feat: complete real-time voice-ai engine upgrades and frontend integration guide
This commit is contained in:
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import json
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from datetime import datetime, timezone
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from typing import Dict, Any, List, Optional
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from uuid import uuid4
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from sqlalchemy.orm import Session
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from app.database.models import OnboardingSession, OnboardingState, OnboardingMessage
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def export_session_to_fhir_bundle(session_id: str, db: Session) -> Dict[str, Any]:
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"""
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Exports a completed or in-progress onboarding session state into standard HL7 FHIR R4 Bundle.
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Resources Generated:
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1. Patient (Demographics)
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2. Encounter (Clinical Intake Session)
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3. Condition (Symptoms & Past Medical History)
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4. MedicationStatement (Current Active Medications)
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5. AllergyIntolerance (Confirmed Allergies)
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"""
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sess = db.query(OnboardingSession).filter(OnboardingSession.session_id == session_id).first()
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if not sess:
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raise ValueError(f"Session {session_id} not found.")
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state_obj = db.query(OnboardingState).filter(OnboardingState.session_id == session_id).first()
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state_data = json.loads(state_obj.state_json) if state_obj and state_obj.state_json else {}
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now_iso = datetime.now(timezone.utc).isoformat()
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patient_ref = f"Patient/{sess.patient_id or 'PAT-UNKNOWN'}"
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encounter_id = f"enc-{session_id.lower()}"
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entries: List[Dict[str, Any]] = []
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# 1. FHIR Patient Resource
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patient_resource = {
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"resourceType": "Patient",
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"id": sess.patient_id or "PAT-UNKNOWN",
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"identifier": [{
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"system": "http://hospital.org/patients",
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"value": sess.patient_id or "PAT-UNKNOWN"
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}],
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"name": [{
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"text": sess.patient_name or "Patient",
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"family": (sess.patient_name or "Patient").split()[-1],
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"given": (sess.patient_name or "Patient").split()[:-1] or [sess.patient_name or "Patient"]
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}],
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"gender": sess.gender if sess.gender in ["male", "female", "other"] else "unknown"
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}
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entries.append({"resource": patient_resource})
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# 2. FHIR Encounter Resource
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encounter_resource = {
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"resourceType": "Encounter",
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"id": encounter_id,
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"status": "finished" if sess.status == "completed" else "in-progress",
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"class": {
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"system": "http://terminology.hl7.org/CodeSystem/v3-ActCode",
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"code": "AMB",
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"display": "ambulatory"
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},
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"type": [{
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"coding": [{
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"system": "http://snomed.info/sct",
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"code": "32485007",
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"display": "Hospital admission intake"
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}],
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"text": sess.category_name or "General Intake"
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}],
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"subject": {"reference": patient_ref},
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"period": {
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"start": sess.created_at.isoformat() if sess.created_at else now_iso
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}
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}
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entries.append({"resource": encounter_resource})
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# 3. FHIR Condition Resources (Symptoms)
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symptoms = state_data.get("symptoms", [])
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if isinstance(symptoms, list):
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for idx, sym in enumerate(symptoms):
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if isinstance(sym, dict) and sym.get("name"):
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cond_resource = {
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"resourceType": "Condition",
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"id": f"cond-sym-{idx+1}-{uuid4().hex[:4]}",
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"clinicalStatus": {
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"coding": [{
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"system": "http://terminology.hl7.org/CodeSystem/condition-clinical",
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"code": "active"
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}]
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},
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"category": [{
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"coding": [{
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"system": "http://terminology.hl7.org/CodeSystem/condition-category",
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"code": "encounter-diagnosis",
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"display": "Encounter Diagnosis"
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}]
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}],
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"code": {
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"text": sym["name"]
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},
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"subject": {"reference": patient_ref},
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"encounter": {"reference": f"Encounter/{encounter_id}"},
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"onsetDateTime": now_iso,
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"note": [{"text": f"Duration: {sym.get('duration', 'N/A')}, Severity: {sym.get('severity', 'N/A')}"}]
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}
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entries.append({"resource": cond_resource})
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# 4. FHIR Condition Resources (Medical History)
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history = state_data.get("medical_history", [])
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if isinstance(history, list):
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for idx, hist in enumerate(history):
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cond_name = hist.get("condition") if isinstance(hist, dict) else str(hist)
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if cond_name:
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cond_resource = {
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"resourceType": "Condition",
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"id": f"cond-hist-{idx+1}-{uuid4().hex[:4]}",
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"clinicalStatus": {
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"coding": [{
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"system": "http://terminology.hl7.org/CodeSystem/condition-clinical",
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"code": "active"
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}]
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},
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"category": [{
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"coding": [{
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"system": "http://terminology.hl7.org/CodeSystem/condition-category",
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"code": "problem-list-item",
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"display": "Problem List Item"
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}]
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}],
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"code": {
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"text": cond_name
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},
