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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]))
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