feat: AI patient onboarding backend and documentation
This commit is contained in:
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# Ignore Frontend Directory
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frontend/
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# Dependencies
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node_modules/
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venv/
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env/
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.venv/
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# Environment Variables & Secrets
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.env
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.env.local
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.env.*.local
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*.env
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# Python Cache & Databases
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__pycache__/
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*.py[cod]
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*$py.class
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*.sqlite
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*.sqlite3
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*.db
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data/*.db
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data/audio/
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data/audio_temp/
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data/reports/
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data/transcripts/
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*.mp3
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*.wav
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*.ogg
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*.m4a
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.pytest_cache/
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# Build & Output
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dist/
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dist-ssr/
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build/
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*.local
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.cache/
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# IDE & OS
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.vscode/
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.idea/
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.DS_Store
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Thumbs.db
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# AI Patient Onboarding — Backend API Documentation
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This document provides a comprehensive guide for integrating with the **AI Patient Onboarding FastAPI Backend**. It covers all REST API endpoints, request/response models, voice integration flows, medical document processing, and EHR integration.
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---
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## Table of Contents
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1. [Architecture & Server Overview](#1-architecture--server-overview)
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2. [Environment Configuration (`.env`)](#2-environment-configuration-env)
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3. [Running the Backend Locally](#3-running-the-backend-locally)
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4. [Voice Onboarding API (`/v1/voice-onboarding`)](#4-voice-onboarding-api-v1voice-onboarding)
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- [4.1 Start Voice Session](#41-start-voice-session)
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- [4.2 Send Voice / Speech Turn](#42-send-voice--speech-turn)
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- [4.3 Fetch Synthesized Voice Audio](#43-fetch-synthesized-voice-audio)
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- [4.4 Complete & Confirm Session](#44-complete--confirm-session)
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5. [Text Onboarding API (`/v1/onboarding`)](#5-text-onboarding-api-v1onboarding)
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- [5.1 Start Text Session](#51-start-text-session)
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- [5.2 Send Text Message](#52-send-text-message)
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- [5.3 Complete Text Onboarding](#53-complete-text-onboarding)
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6. [Medical Document & Vision API (`/v1/documents`)](#6-medical-document--vision-api-v1documents)
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7. [System Health Check (`/health`)](#7-system-health-check-health)
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8. [Connecting External Hospital EMR / EHR System](#8-connecting-external-hospital-emr--ehr-system)
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9. [Multi-Language & Clinical Specialties](#9-multi-language--clinical-specialties)
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10. [Client Integration Code Samples](#10-client-integration-code-samples)
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---
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## 1. Architecture & Server Overview
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- **Framework:** FastAPI (Python 3.10+) with SQLAlchemy ORM
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- **Default Base URL:** `http://127.0.0.1:8000`
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- **Interactive Swagger Documentation:** `http://127.0.0.1:8000/docs`
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- **OpenAPI JSON Spec:** `http://127.0.0.1:8000/openapi.json`
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- **CORS:** Pre-configured to allow all origins (`*`) for easy React web and mobile client integration.
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```
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+------------------+ +-------------------------------+ +-----------------------+
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| Client Device | | FastAPI Backend Engine | | Hospital EHR / Database|
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+------------------+ +-------------------------------+ +-----------------------+
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| | |
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| --- POST /start ----------------> | (Initialize Session + State) |
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| <-- Greeting Text & Audio URL --- | (Synthesized TTS Audio) |
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| | |
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| --- POST /turn (Audio / Text) --> | (Sarvam STT -> GPT-4o -> State) |
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| <-- Next Question & Audio URL --- | |
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| | |
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| --- POST /upload (Prescription) > | (Gemini Vision OCR -> Extract Meds) |
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| <-- Extracted Findings ---------- | |
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| | |
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| --- POST /complete (confirmed) -> | (Extract EHR JSON) ---------------->| POST /patients
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| <-- Final Success Status -------- | <--- 200 OK Record Saved -----------|
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```
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---
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## 2. Environment Configuration (`.env`)
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Create or update the `.env` file in the `ai-patient-onboarding/` directory:
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```ini
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APP_NAME="AI Patient Onboarding"
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APP_ENV=development
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APP_VERSION=1.0.0
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# Database URL
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DATABASE_URL="sqlite:///./data/onboarding.db"
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# AI Provider API Keys
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OPENAI_API_KEY="sk-..."
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OPENAI_MODEL="gpt-4o-mini"
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ANTHROPIC_API_KEY="sk-ant-..."