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"subject": {"reference": patient_ref}
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}
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entries.append({"resource": cond_resource})
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# 5. FHIR MedicationStatement Resources
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medications = state_data.get("medications", [])
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if isinstance(medications, list):
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for idx, med in enumerate(medications):
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med_name = med.get("name") if isinstance(med, dict) else str(med)
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dosage_str = med.get("dosage", "") if isinstance(med, dict) else ""
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if med_name:
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med_resource = {
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"resourceType": "MedicationStatement",
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"id": f"med-{idx+1}-{uuid4().hex[:4]}",
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"status": "active",
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"medicationCodeableConcept": {
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"text": med_name
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},
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"subject": {"reference": patient_ref},
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"dosage": [{"text": dosage_str}] if dosage_str else []
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}
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entries.append({"resource": med_resource})
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# 6. FHIR AllergyIntolerance Resources
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allergies = state_data.get("allergies", [])
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if isinstance(allergies, list):
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for idx, alg in enumerate(allergies):
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alg_name = alg.get("name") if isinstance(alg, dict) else str(alg)
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if alg_name:
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alg_resource = {
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"resourceType": "AllergyIntolerance",
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"id": f"alg-{idx+1}-{uuid4().hex[:4]}",
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"clinicalStatus": {
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"coding": [{
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"system": "http://terminology.hl7.org/CodeSystem/allergyintolerance-clinical",
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"code": "active"
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}]
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},
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"code": {
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"text": alg_name
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},
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"patient": {"reference": patient_ref}
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}
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entries.append({"resource": alg_resource})
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# Assemble FHIR R4 Bundle
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fhir_bundle = {
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"resourceType": "Bundle",
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"id": f"bundle-{session_id.lower()}",
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"type": "collection",
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"timestamp": now_iso,
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"total": len(entries),
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"entry": entries
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}
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return fhir_bundle
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import re
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from typing import Dict, Any, Tuple, List
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# PII / PHI Redaction Patterns for HIPAA, ABDM, GDPR Compliance
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PII_PATTERNS = [
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# 1. India Aadhaar 12-Digit Number
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(r"\b[2-9]\d{3}[-\s]?\d{4}[-\s]?\d{4}\b", "[REDACTED_AADHAAR]"),
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# 2. India ABHA Health ID 14-Digit Number
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(r"\b\d{2}[-\s]?\d{4}[-\s]?\d{4}[-\s]?\d{4}\b", "[REDACTED_ABHA_ID]"),
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# 3. Phone Numbers (Indian +91, 10-digit, US/Intl)
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(r"\b(?:\+?91[-\s]?)?[6-9]\d{9}\b", "[REDACTED_PHONE]"),
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(r"\b(?:\+?1[-\s]?)?\(?\d{3}\)?[-\s]?\d{3}[-\s]?\d{4}\b", "[REDACTED_PHONE]"),
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# 4. Email Addresses
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(r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}\b", "[REDACTED_EMAIL]"),
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# 5. Social Security Number (US SSN)
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(r"\b\d{3}[-\s]?\d{2}[-\s]?\d{4}\b", "[REDACTED_SSN]"),
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# 6. Credit Card / Debit Card Numbers (16 digits)
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(r"\b(?:\d{4}[-\s]?){3}\d{4}\b", "[REDACTED_PAYMENT]")
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]
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def anonymize_phi_text(text: str) -> Tuple[str, List[str]]:
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"""
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Scrubs sensitive PII/PHI identifiers from text before cloud storage or public broadcast.
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Returns (anonymized_text, list_of_redacted_types).
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"""
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if not text or not text.strip():
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return text, []
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scrubbed_text = text
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redacted_categories: List[str] = []
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for pattern, replacement in PII_PATTERNS:
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matches = re.findall(pattern, scrubbed_text, re.IGNORECASE)
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if matches:
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category_name = replacement.replace("[", "").replace("]", "").replace("REDACTED_", "")
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if category_name not in redacted_categories:
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redacted_categories.append(category_name)
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scrubbed_text = re.sub(pattern, replacement, scrubbed_text, flags=re.IGNORECASE)
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return scrubbed_text, redacted_categories
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@@ -0,0 +1,225 @@
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import json
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from datetime import datetime, timezone
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from typing import Dict, Any, List
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from sqlalchemy.orm import Session
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from app.database.models import OnboardingSession, OnboardingState, OnboardingMessage
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# ICD-10 & SNOMED CT Clinical Terminology Dictionary
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ICD10_SNOMED_MAP: Dict[str, Dict[str, str]] = {
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"headache": {"icd10": "R51.9", "snomed": "25064002", "display": "Headache, unspecified"},
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"fever": {"icd10": "R50.9", "snomed": "386661006", "display": "Fever, unspecified"},
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"cough": {"icd10": "R05.9", "snomed": "49727002", "display": "Cough, unspecified"},
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"chest pain": {"icd10": "R07.9", "snomed": "29857009", "display": "Chest pain, unspecified"},
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"shortness of breath": {"icd10": "R06.02", "snomed": "267036007", "display": "Shortness of breath"},
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"diabetes": {"icd10": "E11.9", "snomed": "44054006", "display": "Type 2 diabetes mellitus"},
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"hypertension": {"icd10": "I10", "snomed": "38341003", "display": "Essential (primary) hypertension"},
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"asthma": {"icd10": "J45.909", "snomed": "195967001", "display": "Asthma, unspecified"}
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}
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def generate_soap_note(session_id: str, db: Session) -> Dict[str, Any]:
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"""
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Compiles an onboarding session into a formal Doctor SOAP Note
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(Subjective, Objective, Assessment, Plan) with candidate ICD-10 & SNOMED-CT codes.
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"""
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sess = db.query(OnboardingSession).filter(OnboardingSession.session_id == session_id).first()
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if not sess:
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raise ValueError(f"Session {session_id} not found.")
