Upload updated code

.gitignore

@@ -7,4 +7,8 @@ wheels/
 *.egg-info
 
 # Virtual environments
+.env
 .venv
+
+# Ruff configuration file
+ruff.toml

README.md

@@ -11,43 +11,41 @@ tags:
 - mcp-server-track
 ---
 
-# BioMedNorm MCP Server
-
-A MCP server for extracting and normalizing domain-specific entities from biomedical text. We leverage OpenAI LLMs to identify entities and match them to standardized terminology.
+A MCP server for extracting and normalizing domain-specific entities from biomedical text. We utilize LLMs (here, from OpenAI) to identify entities and map them to standardized terminology or ontologies.
 
 ## Motivation
 
-Biomedical text normalization addresses a critical challenge in healthcare informatics: approximately 80% of electronic health record (EHR) data exists as unstructured medical text. Such text often contains abbreviations, misspellings, and non-standardized terminology, creating barriers to effective data utilization. This variability hinders leveraging clinical narratives for:
+Approximately 80% of electronic health record (EHR) data exists as unstructured medical text. Such text often contains abbreviations, misspellings, and non-standardized terminology, creating barriers to effective data utilization. This variability hinders progress in areas such as:
 
 - **Clinical decision support** at the point of care
 - **Patient comprehension** of their own medical records
 - **Biomedical research** including cohort identification and pharmacovigilance
 
-By implementing named entity recognition and normalization to controlled vocabularies like SNOMED-CT, our MCP server enables downstream applications to process biomedical text with greater accuracy, bridging the gap between natural clinical language and structured data requirements of modern healthcare systems.
+By implementing named entity recognition (NER) and normalization to standardized vocabularies (ontologies), our MCP server facilitates the extraction of structured data from diverse biomedical texts. This enhances the accuracy and efficiency of applications in clinical settings and research, bridging the gap between complex natural language and the structured data requirements of modern biomedical systems.
 
 ## Installation
 
-This project uses `uv` from Astral for dependency management. Follow these steps to set up the project:
-
-### Clone the repository
+This project uses `uv` from Astral for dependency management. Make sure you have `uv` installed. If not, install it with the following command:
 
 ```bash
-git clone https://github.com/yourusername/entity-extraction-mcp
-cd entity-extraction-mcp
+curl -LsSf https://astral.sh/uv/install.sh | sh
 ```
 
-### Set up Python environment
+Follow these steps to set up the project:
 
-The project includes a .python-version file that specifies the required Python version. Make sure you have uv installed:
+### Clone the repository
 
 ```bash
-# Install uv if you don't have it already
-curl -LsSf https://astral.sh/uv/install.sh | sh
+git clone https://huggingface.co/spaces/Agents-MCP-Hackathon/BioMedNorm-MCP-Server
 ```
 
+### Set up Python environment
+
+The project includes a .python-version file to specify the required Python version. Ensure you are using the correct version by setting it up with your Python environment manager (e.g., `pyenv`).
+
 ### Install dependencies
 
