding} {region}" if region else finding
+    query_embedding = embedder.encode(query)
 
     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}
-    
+            best_match = {"hpo_id": hpo_id, "hpo_name": hpo_name}
+
     conn.close()
-    return best_match if best_score > threshold else None
 
+    return best_match if best_score >= threshold else None
 
 def get_genes_for_hpo(hpo_id):
-    """Retrieves associated genes for a given 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 []
 
+    return result[0].split(',') if result else []
 
-def get_hpo_for_finding(finding, region, threshold):
-    """Finds the best HPO term and retrieves associated genes."""
-    hpo_match = find_best_hpo_match(finding, region, threshold)
-    if hpo_match:
-        hpo_match["genes"] = get_genes_for_hpo(hpo_match["hpo_id"])
-    else:
-        hpo_match = {"hpo_id": "NA", "hpo_term": "NA", "genes": []}
-    return hpo_match
-
+def hpo_mapper_ui(finding, region, threshold):
+    if not finding:
+        return "Please enter a clinical pathological finding.", "", ""
 
-def run_ontogpt(finding, region):
-    """Runs OntoGPT to extract information."""
-    input_text = f"{finding} in {region}"
-    result = subprocess.run([
-        "ontogpt", "complete", "-m", ONTOGPT_MODEL, "-i", input_text
-    ], capture_output=True, text=True)
-    return result.stdout.strip()
+    match = find_best_hpo_match(finding, region, threshold)
+    if match:
+        genes = get_genes_for_hpo(match['hpo_id'])
+        return match['hpo_id'], match['hpo_name'], ", ".join(genes)
+    else:
+        return "", "No match found.", ""
 
+image_path = "https://huggingface.co/UoS-HGIG/MIMIC/resolve/main/images/hpo.png"
 
-def hpo_mapper_ui(finding, region, threshold):
-    """Function for Gradio UI to get HPO mappings and OntoGPT results."""
-    if not finding or not region:
-        return "Please enter both finding and region.", "", ""
-    
-    hpo_result = get_hpo_for_finding(finding, region, threshold)
-    ontogpt_output = run_ontogpt(finding, region)
-    
-    return hpo_result["hpo_id"], hpo_result["hpo_term"], ", ".join(hpo_result["genes"]), ontogpt_output
-
-# Create Gradio UI
 demo = gr.Interface(
     fn=hpo_mapper_ui,
     inputs=[
-        gr.Textbox(label="Finding"),
-        gr.Textbox(label="Region"),
-        gr.Slider(minimum=0.5, maximum=1.0, step=0.01, value=0.74, label="Threshold")
+        gr.Textbox(label="Pathological Finding"),
+        gr.Textbox(label="Anatomical Region (optional)"),
+        gr.Slider(0.5, 1.0, 0.01, value=0.74, label="Similarity Threshold")
     ],
     outputs=[
         gr.Textbox(label="HPO ID"),
         gr.Textbox(label="HPO Term"),
-        gr.Textbox(label="Associated Genes"),
-        gr.Textbox(label="OntoGPT Output")
+        gr.Textbox(label="HPO-associated Genes")
     ],
-    title="HPO Mapper with OntoGPT",
+    title="Human Phenotype Ontology (HPO) Mapper",
     description=(
-        "Enter a clinical finding and anatomical region to get the best-matching HPO term and associated genes, "
-        "now enriched with OntoGPT-generated ontology-based descriptions.\n\n"
-        "### Reference:\n"
-        "**Application of Generative Artificial Intelligence to Utilise Unstructured Clinical Data for Acceleration of Inflammatory Bowel Disease Research**\n"
-        "Alex Z Kadhim, Zachary Green, Iman Nazari, Jonathan Baker, Michael George, Ashley Heinson, Matt Stammers, Christopher Kipps, R Mark Beattie, James J Ashton, Sarah Ennis\n"
-        "medRxiv 2025.03.07.25323569; [DOI: 10.1101/2025.03.07.25323569](https://doi.org/10.1101/2025.03.07.25323569)"
+        "![Workflow](https://huggingface.co/UoS-HGIG/MIMIC/blob/main/images/hpo.png)\n\n"
+        "Enter a pathological finding and optionally a region to map it to the closest Human Phenotype Ontology (HPO) term and retrieve associated genes.\n\n"
+        "**Reference:**\n"
+        "Application of Generative Artificial Intelligence to Utilise Unstructured Clinical Data for Acceleration of Inflammatory Bowel Disease Research\n"
+        "Alex Z Kadhim, Zachary Green, Iman Nazari, Jonathan Baker, Michael George, Ashley Heinson, Matt Stammers, Christopher M Kipps, R Mark Beattie, James J Ashton, Sarah Ennis\n"
+        "medRxiv 2025.03.07.25323569; doi: [https://doi.org/10.1101/2025.03.07.25323569](https://doi.org/10.1101/2025.03.07.25323569)\n"
+        "HPO to gene mappings obtained from [Jax](https://hpo.jax.org/data/annotations)"
     )
-
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()