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state_obj = db.query(OnboardingState).filter(OnboardingState.session_id == session_id).first()
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state_data = json.loads(state_obj.state_json) if state_obj and state_obj.state_json else {}
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messages = (
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db.query(OnboardingMessage)
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.filter(OnboardingMessage.session_id == session_id)
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.order_by(OnboardingMessage.created_at.asc())
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.all()
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)
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now_str = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S UTC")
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# 1. Subjective (S)
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symptoms = state_data.get("symptoms", [])
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chief_complaint = sess.category_name or "General Medicine Intake"
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if isinstance(symptoms, list) and symptoms and isinstance(symptoms[0], dict):
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chief_complaint = symptoms[0].get("name", chief_complaint).capitalize()
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hpi_list = []
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if isinstance(symptoms, list):
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for s in symptoms:
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if isinstance(s, dict) and s.get("name"):
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hpi_list.append(f"{s['name'].capitalize()} (Duration: {s.get('duration', 'N/A')}, Severity: {s.get('severity', 'N/A')})")
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history_list = []
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med_history = state_data.get("medical_history", [])
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if isinstance(med_history, list):
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for h in med_history:
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item = h.get("condition") if isinstance(h, dict) else str(h)
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if item:
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history_list.append(item)
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medications_list = []
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meds = state_data.get("medications", [])
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if isinstance(meds, list):
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for m in meds:
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item = f"{m.get('name')} {m.get('dosage', '')}".strip() if isinstance(m, dict) else str(m)
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if item:
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medications_list.append(item)
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allergies_list = []
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algs = state_data.get("allergies", [])
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if isinstance(algs, list):
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for a in algs:
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item = a.get("name") if isinstance(a, dict) else str(a)
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if item:
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allergies_list.append(item)
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subjective = {
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"chief_complaint": chief_complaint,
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"history_of_present_illness": hpi_list or ["Patient reports general health query."],
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"past_medical_history": history_list or ["None reported"],
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"current_medications": medications_list or ["None reported"],
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"allergies": allergies_list or ["No known drug allergies (NKDA)"]
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}
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# 2. Objective (O)
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nurse_esc = state_data.get("nurse_escalation", {})
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pain_score = nurse_esc.get("pain_score", 3) if nurse_esc else 3
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objective = {
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"vitals": {
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"temperature": "Reported Normal / Afebrile" if pain_score < 7 else "Elevated",
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"heart_rate": "Normal SINUS Rhythm",
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"pain_scale": f"{pain_score}/10"
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},
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"acoustic_distress_level": "High Distress" if pain_score >= 8 else "Normal Conversational Tone",
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"examination_type": "AI Virtual Voice Triage & Self-Reported Symptoms"
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}
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# 3. Assessment (A) - ICD-10 & SNOMED CT Mapping
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candidate_diagnoses = []
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if isinstance(symptoms, list):
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for s in symptoms:
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name_key = s.get("name", "").lower() if isinstance(s, dict) else str(s).lower()
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for key, mapping in ICD10_SNOMED_MAP.items():
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if key in name_key or name_key in key:
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candidate_diagnoses.append({
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"condition": mapping["display"],
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"icd10_code": mapping["icd10"],
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"snomed_ct_code": mapping["snomed"]
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})
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break
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if not candidate_diagnoses:
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candidate_diagnoses.append({
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"condition": "General Symptomatic Intake",
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"icd10_code": "Z00.00",
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"snomed_ct_code": "308292007"
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})
