140 lines
4.6 KiB
Python
140 lines
4.6 KiB
Python
import json
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from app.ai.openai_client import OpenAIClient
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from app.config import OPENAI_API_KEY
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from app.schemas.onboarding import PatientOnboardingResult
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class ResultExtractionService:
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def __init__(self):
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self.openai = OpenAIClient()
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async def extract_result(
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self,
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session,
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state,
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conversation_history,
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) -> dict:
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current_state = json.loads(
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state.state_json
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)
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prompt = f"""
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You are a hospital patient onboarding data extraction assistant.
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Your task is to convert the completed onboarding information
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into structured patient intake data.
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You are NOT a doctor.
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Do not diagnose the patient.
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Do not add information that was not provided.
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PATIENT:
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{json.dumps({
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"id": session.patient_id,
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"name": session.patient_name,
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"age": session.age,
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"gender": session.gender,
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}, indent=2)}
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CATEGORY:
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{json.dumps({
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"type": session.category_type,
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"code": session.category_code,
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"name": session.category_name,
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}, indent=2)}
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CURRENT STATE:
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{json.dumps(current_state, indent=2)}
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CONVERSATION:
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{json.dumps(conversation_history, indent=2)}
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Return ONLY JSON conforming to this exact structure:
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{{
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"patient_id": "{session.patient_id}",
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"category": {{
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"type": "{session.category_type}",
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"code": "{session.category_code}",
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"name": "{session.category_name}"
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}},
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"chief_complaint": "string or null",
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"symptoms": [
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{{
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"name": "string",
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"duration": "string or null",
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"severity": "string or null",
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"description": "string or null"
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}}
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],
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"medical_history": [],
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"medications": [],
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"allergies": [],
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"additional_information": "string or null",
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"language": "{session.language}",
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"onboarding_completed": true
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}}
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"""
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if OPENAI_API_KEY and OPENAI_API_KEY.strip() not in ["your_openai_api_key", "your_openai_key", ""]:
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try:
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raw_response = await self.openai.generate_response(
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system_prompt=prompt,
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user_message="Generate the final structured patient onboarding result.",
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)
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cleaned = raw_response.strip()
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if cleaned.startswith("```json"):
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cleaned = cleaned[7:]
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if cleaned.startswith("```"):
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cleaned = cleaned[3:]
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if cleaned.endswith("```"):
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cleaned = cleaned[:-3]
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cleaned = cleaned.strip()
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parsed = json.loads(cleaned)
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validated = PatientOnboardingResult.model_validate(parsed)
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return validated.model_dump()
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except Exception as e:
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print(f"[Result Extraction Error]: {e}, falling back to structured state")
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# Fallback simulator from state
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chief_complaint = current_state.get("reason_for_visit")
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if isinstance(chief_complaint, dict):
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chief_complaint = chief_complaint.get("value")
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symptoms_raw = current_state.get("symptoms")
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symptoms_list = []
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if isinstance(symptoms_raw, dict):
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symptoms_list = symptoms_raw.get("items", [])
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elif isinstance(symptoms_raw, list):
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symptoms_list = symptoms_raw
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meds_raw = current_state.get("medications")
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meds_list = meds_raw.get("items", []) if isinstance(meds_raw, dict) else (meds_raw or [])
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allergies_raw = current_state.get("allergies")
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allergies_list = allergies_raw.get("items", []) if isinstance(allergies_raw, dict) else (allergies_raw or [])
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history_raw = current_state.get("medical_history")
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history_list = history_raw.get("items", []) if isinstance(history_raw, dict) else (history_raw or [])
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fallback_result = {
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"patient_id": session.patient_id,
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"category": {
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"type": session.category_type,
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"code": session.category_code,
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"name": session.category_name,
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},
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"chief_complaint": chief_complaint or "Consultation Intake",
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"symptoms": symptoms_list or [{"name": chief_complaint or "Reported discomfort", "duration": "recent", "severity": "mild", "description": None}],
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"medical_history": history_list,
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"medications": meds_list,
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"allergies": allergies_list,
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"additional_information": None,
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"language": session.language,
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"onboarding_completed": True,
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}
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validated = PatientOnboardingResult.model_validate(fallback_result)
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return validated.model_dump()
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