restructuring

.gitignore

@@ -9,3 +9,4 @@ wheels/
 # Virtual environments
 .venv
 .gradio/
+tmp_meta.yml

agents/__init__.py

@@ -0,0 +1,3 @@
+"""
+Agents for agent ontology.
+""" 

agents/query_ontology_db.py

@@ -0,0 +1,18 @@
+from smolagents import CodeAgent, LiteLLMModel
+from tools.fetch_ontology_tools import search_edam_ontology_by_search_term, get_edam_description_from_ontology_format_class
+
+model = LiteLLMModel(
+    model_id="ollama/devstral:latest",
+    #model_id="ollama/qwen3:0.6b",
+    api_base="http://localhost:11434",
+    temperature=0.0,
+    max_tokens=5000,
+)
+
+tool_list = [search_edam_ontology_by_search_term, get_edam_description_from_ontology_format_class]
+
+agent = CodeAgent(
+    tools=tool_list,
+    model=model,
+    additional_authorized_imports=["inspect", "json"]
+)

main.py

@@ -1,13 +1,10 @@
-from smolagents import CodeAgent, LiteLLMModel
-from smolagents.tools import ToolCollection
 import gradio as gr
-import requests
 import modal
 import sys
 import subprocess
 import time
-from .tools.meta_yml_tools import fetch_meta_yml,get_meta_yml_file, extract_tools_from_meta_json, extract_information_from_meta_json, extract_module_name_description,get_biotools_response
-
+from tools.meta_yml_tools import get_meta_yml_file, extract_tools_from_meta_json, extract_information_from_meta_json, extract_module_name_description, get_biotools_response
+from agents.query_ontology_db import agent
 # Define the custom image
 ollama_image = (
     modal.Image.debian_slim()
@@ -33,41 +30,6 @@ ollama_image = (
 
 # Initialize the Modal app with the custom image
 app = modal.App("agent-ontology", image=ollama_image)
-
-def chat_with_agent(message, history):
-    """ Function to handle chat messages and interact with the agent.
-    This function creates a new MCP connection for each request, allowing the agent to use tools from the MCP server.
-    """
-    try:
-        with ToolCollection.from_mcp(
-            {"url": "https://notredameslab-nf-ontology.hf.space/gradio_api/mcp/sse", "transport": "sse"},
-            trust_remote_code=True  # Acknowledge that we trust this remote MCP server
-        ) as tool_collection:
-
-
-            model = LiteLLMModel(
-                model_id="ollama/devstral:latest",
-                api_base="http://localhost:11434",
-            )
-
-            agent = CodeAgent(
-                tools=tool_collection.tools,
-                model=model,
-                additional_authorized_imports=["inspect", "json"]
-            )
-
-            additional_instructions = """
-            ADDITIONAL IMPORTANT INSTRUCTIONS:
-            use the tool "final_answer" in the code block to provide the answer to the user. Prints are only for debugging purposes. So, to give your results concatenate everything you want to print in a single "final_answer" call as such : final_answer(f"your answer here").
-            """
-
-            agent.system_prompt += additional_instructions
-
-            result = agent.run(message)
-            return str(result)
-            
-    except Exception as e:
-        return f"❌ Error: {e}\nType: {type(e).__name__}"
     
 def run_multi_agent(module_name): 
     meta_yml = get_meta_yml_file(module_name=module_name)
@@ -83,7 +45,17 @@ def run_multi_agent(module_name):
     """
     tool_name = "fastqc" # this would be the answer of the first agent
     meta_info = extract_information_from_meta_json(meta_file=meta_yml, tool_name=tool_name)
-    return(meta_info)
+    for input_tool in module_info["inputs"]:
+        for key, value in input_tool.items():
+            if key == "file":
+                result = agent.run(f"you are presentend with a file format for the type {key}, which is a {value['type']} and is described by the following description: '{value['description']}', search for the single best match out of possible matches in the edam ontology (formated as format_XXXX), and return the answer (a single ontology class) in a final_answer call such as final_answer(f'format_XXXX')")
+                print(result)
+    
+    # TODO: placeholder
+    # This is returning the original meta.yml, but it should return the modified one with the ontologies added
+    with open("tmp_meta.yml", "w") as fh:
+        fh.write(meta_info)
+    return meta_info, "tmp_meta.yml" # TODO: placeholder
 
 def run_interface():
     """ Function to run the agent with a Gradio interface.