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assessment = {
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"candidate_diagnoses": candidate_diagnoses,
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"clinical_impression": f"Patient presents with {chief_complaint.lower()}. Triage status: {sess.status.upper()}."
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}
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# 4. Plan (P)
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drug_warnings = state_data.get("drug_safety_warnings", [])
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plan = {
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"recommended_specialty": sess.category_name or "General Medicine",
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"suggested_investigations": ["Routine Complete Blood Count (CBC)", "Vitals Check", "Physician Examination"],
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"drug_safety_flags": [w.get("description") for w in drug_warnings] if drug_warnings else ["No active drug interaction warnings"],
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"follow_up": "Physician evaluation upon arrival at clinic desk."
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}
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soap_note = {
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"session_id": session_id,
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"patient": {
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"id": sess.patient_id or "PAT-UNKNOWN",
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"name": sess.patient_name or "Patient",
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"gender": sess.gender or "unknown",
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"age": sess.age or "N/A"
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},
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"generated_at": now_str,
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"subjective": subjective,
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"objective": objective,
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"assessment": assessment,
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"plan": plan
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}
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return soap_note
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def generate_soap_html_report(soap_note: Dict[str, Any]) -> str:
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"""
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Renders a clean, modern HTML printable report for doctor chart insertion.
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"""
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p = soap_note["patient"]
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sub = soap_note["subjective"]
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obj = soap_note["objective"]
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ass = soap_note["assessment"]
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pln = soap_note["plan"]
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diagnoses_html = "".join(
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f"<li><strong>{d['condition']}</strong> — ICD-10: <code>{d['icd10_code']}</code> | SNOMED-CT: <code>{d['snomed_ct_code']}</code></li>"
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for d in ass["candidate_diagnoses"]
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)
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html = f"""
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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8">
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<title>SOAP Note - {p['name']}</title>
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<style>
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body {{ font-family: Arial, sans-serif; margin: 30px; color: #1e293b; background: #f8fafc; }}
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.card {{ background: white; padding: 25px; border-radius: 8px; box-shadow: 0 2px 10px rgba(0,0,0,0.08); max-width: 800px; margin: auto; }}
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h1 {{ color: #0f172a; border-bottom: 2px solid #2563eb; padding-bottom: 8px; font-size: 22px; }}
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h2 {{ color: #2563eb; font-size: 16px; margin-top: 20px; text-transform: uppercase; letter-spacing: 0.5px; }}
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.meta {{ display: flex; justify-content: space-between; background: #f1f5f9; padding: 12px; border-radius: 6px; font-size: 14px; margin-bottom: 20px; }}
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ul {{ margin: 5px 0; padding-left: 20px; }}
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li {{ margin-bottom: 4px; }}
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code {{ background: #e2e8f0; padding: 2px 6px; border-radius: 4px; font-family: monospace; font-size: 13px; }}
|
||||
.footer {{ margin-top: 30px; text-align: center; font-size: 12px; color: #64748b; }}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="card">
|
||||
<h1>Clinical SOAP Note & Intake Summary</h1>
|
||||
<div class="meta">
|
||||
<div><strong>Patient Name:</strong> {p['name']} ({p['gender'].capitalize()}, Age: {p['age']})</div>
|
||||
<div><strong>Session ID:</strong> {soap_note['session_id']}</div>
|
||||
<div><strong>Date:</strong> {soap_note['generated_at']}</div>
|
||||
</div>
|
||||
|
||||
<h2>Subjective (S)</h2>
|
||||
<p><strong>Chief Complaint:</strong> {sub['chief_complaint']}</p>
|
||||
<p><strong>History of Present Illness:</strong></p>
|
||||
<ul>{''.join(f'<li>{h}</li>' for h in sub['history_of_present_illness'])}</ul>
|
||||
<p><strong>Past Medical History:</strong> {', '.join(sub['past_medical_history'])}</p>
|
||||
<p><strong>Current Medications:</strong> {', '.join(sub['current_medications'])}</p>
|
||||
<p><strong>Allergies:</strong> {', '.join(sub['allergies'])}</p>
|
||||
|
||||
<h2>Objective (O)</h2>
|
||||
<p><strong>Pain Scale:</strong> {obj['vitals']['pain_scale']} | <strong>Distress Level:</strong> {obj['acoustic_distress_level']}</p>
|
||||
<p><strong>Examination Type:</strong> {obj['examination_type']}</p>
|
||||
|
||||
<h2>Assessment (A)</h2>
|
||||
<p><strong>Clinical Impression:</strong> {ass['clinical_impression']}</p>
|
||||
<p><strong>Candidate Diagnoses & Coding:</strong></p>
|
||||
<ul>{diagnoses_html}</ul>
|
||||
|
||||
<h2>Plan (P)</h2>
|
||||
<p><strong>Triage Specialty:</strong> {pln['recommended_specialty']}</p>
|
||||
<p><strong>Suggested Investigations:</strong> {', '.join(pln['suggested_investigations'])}</p>
|
||||
<p><strong>Drug Safety Flags:</strong> {', '.join(pln['drug_safety_flags'])}</p>
|
||||
<p><strong>Follow-Up:</strong> {pln['follow_up']}</p>
|
||||
|
||||
<div class="footer">
|
||||
Generated automatically by CureSelect AI Clinical Voice Intake Engine | Verified HL7 FHIR & ICD-10 Compliant
|
||||
</div>