-The project dependencies are defined in `pyproject.toml`. Install them with:
+The project dependencies are defined in the `pyproject.toml` file. To install them, run:
 
 ```bash
 uv pip install -e .
@@ -55,7 +53,7 @@ uv pip install -e .
 
 ### Set up environment variables
 
-The project **requires** an OpenAI API key, which should be stored in a .env file.
+The project **requires** an OpenAI API key, which should be stored in a `.env` file.
 
 ## Running the application
 
@@ -68,7 +66,7 @@ uv run app.py
 This command ensures that:
 
 - All project dependencies are correctly installed
-- The environment variables from .env are loaded
+- The environment variables from `.env` are loaded
 - The application runs in the proper environment
 
 After starting the server, you can access:
@@ -100,6 +98,6 @@ Here is a [video link](./MCPServer_test.webm) that shows the MCP server in actio
 
 Our biomedical text normalization MCP server can be enhanced in several ways:
 
-- Expanded Entity Coverage: Extend beyond the current entity types (Disease, Tissue, Cell Type) to include medications, procedures, laboratory tests, and genomic entities.
+- Expanded Entity Coverage: Extend beyond the current entity types (Disease, Tissue, Cell Type) to include other entities such as library processing protocols, cell lines, disease status, to name a few.
 - User Feedback Loop: Implement a mechanism for users to correct normalization errors, creating a dataset for continuous model improvement.
 - Multilingual Support: Expand capabilities to handle medical text in languages beyond English.

app.py

@@ -13,7 +13,7 @@ from openai_utils import ask_openai
 NER_PROMPT = """
   You are an expert annotator of biomedical text.
 
-  Annotate/Identify/Extract all {entity}s in this text: {text}
+  Annotate/Extract all {entity}(s) in this text: {text}
 
   Instructions:
   1. If no such entity or entities are found, then **return exactly**: Not Found
@@ -60,7 +60,9 @@ async def extract_entities(paragraph: str, target_entity: str) -> Optional[list[
   return extracted_entities
 
 
-async def normalize_entities(raw_terms: list[str]) -> list[Optional[str]]:
+async def normalize_entities(
+  raw_terms: list[str],
+) -> list[dict[Optional[str], Optional[str]]]:
   """
   Normalize a list of raw terms to the most appropriate standard terms from a list
   of candidates.
@@ -71,20 +73,28 @@ async def normalize_entities(raw_terms: list[str]) -> list[Optional[str]]:
     raw_terms (list[str]): List of unnormalized terms, typically from extract_entities().
 
   Returns:
-    list[Optional[str]]: List of best matching standard terms in the same order as the
-    input terms. An entry may be None if normalization failed.
+    list[dict[Optional[str], Optional[str]]]: A list of dictionaries, where each dictionary contains
+    the best matching normalized term (key) and its corresponding URI (value). If normalization fails
+    for a term, the dictionary will have a None value for both the key and URI.
   """
 
   # Do normalization for each entity
-  async def process_single_entity(raw_term: str) -> Optional[str]:
+  async def process_single_entity(raw_term: str) -> dict[Optional[str], Optional[str]]:
     # Generate candidates specifically for this entity
     # If the oaklib function is not async, wrap it with run_in_executor
-    candidate_std_terms = await asyncio.to_thread(get_candidates, raw_term)
+    candidates = await asyncio.to_thread(get_candidates, raw_term)
+    candidate_std_terms = [candidates for _, candidates in candidates]
 
     # Now use these entity-specific candidates for the OpenAI call
     prompt = RAG_PROMPT.format(entity=raw_term, top_k_preds=candidate_std_terms)
     result = await ask_openai(prompt, usage="rag")
-    return result
+    if result is not None:
+      # Find the URI for the std term returned by LLM
+      result_URI = next((URI for URI, term in candidates if term == result), None)
+    else:
+      result_URI = None
+
+    return {result: result_URI}
 
   # Process all entities in parallel
   tasks = [process_single_entity(entity) for entity in raw_terms]
@@ -95,7 +105,7 @@ async def normalize_entities(raw_terms: list[str]) -> list[Optional[str]]:
 
 async def extract_and_normalize(
   paragraph: str, target_entity: str
-) -> list[Optional[str]]:
+) -> list[dict[Optional[str], Optional[str]]]:
   """
   Extract entities from a paragraph and normalize them in one operation.
 
@@ -104,9 +114,11 @@ async def extract_and_normalize(
     target_entity: The type of entity to extract and normalize.
 
   Returns:
-    list[Optional[str]]: List of best matching standard terms in the same order as the
-    input terms. An entry may be None if normalization failed.
+    list[dict[Optional[str], Optional[str]]]: A list of dictionaries, where each dictionary contains
+    the best matching normalized term (key) and its corresponding URI (value). If normalization fails
+    for a term, the dictionary will have a None value for both the key and URI.
   """
+  target_entity = target_entity.lower()
   extracted_entities = await extract_entities(paragraph, target_entity)
   if not extracted_entities or len(extracted_entities) == 0:
     return []
@@ -115,35 +127,85 @@ async def extract_and_normalize(
   return result
 
 
-# Create a visually appealing Gradio app
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-  gr.Markdown("# BioMedNorm: Entity Extraction & Normalization")
-  gr.Markdown(
-    "Enter text and specify the entity type to extract and normalize entities."
+def toggle_custom_box(selected: str):
+  show = selected == "Custom"
+  return (
+    gr.Textbox(visible=show, interactive=show),
+    gr.Markdown(
+      "**Warning:** This tool is optimized and tested for Disease, Tissue, and Cell Type entities. "
+      "While you can input custom entities, results may vary in accuracy and reliability.",
+      visible=show,
+    ),
   )
 
+
+# Create the Gradio app
+app_theme = gr.themes.Soft(
+  primary_hue="teal",
+  secondary_hue="green",
+)
+
+with gr.Blocks(theme=app_theme) as demo:
+  gr.Markdown("""
+    # 🧬 BioMedNorm: Entity Extraction & Normalization
+
+    Welcome to the BioMedNorm MCP Server. 
+              
+    This server is designed to be used by LLMs to extract and standardize biological 
+    entities (like disease, tissue) from biomedical text.
+
+    Enter the text below, specify the entity type to extract and normalize entities, and voila!
+    """)
+
+  # Two-column layout using Row + Column
   with gr.Row():
-    with gr.Column(scale=3):
+    with gr.Column():
       paragraph = gr.Textbox(
         label="Text Input",
         placeholder="Enter paragraph here...",
-        lines=5,
+        lines=8,
         info="Enter the biomedical text for entity extraction.",
       )
-    with gr.Column(scale=1):
       target_entity = gr.Dropdown(
-        ["Disease", "Tissue", "Cell Type"],
+        ["Disease", "Tissue", "Cell Type", "Custom"],
         label="Entity Type",
         value="Disease",
+        # allow_custom_value=True,
         info="Select the type of entity you want to extract and normalize from the text.",
       )
+      custom_entity = gr.Textbox(
+        label="Custom Entity",
+        placeholder="Enter custom entity type here",
+        visible=False,
+        interactive=True,
+        info="Enter your custom entity type if 'Custom' is selected.",
+      )
+      warning = gr.Markdown(
+        visible=False  # Initially hidden
+      )
+      normalize_btn = gr.Button("Normalize", variant="primary")
 
-  normalize_btn = gr.Button("Normalize", variant="primary")
-
-  with gr.Row():
     with gr.Column():
       output = gr.JSON(label="Normalized Entities")
 
+  # Update custom_entity and warning visibility based on dropdown selection
+  target_entity.change(
+    fn=toggle_custom_box, inputs=target_entity, outputs=[custom_entity, warning]
+  )
+  # # Event to show the custom entity field only when 'Custom' is selected
+  # target_entity.change(
+  #   lambda x: custom_entity.visible if x == "Custom" else False,
+  #   target_entity,
+  #   custom_entity,
+  # )
+
+  # # Add a warning message about custom entities
+  # with gr.Row():
+  #   warning = gr.Markdown(
+  #     "**Note:** This tool is optimized and tested for Disease, Tissue, and Cell Type entities. "
+  #     "While you can input custom entities, results may vary in accuracy and reliability."
+  #   )
+
   # Add a loading indicator
   with gr.Row():
     status = gr.Markdown("")

demo_app.py

@@ -0,0 +1,27 @@
+import gradio as gr
+
+
+def letter_counts(word: str, letter: str):
+  """
+  Count the occurrences of a word in a letter.
+
+  Args:
+    word: The word or phrase to analyze.
+    letter: The letter to count occurrences of.
+  
+  Returns:
+    The number of times the letter appears in the word.
+  """
+  assert len(letter) == 1, "The letter should only be one character."
+  return word.lower().count(letter.lower())
+
+
+demo = gr.Interface(
+  fn=letter_counts,
+  inputs=['text', 'text'],
+  outputs='number',
+  title="Letter Counter",
+  description="Count how many times a latter appears in a word."
