Spaces:
Sleeping
Sleeping
Write some tools to do stuff with papers
Browse files
app.py
CHANGED
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from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool
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import datetime
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import requests
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import pytz
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import yaml
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from tools.final_answer import FinalAnswerTool
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from Gradio_UI import GradioUI
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"""
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@tool
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def get_current_time_in_timezone(timezone: str) -> str:
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@@ -36,6 +126,10 @@ def get_current_time_in_timezone(timezone: str) -> str:
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final_answer = FinalAnswerTool()
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# 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:
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# model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud'
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@@ -55,7 +149,7 @@ with open("prompts.yaml", 'r') as stream:
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agent = CodeAgent(
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model=model,
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tools=[final_answer], ## add your tools here (don't remove final answer)
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max_steps=6,
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verbosity_level=1,
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grammar=None,
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from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool, Tool
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import datetime
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import requests
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import pytz
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import yaml
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import json
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from tools.final_answer import FinalAnswerTool
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from Gradio_UI import GradioUI
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from smolagents import Tool
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from epmc_xml import fetch
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import requests
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import json
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class EuropePMCCitationsTool(Tool):
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name = "europepmc_citation_downloader"
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description = """
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This tool queries the Europe PMC API to retrieve citations for a given PMCID.
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It returns a list of cited PMCID strings from the citation list.
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"""
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inputs = {
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"pmcid": {
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"type": "string",
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"description": "the PMCID to query (e.g., 'PMC7779037')",
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}
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}
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output_type = "array"
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def forward(self, pmcid: str):
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url = f"https://www.ebi.ac.uk/europepmc/webservices/rest/PMC/{pmcid}/citations?page=1&pageSize=1000&format=json"
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headers = {
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'Content-Type': 'application/json',
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}
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response = requests.get(url, headers=headers)
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if response.status_code != 200:
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raise Exception(f"Error querying Europe PMC API: {response.status_code}")
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data = json.loads(response.text)
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citations = data.get("citationList", {}).get("citation", [])
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# Extract PMCID strings from citations
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pmcid_list = [str(citation.get("id", "")) for citation in citations]
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return pmcid_list
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class EuropePMCArticleTextTool(Tool):
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name = "europepmc_fulltext_downloader"
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description = """
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This is a tool that fetches the full text of an article from Europe PMC.
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It takes a PMCID (format PMC\d+) as input and returns the full text of the article as a string.
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Articles can only be fetched if they are open access; returns emptty string if article is not open access.
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"""
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inputs = {
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"pmcid": {
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"type": "string",
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"description": "the PMCID of the article to fetch (e.g., 'PMC7779037')",
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}
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}
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output_type = "string"
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def forward(self, pmcid: str):
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# Fetch the article using epmc_xml
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try:
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article = fetch.article(pmcid)
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# Extract the full text from the article object
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full_text = article.get_body()
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except:
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full_text = ""#f"The article {pmcid} does not appear to be open access."
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# Return the full text as a string
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return full_text
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class PMID2PMCIDConverter(Tool):
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name = "pmid_2_pmcid_converter"
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description = """
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This tool converts the pmids (all numbers) to pmcids (format PMC\d+).
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Use this to convert pmids reported by the citation tool to pmcids needed by the fulltext downloader
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"""
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inputs = {
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"pmid": {
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"type": "string",
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"description": "the PMID to query (e.g., '36033386')",
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}
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}
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output_type = "string"
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def forward(self, pmid):
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url = f"https://www.ncbi.nlm.nih.gov/pmc/utils/idconv/v1.0?ids={pmid}&versions=no&format=json"
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headers = {
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'Content-Type': 'application/json',
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}
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response = requests.get(url, headers=headers)
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if response.status_code != 200:
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raise Exception(f"Error querying NCBI API: {response.status_code}")
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data = json.loads(response.text)
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pmcid = data.get("records", {})[0].get("pmcid", [])
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return pmcid
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@tool
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def get_current_time_in_timezone(timezone: str) -> str:
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final_answer = FinalAnswerTool()
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citationtool = EuropePMCCitationsTool()
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fulltexttool = EuropePMCArticleTextTool()
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convert_id_tool = PMID2PMCIDConverter()
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# 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:
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# model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud'
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agent = CodeAgent(
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model=model,
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tools=[final_answer, citationtool, fulltexttool, convert_id_tool], ## add your tools here (don't remove final answer)
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max_steps=6,
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verbosity_level=1,
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grammar=None,
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