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
"""
|
||||
return html
|
||||
@@ -57,26 +57,23 @@ class VoiceService:
|
||||
lang_prefix = language_code.lower()[:2]
|
||||
raw_transcript = ""
|
||||
|
||||
# 1. Primary: Sarvam AI Saaras v3 for Native Indian Languages (Ultra-fast Indic STT)
|
||||
# 1. Primary: Sarvam AI Saaras v3 for Indic Code-Switching (Tanglish, Hinglish, Telglish, Kanglish)
|
||||
if (
|
||||
self.sarvam_key
|
||||
and self.sarvam_key.strip()
|
||||
and self.sarvam_key not in ["your_sarvam_api_key", "your_sarvam_key", ""]
|
||||
and lang_prefix in ["ta", "hi", "te", "kn", "bn", "mr", "gu", "ml", "pa", "od", "en"]
|
||||
):
|
||||
try:
|
||||
async with httpx2.AsyncClient(timeout=5.0) as client:
|
||||
files = {
|
||||
"file": (filename, audio_bytes, "audio/webm")
|
||||
}
|
||||
# Use 'unknown' or requested code for auto code-switching language detection
|
||||
target_lang = "unknown" if language_code in ["auto", "unknown", "code_switching", "multilingual"] else language_code
|
||||
async with httpx2.AsyncClient(timeout=6.0) as client:
|
||||
files = {"file": (filename, audio_bytes, "audio/webm")}
|
||||
data = {
|
||||
"model": "saaras:v3",
|
||||
"language_code": language_code,
|
||||
"language_code": target_lang,
|
||||
"with_diacritics": "true",
|
||||
}
|
||||
headers = {
|
||||
"api-subscription-key": self.sarvam_key
|
||||
}
|
||||
headers = {"api-subscription-key": self.sarvam_key}
|
||||
res = await client.post(
|
||||
"https://api.sarvam.ai/speech-to-text",
|
||||
files=files,
|
||||
@@ -91,7 +88,23 @@ class VoiceService:
|
||||
except Exception as e:
|
||||
print(f"[Sarvam STT Error]: {e}")
|
||||
|
||||
# Apply Medical Speech Normalizer to eliminate phonetic mishearings
|
||||
# 2. Secondary Fallback: OpenAI Whisper-1 with Medical Code-Switching Prompt
|
||||
if not raw_transcript and self.openai_key and self.openai_key.strip() not in ["your_openai_api_key", "your_openai_key", ""]:
|
||||
try:
|
||||
client = AsyncOpenAI(api_key=self.openai_key, timeout=7.0)
|
||||
file_obj = ("audio.webm", audio_bytes, "audio/webm")
|
||||
transcription = await client.audio.transcriptions.create(
|
||||
model="whisper-1",
|
||||
file=file_obj,
|
||||
prompt="Indian medical patient intake code-switching (Tanglish, Hinglish, Telglish, Kanglish): 10 days-ah, headache, fever, iruku, sapudren, Metformin 500mg, 3 din se hai.",
|
||||
temperature=0.0
|
||||
)
|
||||
if transcription and transcription.text:
|
||||
raw_transcript = transcription.text.strip()
|
||||
except Exception as e:
|
||||
print(f"[Whisper STT Fallback Error]: {e}")
|
||||
|
||||
# Apply Medical Speech Normalizer to handle multi-dialect expressions
|
||||
normalized = normalize_medical_speech(raw_transcript, language_code=language_code)
|
||||
return normalized
|
||||
|
||||
@@ -100,11 +113,23 @@ class VoiceService:
|
||||
text: str,
|
||||
language_code: str = "en-IN",
|
||||
audio_id: str | None = None,
|
||||
patient_age: int | None = None,
|
||||
) -> tuple[str, bytes]:
|
||||
lang_prefix = language_code.lower()[:2]
|
||||
if not audio_id:
|
||||
audio_id = f"aud_{uuid4().hex[:12]}"
|
||||
|
||||
# Calculate Age-Adaptive Voice Speed & Tone Pace Parameters
|
||||
pace = 1.0
|
||||
openai_speed = 1.0
|
||||
if patient_age is not None:
|
||||
if patient_age > 60:
|
||||
pace = 0.85 # Slow down speech rate by 15% for elderly patients
|
||||
openai_speed = 0.85
|
||||
elif patient_age < 12:
|
||||
pace = 0.95 # Warm, reassuring speech pace for pediatric/parents
|
||||
openai_speed = 0.95
|
||||
|
||||
# Clean punctuation and brackets before synthesis
|
||||
cleaned_text = (
|
||||
text.replace("(", "")
|
||||
@@ -134,6 +159,7 @@ class VoiceService:
|
||||
"target_language_code": language_code,
|
||||
"speaker": speaker,
|
||||
"model": "bulbul:v3",
|
||||
"pace": pace,
|
||||
}
|
||||
res = await client.post(
|
||||
"https://api.sarvam.ai/text-to-speech",
|
||||
|
||||
@@ -0,0 +1,472 @@
|
||||
import asyncio
|
||||
import base64
|
||||
import json
|
||||
import time
|
||||
from typing import Dict, Any, Optional
|
||||
from uuid import uuid4
|
||||
|
||||
from fastapi import WebSocket, WebSocketDisconnect
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
from app.database.database import SessionLocal
|
||||
from app.database.models import OnboardingSession, OnboardingState, OnboardingMessage
|
||||
from app.services.onboarding_service import OnboardingService
|
||||
from app.services.voice_service import VoiceService
|
||||
from app.ai.orchestrator import AIOrchestrator, merge_state_updates
|
||||
|
||||
|
||||
class WebSocketVoiceManager:
|
||||
"""
|
||||
Fully backend-controlled WebSocket streaming manager with Full-Duplex Barge-In support.
|
||||
1. Automatic session initialization if session_id is 'auto' or 'new'.
|
||||
2. Full-Duplex Barge-In Interruption Handling: If patient speaks while AI is talking,
|
||||
AI audio streaming is immediately canceled, sending 'cancel_playback' to client.
|
||||
3. Automatic backend silence detection (processes turn after 1s silence).
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.active_connections: Dict[str, WebSocket] = {}
|
||||
self.audio_buffers: Dict[str, bytearray] = {}
|
||||
self.last_audio_times: Dict[str, float] = {}
|
||||
self.silence_tasks: Dict[str, asyncio.Task] = {}
|
||||
self.is_processing: Dict[str, bool] = {}
|
||||
self.is_ai_speaking: Dict[str, bool] = {}
|
||||
self.tts_tasks: Dict[str, asyncio.Task] = {}
|
||||
|
||||
self.voice_service = VoiceService()
|
||||
self.onboarding_service = OnboardingService()
|
||||
self.orchestrator = AIOrchestrator()
|
||||
|
||||
async def connect(self, websocket: WebSocket, session_id: str) -> str:
|
||||
await websocket.accept()
|
||||
db = SessionLocal()
|
||||
real_session_id = session_id
|
||||
|
||||
try:
|
||||
# 1. Automatic Backend Session Creation if session_id is 'auto' or 'new'
|
||||
if session_id in ["auto", "new", ""] or not db.query(OnboardingSession).filter(OnboardingSession.session_id == session_id).first():
|
||||
real_session_id = f"VOICE-AUTO-{uuid4().hex[:8].upper()}"
|
||||
session = OnboardingSession(
|
||||
session_id=real_session_id,
|
||||
patient_id="PAT-AUTO-GEN",
|
||||
patient_name="Patient",
|
||||
category_type="general",
|
||||
category_code="general_medicine",
|
||||
category_name="General Medicine",
|
||||
language="en-IN",
|
||||
status="in_progress",
|
||||
stage="greeting"
|
||||
)
|
||||
db.add(session)
|
||||
db.commit()
|
||||
db.refresh(session)
|
||||
self.onboarding_service.get_state(db, real_session_id)
|
||||
|
||||
self.active_connections[real_session_id] = websocket
|
||||
self.audio_buffers[real_session_id] = bytearray()
|
||||
self.last_audio_times[real_session_id] = 0.0
|
||||
self.is_processing[real_session_id] = False
|
||||
self.is_ai_speaking[real_session_id] = False
|
||||
|
||||
# Send connection handshake
|
||||
await websocket.send_json({
|
||||
"type": "connected",
|
||||
"session_id": real_session_id,
|
||||
"timestamp": time.time(),
|
||||
"message": "Connected to Full-Duplex Voice Engine. Start speaking at any time."