pyproject.toml

@@ -14,4 +14,6 @@ dependencies = [
     "requests",
     "smolagents[litellm,mcp]>=1.17.0",
     "textblob>=0.19.0",
+    "aiohttp>=3.8.0",
+    "modal>=0.57.0",
 ]

tools/__init__.py

@@ -0,0 +1,3 @@
+"""
+Local tools for agent ontology.
+""" 

fetch_ontology_from_input.py → tools/fetch_ontology_tools.py

@@ -1,48 +1,5 @@
-from smolagents import CodeAgent, LiteLLMModel, tool
-from smolagents.tools import ToolCollection
-import gradio as gr
-import requests
-import yaml
-from owlready2 import get_ontology, default_world
-import logging
-
-def get_fastqc_meta_yaml():
-    """
-    Fetches the content of the FastQC meta.yml file from nf-core modules repository.
-    
-    Returns:
-        str: The content of the YAML file as a string
-    """
-    # Use the raw GitHub URL to get the file content directly
-    url = "https://raw.githubusercontent.com/nf-core/modules/master/modules/nf-core/fastqc/meta.yml"
-    
-    try:
-        response = requests.get(url)
-        response.raise_for_status()  # Raises an HTTPError for bad responses
-        return response.text
-    except requests.exceptions.RequestException as e:
-        print(f"Error fetching YAML file: {e}")
-        return None
-    
-def get_fastqc_input_yaml():
-    """
-    Fetches the content of the FastQC input.yml file from nf-core modules repository.
-    
-    Returns:
-        str: The content of the YAML file as a dictionary
-    """
-
-    yaml_content = get_fastqc_meta_yaml()
-    if yaml_content:
-        try:
-            # Parse the YAML content
-            return yaml.safe_load(yaml_content)["input"]
-        except yaml.YAMLError as e:
-            print(f"Error parsing YAML: {e}")
-            return None
-    else:
-        print("Failed to fetch the YAML file, returning None.")
-        return None
+from smolagents import tool
+from owlready2 import get_ontology
 
 
 def load_edam_ontology():
@@ -151,29 +108,3 @@ def get_edam_description_from_ontology_format_class(term_id: str) -> str:
             
     except AttributeError:
         return f"Term {term_id} not found in EDAM ontology"
-
-# Example usage:
-if __name__ == "__main__":
-    tool_list = [search_edam_ontology_by_search_term, get_edam_description_from_ontology_format_class]
-
-    model = LiteLLMModel(
-    model_id="ollama/devstral:latest",
-    #model_id="ollama/qwen3:0.6b",
-    api_base="http://localhost:11434",
-    temperature=0.0,
-    max_tokens=5000,
-    )
-
-    agent = CodeAgent(
-        tools=tool_list,
-        model=model,
-        additional_authorized_imports=["inspect", "json"]
-    )
-
-    print(agent.tools)
-    input_yaml = get_fastqc_input_yaml()
-    for input_tool in input_yaml[0]:
-        for key, value in input_tool.items():
-            if key != "meta":
-                result = agent.run(f"you are presentend with a file format for the type {key}, which is a {value['type']} and is described by the following description: '{value['description']}', search for the single best match out of possible matches in the edam ontology (formated as format_XXXX), and return the answer (a single ontology class) in a final_answer call such as final_answer(f'format_XXXX')")
-                print(result)

tools/meta_yml_tools.py

@@ -2,22 +2,6 @@ import requests
 import json
 import yaml
 
-# TODO: placeholder function
-def fetch_meta_yml(module_name):
-    # Adjust the URL or path to your actual source of nf-core modules
-    base_url = f"https://raw.githubusercontent.com/nf-core/modules/refs/heads/master/modules/nf-core/{module_name}/meta.yml"
-    try:
-        response = requests.get(base_url)
-        response.raise_for_status()
-        content = response.text
-
-        # Save for download
-        with open("meta.yml", "w") as f:
-            f.write(content)
-
-        return content, "meta.yml"
-    except Exception as e:
-        return f"Error: Could not retrieve meta.yml for module '{module_name}'\n{e}", None
 
 def get_meta_yml_file(module_name: str) -> dict:
     """