app.py

from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool, Tool
import datetime
import requests
import pytz
import yaml
import json
from tools.final_answer import FinalAnswerTool

from Gradio_UI import GradioUI

from smolagents import Tool
from epmc_xml import fetch
import requests
import json

class EuropePMCCitationsTool(Tool):
    name = "europepmc_citation_downloader"
    description = """
    This tool queries the Europe PMC API to retrieve citations for a given PMCID.
    It returns a list of cited PMCID strings from the citation list.
    """
    inputs = {
        "pmcid": {
            "type": "string",
            "description": "the PMCID to query (e.g., 'PMC7779037')",
        }
    }
    output_type = "array"

    def forward(self, pmcid: str):
        url = f"https://www.ebi.ac.uk/europepmc/webservices/rest/PMC/{pmcid}/citations?page=1&pageSize=1000&format=json"
        
        headers = {
            'Content-Type': 'application/json',
        }
        
        response = requests.get(url, headers=headers)
        if response.status_code != 200:
            raise Exception(f"Error querying Europe PMC API: {response.status_code}")
        
        data = json.loads(response.text)
        citations = data.get("citationList", {}).get("citation", [])
        
        # Extract PMCID strings from citations
        pmcid_list = [str(citation.get("id", "")) for citation in citations]
        
        return pmcid_list


class EuropePMCArticleTextTool(Tool):
    name = "europepmc_fulltext_downloader"
    description = """
    This is a tool that fetches the full text of an article from Europe PMC.
    It takes a PMCID (format PMC\d+) as input and returns the full text of the article as a string.
    Articles can only be fetched if they are open access; returns emptty string if article is not open access.
    """
    inputs = {
        "pmcid": {
            "type": "string",
            "description": "the PMCID of the article to fetch (e.g., 'PMC7779037')",
        }
    }
    output_type = "string"

    def forward(self, pmcid: str):
        # Fetch the article using epmc_xml
        try:
            article = fetch.article(pmcid)
        
            # Extract the full text from the article object
            full_text = article.get_body()
        except:
            full_text = ""#f"The article {pmcid} does not appear to be open access."
        
        
        # Return the full text as a string
        return full_text


class PMID2PMCIDConverter(Tool):
    name = "pmid_2_pmcid_converter"
    description = """
    This tool converts the pmids (all numbers) to pmcids (format PMC\d+).
    Use this to convert pmids reported by the citation tool to pmcids needed by the fulltext downloader
    """
    inputs = {
        "pmid": {
            "type": "string",
            "description": "the PMID to query (e.g., '36033386')",
        }
    }
    output_type = "string"

    def forward(self, pmid):
        url = f"https://www.ncbi.nlm.nih.gov/pmc/utils/idconv/v1.0?ids={pmid}&versions=no&format=json"

        headers = {
            'Content-Type': 'application/json',
        }
        
        response = requests.get(url, headers=headers)
        if response.status_code != 200:
            raise Exception(f"Error querying NCBI API: {response.status_code}")
        
        data = json.loads(response.text)    
        pmcid = data.get("records", {})[0].get("pmcid", [])
        
        return pmcid


@tool
def get_current_time_in_timezone(timezone: str) -> str:
    """A tool that fetches the current local time in a specified timezone.
    Args:
        timezone: A string representing a valid timezone (e.g., 'America/New_York').
    """
    try:
        # Create timezone object
        tz = pytz.timezone(timezone)
        # Get current time in that timezone
        local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S")
        return f"The current local time in {timezone} is: {local_time}"
    except Exception as e:
        return f"Error fetching time for timezone '{timezone}': {str(e)}"


final_answer = FinalAnswerTool()

citationtool = EuropePMCCitationsTool()
fulltexttool = EuropePMCArticleTextTool()
convert_id_tool = PMID2PMCIDConverter()

# If the agent does not answer, the model is overloaded, please use another model or the following Hugging Face Endpoint that also contains qwen2.5 coder:
# model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud' 

model = HfApiModel(
max_tokens=2096,
temperature=0.5,
model_id='deepseek-ai/DeepSeek-R1-Distill-Qwen-32B',# it is possible that this model may be overloaded
custom_role_conversions=None,
)


# Import tool from Hub
image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)

with open("prompts.yaml", 'r') as stream:
    prompt_templates = yaml.safe_load(stream)
    
agent = CodeAgent(
    model=model,
    tools=[final_answer, citationtool, fulltexttool, convert_id_tool], ## add your tools here (don't remove final answer)
    max_steps=6,
    verbosity_level=1,
    grammar=None,
    planning_interval=None,
    name=None,
    description=None,
    prompt_templates=prompt_templates
)


GradioUI(agent).launch()