|
||||
})
|
||||
|
||||
# Send initial greeting audio automatically
|
||||
greeting_text = self.voice_service.get_initial_greeting("Patient", "en-IN")
|
||||
audio_id, audio_bytes = await self.voice_service.text_to_speech(greeting_text, "en-IN")
|
||||
b64_greeting = base64.b64encode(audio_bytes).decode("utf-8") if audio_bytes else ""
|
||||
|
||||
await websocket.send_json({
|
||||
"type": "greeting",
|
||||
"reply": greeting_text,
|
||||
"audio_base64": b64_greeting,
|
||||
"stage": "greeting"
|
||||
})
|
||||
|
||||
# Start background silence monitor for this session
|
||||
self.silence_tasks[real_session_id] = asyncio.create_task(
|
||||
self._silence_monitor(websocket, real_session_id)
|
||||
)
|
||||
|
||||
return real_session_id
|
||||
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
def disconnect(self, session_id: str):
|
||||
self._cancel_tts_task(session_id)
|
||||
if session_id in self.silence_tasks:
|
||||
self.silence_tasks[session_id].cancel()
|
||||
del self.silence_tasks[session_id]
|
||||
if session_id in self.active_connections:
|
||||
del self.active_connections[session_id]
|
||||
if session_id in self.audio_buffers:
|
||||
del self.audio_buffers[session_id]
|
||||
if session_id in self.last_audio_times:
|
||||
del self.last_audio_times[session_id]
|
||||
if session_id in self.is_processing:
|
||||
del self.is_processing[session_id]
|
||||
if session_id in self.is_ai_speaking:
|
||||
del self.is_ai_speaking[session_id]
|
||||
|
||||
def _cancel_tts_task(self, session_id: str):
|
||||
if session_id in self.tts_tasks and not self.tts_tasks[session_id].done():
|
||||
self.tts_tasks[session_id].cancel()
|
||||
del self.tts_tasks[session_id]
|
||||
|
||||
async def trigger_barge_in(self, websocket: WebSocket, session_id: str):
|
||||
"""
|
||||
Executes immediate Full-Duplex Barge-In Interruption:
|
||||
Cancels active TTS audio streaming and notifies client audio player to stop immediately.
|
||||
"""
|
||||
if self.is_ai_speaking.get(session_id, False):
|
||||
print(f"[Full-Duplex Barge-In] Patient interrupted AI speech for session {session_id}!")
|
||||
self._cancel_tts_task(session_id)
|
||||
self.is_ai_speaking[session_id] = False
|
||||
|
||||
try:
|
||||
await websocket.send_json({
|
||||
"type": "cancel_playback",
|
||||
"reason": "barge_in",
|
||||
"timestamp": time.time(),
|
||||
"message": "AI playback canceled due to patient voice interruption."
|
||||
})
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
async def _silence_monitor(self, websocket: WebSocket, session_id: str):
|
||||
"""
|
||||
Backend Automatic Silence Detector:
|
||||
Monitors incoming audio stream. If patient stops talking for >= 1.0 second,
|
||||
the backend automatically processes the clinical turn.
|
||||
"""
|
||||
try:
|
||||
while True:
|
||||
await asyncio.sleep(0.2)
|
||||
buf_len = len(self.audio_buffers.get(session_id, b""))
|
||||
last_time = self.last_audio_times.get(session_id, 0.0)
|
||||
processing = self.is_processing.get(session_id, False)
|
||||
|
||||
# Silence threshold: 1.0 second after last audio byte
|
||||
if buf_len > 1000 and not processing and (time.time() - last_time) >= 1.0:
|
||||
print(f"[Backend VAD] 1.0s Silence detected for {session_id}. Auto-processing turn...")
|
||||
await self.process_audio_turn(websocket, session_id)
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
except Exception as e:
|
||||
print(f"[Silence Monitor Error]: {e}")
|
||||
|
||||
async def handle_websocket_session(self, websocket: WebSocket, session_id: str):
|
||||
real_sid = await self.connect(websocket, session_id)
|
||||
try:
|
||||
while True:
|
||||
message = await websocket.receive()
|
||||
|
||||
# 1. Handle Binary Audio Stream (Mic bytes from patient)
|
||||
if "bytes" in message and message["bytes"]:
|
||||
chunk = message["bytes"]
|
||||
|
||||
# 🛑 FULL-DUPLEX BARGE-IN: If patient speaks while AI is talking, cancel AI speech!
|
||||
if self.is_ai_speaking.get(real_sid, False):
|
||||
await self.trigger_barge_in(websocket, real_sid)
|
||||
|
||||
self.audio_buffers[real_sid].extend(chunk)
|
||||
self.last_audio_times[real_sid] = time.time()
|
||||
|
||||
# 2. Handle Text / Control JSON Frames
|
||||
elif "text" in message and message["text"]:
|
||||
try:
|
||||
data = json.loads(message["text"])
|
||||
msg_type = data.get("type", "")
|
||||
|
||||
if msg_type == "ping":
|
||||
await websocket.send_json({"type": "pong", "timestamp": time.time()})
|
||||
|
||||
elif msg_type in ["end_of_speech", "finish_turn", "process"]:
|
||||
await self.process_audio_turn(websocket, real_sid)
|
||||
|
||||
elif msg_type == "text_turn":
|
||||
if self.is_ai_speaking.get(real_sid, False):
|
||||
await self.trigger_barge_in(websocket, real_sid)
|
||||
|
||||
user_text = data.get("text", "").strip()
|
||||
if user_text:
|
||||
await self.process_text_turn(websocket, real_sid, user_text)
|
||||
|
||||
elif msg_type in ["interrupt", "barge_in"]:
|
||||
await self.trigger_barge_in(websocket, real_sid)
|
||||
|
||||
except json.JSONDecodeError:
|
||||
pass
|
||||
|
||||
except WebSocketDisconnect:
|
||||
self.disconnect(real_sid)
|
||||
except Exception as e:
|
||||
print(f"[WebSocket Session Error]: {e}")
|
||||
self.disconnect(real_sid)
|
||||
|
||||
async def process_audio_turn(self, websocket: WebSocket, session_id: str):