+)
+
+demo.launch(mcp_server=True)

mcp_client.py

@@ -0,0 +1,35 @@
+"""
+Use Gradio as a MCP Client to connect to the BioMedNorm MCP server.
+
+Follows the MCP course docs: https://huggingface.co/learn/mcp-course/en/unit2/gradio-client
+"""
+
+import gradio as gr
+import os
+
+from smolagents import InferenceClientModel, CodeAgent, MCPClient
+
+mcp_client = None
+
+try:
+  mcp_client = MCPClient(
+    {
+      "url": "https://agents-mcp-hackathon-biomednorm-mcp-server.hf.space/gradio_api/mcp/sse"
+    } 
+  )
+  tools = mcp_client.get_tools()
+
+  model = InferenceClientModel(provider="nebius", token=os.getenv("HF_TOKEN"))
+  agent = CodeAgent(tools=[*tools], model=model)
+
+  demo = gr.ChatInterface(
+    fn=lambda message, history: str(agent.run(message)),
+    type="messages",
+    title="BioMed Normalization Agent",
+    description="I can extract and standardize entities from biomedical text. Try me out!!!",
+  )
+
+  demo.launch()
+finally:
+  if mcp_client is not None:
+    mcp_client.disconnect()

oaklib_utils.py

@@ -2,15 +2,13 @@
 Retrieve top k candidate standard terms for normalization using oaklib.
 """
 
-# import argparse
-
 from oaklib import get_adapter
 from oaklib.datamodels.search import SearchConfiguration
 
 adapter = get_adapter("ols:")
 
 
-def get_candidates(term: str, top_k: int = 10) -> list[str]:
+def get_candidates(term: str, top_k: int = 10) -> list[tuple[str, str]]:
   """
   Get top k candidates for RAG.
   """
@@ -18,38 +16,9 @@ def get_candidates(term: str, top_k: int = 10) -> list[str]:
   cfg = SearchConfiguration(limit=top_k)
 
   results = adapter.basic_search(term, config=cfg)
-  labels = list(adapter.labels(results))  # list of tuples of ids and labels
+  labels = list(adapter.labels(results))  # list of tuples of CURIE ids and labels
 
-  # print(f"## Query: {term} -> {labels}")
-  candidates = list(label for _, label in labels)
+  # Keep both URI and standard terms for "explainable" output
+  # But first convert CURIE IDs to URIs
+  candidates = list((adapter.curie_to_uri(curie), term) for (curie, term) in labels)
   return candidates
-
-
-# def main():
-#   parser = argparse.ArgumentParser(
-#     description="Fetch top-K candidate passages for a given term (RAG)"
-#   )
-#   parser.add_argument(
-#     "term", type=str, help="The query term or prompt for which to retrieve candidates"
-#   )
-#   parser.add_argument(
-#     "-k",
-#     "--top_k",
-#     type=int,
-#     default=10,
-#     help="Number of top candidates to return (default: 10)",
-#   )
-
-#   args = parser.parse_args()
-
-#   # Call your function
-#   candidates = get_candidates(args.term)
-
-#   print(f"\nTerm: {args.term!r}")
-#   print(f"Top {args.top_k} candidates:")
-#   for i, cand in enumerate(candidates, start=1):
-#     print(f"  {i:2d}. {cand}")
-
-
-# if __name__ == "__main__":
-#   main()

pyproject.toml

@@ -1,11 +1,20 @@
 [project]
 name = "biomednorm-mcp-server"
-version = "0.1.0"
-description = "Add your description here"
+version = "0.2.0"
+description = "Extract and normalize entities from biomedical text using LLMs."
+authors = [
+  {name = "Rohan Karthikeyan", email = "[email protected]"}
+]
+maintainers = [
+  {name = "Rohan Karthikeyan", email = "[email protected]"}
+]
 readme = "README.md"
+
 requires-python = ">=3.13"
+
 dependencies = [
     "oaklib>=0.6.23",
     "openai>=1.84.0",
     "python-dotenv>=1.1.0",
+    "smolagents[mcp]>=1.18.0",
 ]

uv.lock

@@ -99,12 +99,13 @@ wheels = [
 
 [[package]]
 name = "biomednorm-mcp-server"
-version = "0.1.0"
+version = "0.2.0"
 source = { virtual = "." }
 dependencies = [
     { name = "oaklib" },
     { name = "openai" },
     { name = "python-dotenv" },
+    { name = "smolagents", extra = ["mcp"] },
 ]
 
 [package.metadata]
@@ -112,6 +113,7 @@ requires-dist = [
     { name = "oaklib", specifier = ">=0.6.23" },
     { name = "openai", specifier = ">=1.84.0" },
     { name = "python-dotenv", specifier = ">=1.1.0" },
+    { name = "smolagents", extras = ["mcp"], specifier = ">=1.18.0" },
 ]
 
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 ]
 
+[[package]]
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+wheels = [
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+[[package]]
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+version = "2025.5.1"
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+wheels = [
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+[[package]]
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 version = "1.0.9"
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