| | 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 |
| |
|