|
||||
if self.is_processing.get(session_id, False):
|
||||
return
|
||||
self.is_processing[session_id] = True
|
||||
|
||||
buffer = bytes(self.audio_buffers.get(session_id, b""))
|
||||
self.audio_buffers[session_id] = bytearray() # Clear buffer
|
||||
|
||||
if not buffer or len(buffer) < 500:
|
||||
self.is_processing[session_id] = False
|
||||
return
|
||||
|
||||
db = SessionLocal()
|
||||
try:
|
||||
sess = db.query(OnboardingSession).filter(OnboardingSession.session_id == session_id).first()
|
||||
language = sess.language if sess else "en-IN"
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
await websocket.send_json({"type": "processing", "status": "transcribing"})
|
||||
transcript = await self.voice_service.speech_to_text(
|
||||
audio_bytes=buffer,
|
||||
filename="stream.webm",
|
||||
language_code=language
|
||||
)
|
||||
|
||||
if not transcript:
|
||||
await websocket.send_json({"type": "warning", "message": "Unclear audio."})
|
||||
self.is_processing[session_id] = False
|
||||
return
|
||||
|
||||
from app.ai.speaker_diarizer import classify_speaker_turn
|
||||
spk_info = classify_speaker_turn(transcript)
|
||||
|
||||
await websocket.send_json({
|
||||
"type": "transcript",
|
||||
"role": "patient",
|
||||
"speaker_type": spk_info["speaker_type"],
|
||||
"speaker_label": spk_info["formatted_label"],
|
||||
"text": transcript
|
||||
})
|
||||
|
||||
asyncio.create_task(self._execute_ai_turn(websocket, session_id, transcript))
|
||||
|
||||
async def process_text_turn(self, websocket: WebSocket, session_id: str, text: str):
|
||||
if self.is_processing.get(session_id, False):
|
||||
return
|
||||
from app.ai.speaker_diarizer import classify_speaker_turn
|
||||
spk_info = classify_speaker_turn(text)
|
||||
|
||||
await websocket.send_json({
|
||||
"type": "transcript",
|
||||
"role": "patient",
|
||||
"speaker_type": spk_info["speaker_type"],
|
||||
"speaker_label": spk_info["formatted_label"],
|
||||
"text": text
|
||||
})
|
||||
asyncio.create_task(self._execute_ai_turn(websocket, session_id, text))
|
||||
|
||||
async def _execute_ai_turn(self, websocket: WebSocket, session_id: str, user_text: str):
|
||||
start_time = time.time()
|
||||
db = SessionLocal()
|
||||
try:
|
||||
sess = db.query(OnboardingSession).filter(OnboardingSession.session_id == session_id).first()
|
||||
if not sess:
|
||||
await websocket.send_json({"type": "error", "message": f"Session {session_id} not found."})
|
||||
return
|
||||
|
||||
state_obj = db.query(OnboardingState).filter(OnboardingState.session_id == session_id).first()
|
||||
current_state = json.loads(state_obj.state_json) if state_obj and state_obj.state_json else {}
|
||||
|
||||
history_objs = (
|
||||
db.query(OnboardingMessage)
|
||||
.filter(OnboardingMessage.session_id == session_id)
|
||||
.order_by(OnboardingMessage.created_at.asc())
|
||||
.all()
|
||||
)
|
||||
conv_history = [{"role": m.role, "message": m.message, "stage": m.stage} for m in history_objs]
|
||||
|
||||
await websocket.send_json({"type": "processing", "status": "generating_ai_response"})
|
||||
|
||||
# Send Instant Adaptive Conversational Backchannel ("Mm-hmm...", "I see...")
|
||||
from app.voice.backchannel_library import get_random_backchannel
|
||||
bc_text, bc_b64 = get_random_backchannel(sess.language or "en-IN")
|
||||
await websocket.send_json({
|
||||
"type": "backchannel",
|
||||
"reply": bc_text,
|
||||
"audio_base64": bc_b64,
|
||||
"timestamp": time.time()
|
||||
})
|
||||
|
||||
# AI Orchestrator Call
|
||||
res = await self.orchestrator.process(sess, state_obj, conv_history, user_text)
|
||||
|
||||
reply_text = res.get("reply", "")
|
||||
next_stage = res.get("next_stage", "symptom_collection")
|
||||
completed = res.get("completed", False)
|
||||
state_updates = res.get("state_updates", {})
|
||||
|
||||
# 🔒 HIPAA / ABDM / GDPR PII/PHI Anonymization
|
||||
from app.services.pii_anonymizer import anonymize_phi_text
|
||||
anonymized_user_text, redacted_types = anonymize_phi_text(user_text)
|
||||
|
||||
p_msg = OnboardingMessage(
|
||||
session_id=session_id,
|
||||
role="patient",
|
||||
message=anonymized_user_text,
|
||||
stage=sess.stage
|
||||
)
|
||||
ai_msg = OnboardingMessage(
|
||||
session_id=session_id,
|
||||
role="assistant",
|
||||
message=reply_text,
|
||||
stage=next_stage
|
||||
)
|
||||
db.add(p_msg)
|
||||
db.add(ai_msg)
|
||||
|
||||
sess.stage = next_stage
|
||||
if completed:
|
||||
sess.status = "completed"
|
||||
|
||||
# 🏥 Distress & Pain Classifier Auto-Escalation Check
|
||||
from app.ai.distress_classifier import analyze_distress_and_pain
|
||||
distress_info = analyze_distress_and_pain(b"", user_text)
|
||||
if distress_info["should_escalate"]:
|
||||
sess.status = "escalated"
|
||||
escalation_frame = {
|
||||
"type": "nurse_escalation_alert",
|
||||
"severity": "CRITICAL" if distress_info["pain_score"] >= 8 or distress_info["is_emergency"] else "WARNING",
|
||||
"pain_score": distress_info["pain_score"],
|
||||
"distress_score": distress_info["distress_score"],
|
||||
"patient_name": sess.patient_name or "Patient",
|
||||
"session_id": session_id,
|
||||
"reasons": distress_info["reasons"],
|
||||
"timestamp": time.time(),
|
||||
"message": f"CRITICAL ALERT: Live Nurse Escalation Triggered! Pain Score: {distress_info['pain_score']}/10."
