| from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel |
| from Bio import Entrez |
| from fpdf import FPDF |
| import tempfile |
|
|
| |
| clinical_agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=HfApiModel()) |
|
|
| |
| Entrez.email = "Pub_Email" |
|
|
| def generate_clinical_content(topic, audience): |
| """Generates medical content based on topic and audience.""" |
| prompt = f"Write a detailed medical article on: {topic}.\nTarget Audience: {audience}.\nInclude latest medical research insights." |
| return clinical_agent.run(prompt) |
|
|
| def generate_summary(content): |
| """Summarizes a given clinical document or research paper.""" |
| summary_prompt = f"Summarize the following medical research:\n{content}" |
| return clinical_agent.run(summary_prompt) |
|
|
| def generate_soap_note(symptoms, history): |
| """Generates a SOAP Note for clinicians based on symptoms and patient history.""" |
| soap_prompt = ( |
| f"Create a structured SOAP Note for a patient with:\n" |
| f"Symptoms: {symptoms}\n" |
| f"History: {history}\n" |
| f"Include Assessment & Plan." |
| ) |
| return clinical_agent.run(soap_prompt) |
|
|
| def fetch_pubmed_articles(query): |
| """Fetches latest medical research from PubMed based on a query.""" |
| try: |
| handle = Entrez.esearch(db="pubmed", term=query, retmax=5) |
| record = Entrez.read(handle) |
| handle.close() |
| article_ids = record["IdList"] |
|
|
| articles = [] |
| for article_id in article_ids: |
| handle = Entrez.efetch(db="pubmed", id=article_id, retmode="xml") |
| article_record = Entrez.read(handle) |
| handle.close() |
|
|
| article_data = article_record["PubmedArticle"][0]["MedlineCitation"]["Article"] |
| title = article_data.get("ArticleTitle", "No Title Available") |
| abstract = article_data.get("Abstract", {}).get("AbstractText", ["No Abstract Available"])[0] |
| authors = ", ".join([author["LastName"] for author in article_data.get("AuthorList", []) if "LastName" in author]) |
| url = f"https://pubmed.ncbi.nlm.nih.gov/{article_id}/" |
|
|
| articles.append({"title": title, "abstract": abstract, "authors": authors, "url": url}) |
|
|
| return articles |
| except Exception as e: |
| return [{"title": "Error Fetching Articles", "abstract": str(e), "authors": "N/A", "url": "#"}] |
|
|
| def save_as_text(content, filename): |
| """Saves AI-generated content as a TXT file.""" |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".txt") as tmp_file: |
| tmp_file.write(content.encode()) |
| return tmp_file.name, filename |
|
|
| def save_as_pdf(content, filename): |
| """Saves AI-generated content as a PDF file.""" |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file: |
| pdf = FPDF() |
| pdf.add_page() |
| pdf.set_font("Arial", size=12) |
| pdf.multi_cell(0, 10, content) |
| pdf.output(tmp_file.name) |
| return tmp_file.name, filename |
|
|
