| """ |
| Natural language understanding for TalkToDoc. |
| Interprets basic health-related queries to identify symptoms, intent, or |
| information requests, per functional requirement 5 in the research |
| document. |
| |
| This is a communication aid, not a diagnostic tool. The document is |
| explicit that the system does not provide formal medical diagnosis, so |
| this module only summarizes what the patient is communicating, it never |
| suggests a diagnosis or treatment. |
| |
| Uses the OpenAI API, same as translation.py. |
| |
| MOCK_MODE: if set to "true" in the env file, this uses simple keyword |
| matching instead of a real API call, so the rest of the app can be tested |
| for free, with no API key. Not a substitute for testing real NLU quality. |
| """ |
|
|
| import os |
| from dotenv import load_dotenv |
|
|
| load_dotenv("env") |
|
|
| MOCK_MODE = os.environ.get("MOCK_MODE", "").lower() == "true" |
|
|
| if not MOCK_MODE: |
| from openai import OpenAI |
| _client = OpenAI(api_key=os.environ["OPENAI_API_KEY"]) |
|
|
| MODEL = "gpt-5.6-terra" |
|
|
| _MOCK_KEYWORDS = ["headache", "fever", "stomach", "cough", "dizzy", "pain", "vomit", "rash"] |
|
|
|
|
| def interpret_query(english_text): |
| """ |
| english_text: the patient's message, already translated to English |
| Returns a short plain-language summary of the likely symptoms, intent, |
| or information request, to help the provider quickly understand what |
| the patient needs. Not a diagnosis. |
| """ |
| if MOCK_MODE: |
| text_lower = english_text.lower() |
| found = [word for word in _MOCK_KEYWORDS if word in text_lower] |
| symptoms = ", ".join(found) if found else "an unspecified concern" |
| return f"Patient reports {symptoms}. Requesting guidance. (Mock summary, no AI used.)" |
|
|
| prompt = ( |
| "A patient sent the following message to a healthcare provider. " |
| "In two sentences or less, summarize the likely symptoms, intent, " |
| "or information request. Do not diagnose or suggest treatment, " |
| "only summarize what the patient is communicating.\n\n" |
| f"Message: {english_text}" |
| ) |
|
|
| response = _client.chat.completions.create( |
| model=MODEL, |
| messages=[{"role": "user", "content": prompt}], |
| ) |
| return response.choices[0].message.content.strip() |
|
|
|
|
| if __name__ == "__main__": |
| import sys |
|
|
| if len(sys.argv) < 2: |
| print('Usage: python nlu.py "english text"') |
| else: |
| print(interpret_query(sys.argv[1])) |
|
|
