Update interm.py

interm.py

@@ -1,42 +1,72 @@
-from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
-from fpdf import FPDF
-import tempfile
-
-# Initialize Clinical AI Agent
-clinical_agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=HfApiModel())
-
-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 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
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
+from Bio import Entrez
+from fpdf import FPDF
+import tempfile
+
+# Initialize Clinical AI Agent
+clinical_agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=HfApiModel())
+
+# Your PubMed Email (IMPORTANT: Use your registered PubMed email)
+Entrez.email = "Pub_Email"  # <-- Your registered PubMed 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