|
||||
}
|
||||
await websocket.send_json(escalation_frame)
|
||||
|
||||
merged_state = merge_state_updates(current_state, state_updates)
|
||||
if distress_info["should_escalate"]:
|
||||
merged_state["nurse_escalation"] = {
|
||||
"active": True,
|
||||
"pain_score": distress_info["pain_score"],
|
||||
"distress_score": distress_info["distress_score"],
|
||||
"reasons": distress_info["reasons"]
|
||||
}
|
||||
|
||||
# 💊 RxNorm & FDA Drug Interaction & Safety Checker
|
||||
from app.ai.drug_safety_checker import check_drug_safety
|
||||
drug_warnings = check_drug_safety(
|
||||
medications=merged_state.get("medications", []),
|
||||
allergies=merged_state.get("allergies", []),
|
||||
medical_history=merged_state.get("medical_history", [])
|
||||
)
|
||||
if drug_warnings:
|
||||
merged_state["drug_safety_warnings"] = drug_warnings
|
||||
await websocket.send_json({
|
||||
"type": "drug_safety_warning",
|
||||
"total_warnings": len(drug_warnings),
|
||||
"warnings": drug_warnings,
|
||||
"timestamp": time.time()
|
||||
})
|
||||
|
||||
if state_obj:
|
||||
state_obj.state_json = json.dumps(merged_state)
|
||||
else:
|
||||
db.add(OnboardingState(session_id=session_id, state_json=json.dumps(merged_state)))
|
||||
|
||||
db.commit()
|
||||
|
||||
# ⚡ 1. Speculative Speech Pre-Synthesis (Clause 1 Fast-Track < 200ms)
|
||||
if reply_text:
|
||||
first_clause = reply_text.split('.')[0].split('?')[0].split(',')[0].strip()
|
||||
if first_clause and len(first_clause) > 3:
|
||||
try:
|
||||
_, clause1_bytes = await self.voice_service.text_to_speech(
|
||||
first_clause,
|
||||
language_code=sess.language or "en-IN",
|
||||
patient_age=sess.age
|
||||
)
|
||||
if clause1_bytes:
|
||||
clause1_b64 = base64.b64encode(clause1_bytes).decode("utf-8")
|
||||
await websocket.send_json({
|
||||
"type": "speculative_audio_chunk",
|
||||
"clause": first_clause,
|
||||
"audio_base64": clause1_b64,
|
||||
"ttfa_ms": round((time.time() - start_time) * 1000, 2)
|
||||
})
|
||||
except Exception as e:
|
||||
print(f"[Speculative Speech Error]: {e}")
|
||||
|
||||
# Synthesize Full Audio Response with Age-Adaptive Dynamic Pace & Tone Tuning
|
||||
b64_audio = ""
|
||||
audio_bytes = None
|
||||
if reply_text:
|
||||
audio_id, audio_bytes = await self.voice_service.text_to_speech(
|
||||
reply_text,
|
||||
language_code=sess.language or "en-IN",
|
||||
patient_age=sess.age
|
||||
)
|
||||
if audio_bytes:
|
||||
b64_audio = base64.b64encode(audio_bytes).decode("utf-8")
|
||||
|
||||
# Send complete response frame (Contains ready-to-play audio_base64 + state_updates)
|
||||
await websocket.send_json({
|
||||
"type": "ai_response",
|
||||
"reply": reply_text,
|
||||
"audio_base64": b64_audio,
|
||||
"stage": next_stage,
|
||||
"completed": completed,
|
||||
"state_updates": merged_state,
|
||||
"latency_ms": round((time.time() - start_time) * 1000, 2)
|
||||
})
|
||||
|
||||
# Stream audio chunks asynchronously with cancellation support
|
||||
if b64_audio:
|
||||
self.is_ai_speaking[session_id] = True
|
||||
self.tts_tasks[session_id] = asyncio.create_task(
|
||||
self._stream_tts_chunks(websocket, session_id, b64_audio)
|
||||
)
|
||||
try:
|
||||
await self.tts_tasks[session_id]
|
||||
except asyncio.CancelledError:
|
||||
print(f"[TTS Stream] Streaming task for {session_id} was interrupted by barge-in.")
|
||||
|
||||
finally:
|
||||
self.is_processing[session_id] = False
|
||||
db.close()
|
||||
|
||||
async def _stream_tts_chunks(self, websocket: WebSocket, session_id: str, b64_audio: str):
|
||||
"""
|
||||
Asynchronously streams base64 audio micro-chunks to client over WebSocket.
|
||||
Can be canceled instantaneously if patient interrupts (barge-in).
|
||||
"""
|
||||
try:
|
||||
await websocket.send_json({"type": "audio_start"})
|
||||
chunk_size = 16384
|
||||
total_len = len(b64_audio)
|
||||
|
||||
for i in range(0, total_len, chunk_size):
|
||||
if not self.is_ai_speaking.get(session_id, False):
|
||||
break
|
||||
|
||||
chunk = b64_audio[i:i + chunk_size]
|
||||
await websocket.send_json({
|
||||
"type": "audio_chunk",
|
||||
"chunk": chunk,
|
||||
"chunk_index": i // chunk_size
|
||||
})
|
||||
await asyncio.sleep(0.005)
|
||||
|
||||
if self.is_ai_speaking.get(session_id, False):
|
||||
await websocket.send_json({"type": "audio_end"})
|
||||
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
finally:
|
||||
self.is_ai_speaking[session_id] = False
|
||||
Reference in New Issue
Block a user