Update README.md

README.md

@@ -1,3 +1,86 @@
 ---
 license: openrail
 ---
+from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
+from healthcare_nlp import Diseases, SNOMEDCT
+from healthcare_nlp.preprocessing import de_identify_text
+
+# Disease selection and model configuration
+disease_name = input("Enter a disease name (e.g., 'diabetes'): ").lower()
+disease_concept = SNOMEDCT.query_concept(disease_name)
+model_name = "allenai/biobert-base-cased"  # Clinically-tuned BioBERT model
+
+# Prepare user profile and tokenizer
+user_profile = {"age": int(input("Enter your age: "))}
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Access and process user message
+user_message = input("Ask your question about " + disease_concept.preferred_term + ": ")
+deidentified_message = de_identify_text(user_message)  # Protect sensitive information
+
+# Generate response with model and personalization
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+inputs = tokenizer(deidentified_message, truncation=True, padding="max_length", return_tensors="pt")
+generated_ids = model.generate(**inputs, max_length=256)
+response = tokenizer.decode(generated_ids.squeeze(), clean_up_tokenization=True)
+
+# Enhance response with knowledge base and context
+knowledge_base = Diseases.load_disease_info(disease_concept.concept_id)
+response = personalize_response(response, disease_concept, user_message, user_profile, knowledge_base)
+
+# Conclude with informative disclaimer
+response += (
+    "\nFeel free to ask any further questions you might have. I am still under development, but I leverage "
+    "clinical knowledge and continuous learning to provide accurate and evidence-based information. "
+    "Remember, I am not a substitute for professional healthcare advice. Please consult your doctor "
+    "for diagnosis and treatment decisions."
+)
+
+print(response)
+
+def personalize_response(response, disease_concept, user_message, user_profile, knowledge_base):
+    # Highlight relevant knowledge base sections
+    for key, value in knowledge_base.items():
+        if key.lower() in user_message.lower():
+            response += f"\nRegarding your query about {key}, here's some relevant information: {value}"
+
+    # Adapt response based on disease complexity and user age
+    response = personalize_with_disease_complexity(response, disease_concept)
+    response = personalize_with_user_age(response, user_profile)
+
+    # Add medical references and adjust communication style based on user preferences
+    if user_profile["age"] >= 18 and user_profile["preferred_communication_style"] == "informative":
+        response = add_informative_details(response, knowledge_base)
+    elif user_profile["age"] >= 18 and user_profile["preferred_communication_style"] == "empathetic":
+        # Implement empathetic communication style here (e.g., acknowledge feelings, offer support)
+        pass
+    else:
+        # Use simpler language and avoid overwhelming details for younger users or non-informative preferences
+        pass
+
+    # Re-identify anonymized terms for clinical context (optional)
+    # response = re_identify_text(response, knowledge_base)
+
+    return response
+
+# Personalize response with disease complexity
+def personalize_with_disease_complexity(response, disease_concept):
+    if "complex" in disease_concept.description.lower():
+        response += f"\nPlease note that {disease_concept.preferred_term} is a complex condition. My information offers a starting point, not a definitive explanation. Consult your doctor for a comprehensive understanding."
+    return response
+
+# Personalize response with user age
+def personalize_with_user_age(response, user_profile):
+    if user_profile["age"] < 18:
+        response += f"\nAs you're under 18, it's crucial to involve your parents or guardians in managing your health and seeking professional guidance."
+    return response
+
+# Add medical references or implement different communication styles here based on user preferences
+
+# Re-identify anonymized terms with caution, considering ethical implications and potential bias.
+
+This version enhances professionalism with:
+
+* **De-identification of user message:** Protects sensitive information while processing.
+* **Clinically-informed responses:** Tailored to disease complexity and user age.
+* **