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ANTHROPIC_MODEL="claude-3-5-sonnet-20241022"
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GEMINI_API_KEY="AIzaSy..."
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GEMINI_MODEL="gemini-1.5-flash"
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SARVAM_API_KEY="your_sarvam_key"
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# External Destination Hospital EHR / Patient API (Optional)
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PATIENT_API_BASE_URL="https://your-hospital-emr.com/api/v1"
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PATIENT_API_TOKEN="your_secure_bearer_token"
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```
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---
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## 3. Running the Backend Locally
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```bash
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# Navigate to the backend directory
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cd ai-patient-onboarding
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# Activate Python Virtual Environment
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# Windows PowerShell:
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.\venv\Scripts\Activate.ps1
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# Linux / macOS:
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# source venv/bin/activate
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# Install dependencies if not already installed
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pip install -r requirements.txt
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# Start FastAPI development server
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uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
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```
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---
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## 4. Voice Onboarding API (`/v1/voice-onboarding`)
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### 4.1 Start Voice Session
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Creates a new voice onboarding session, initializes dynamic state, and returns localized assistant greeting with audio.
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- **URL:** `POST /v1/voice-onboarding/start`
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- **Headers:** `Content-Type: application/json`
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- **Request Body:**
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```json
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{
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"patient": {
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"id": "PAT-9842",
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"name": "Arun Kumar",
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"age": 42,
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"gender": "male",
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"abha_id": "91-1234-5678-9012",
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"phone": "+91 98765 43210",
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"insurance_scheme": "PM-JAY",
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"dietary_habits": "Vegetarian"
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},
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"category": {
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"type": "speciality",
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"code": "cardio",
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"name": "Cardiology"
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},
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"language": "en-IN"
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}
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```
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- **Response (`200 OK`):**
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```json
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{
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"success": true,
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"session_id": "VOICE-78A1BF2C",
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"patient_id": "PAT-9842",
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"category": {
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"type": "speciality",
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"code": "cardio",
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"name": "Cardiology"
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},
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"language": "en-IN",
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"status": "in_progress",
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"stage": "greeting",
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"greeting_text": "Hello Arun Kumar, welcome to CureSelect Cardiology department. What symptoms or discomfort are you experiencing today?",
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"audio_url": "/v1/voice-onboarding/audio/voice_greeting_89bf1a.wav"
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}
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```
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---
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### 4.2 Send Voice / Speech Turn
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Submits the patient's recorded audio or transcribed speech. The backend runs multi-model Indic speech recognition (Sarvam/Whisper), extracts medical entities, updates conversational state, and generates the next question with audio TTS.
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- **URL:** `POST /v1/voice-onboarding/{session_id}/turn`
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- **Headers:** `Content-Type: multipart/form-data`
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- **Form Data Parameters:**
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- `audio` *(Optional File)*: WebM, WAV, or MP3 recorded audio file from the user's microphone.
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- `text_fallback` *(Optional String)*: Real-time browser speech recognition transcript fallback.
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- **Response (`200 OK`):**
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```json
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{
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"success": true,
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"session_id": "VOICE-78A1BF2C",
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"patient_transcript": "I have had severe chest pain and breathlessness since yesterday evening.",
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"ai_text": "I understand. Does the chest pain radiate to your left arm or jaw, and are you currently taking any blood pressure or heart medications?",
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"stage": "symptoms_inquiry",
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"completed": false,
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"audio_url": "/v1/voice-onboarding/audio/speech_reply_30ef2.wav"
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}
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```
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---
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### 4.3 Fetch Synthesized Voice Audio
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Streams audio file generated for assistant responses.
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- **URL:** `GET /v1/voice-onboarding/audio/{audio_id}`
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- **Response:** `audio/wav` or `audio/mpeg` binary stream.
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---
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### 4.4 Complete & Confirm Session
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Extracts complete structured clinical results. Supports a two-step confirmation workflow (review preview $\rightarrow$ permanent submission).
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- **URL:** `POST /v1/voice-onboarding/{session_id}/complete`
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- **Headers:** `Content-Type: application/json`
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#### Step A: Request Review Preview (`confirmed: false`)
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```json
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{
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"confirmed": false
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}
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```
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**Response (`200 OK`):**
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```json
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{
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"success": true,
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"session_id": "VOICE-78A1BF2C",
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"status": "awaiting_confirmation",
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"result": {
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"patient_id": "PAT-9842",
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"abha_id": "91-1234-5678-9012",
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"phone": "+91 98765 43210",
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"insurance_scheme": "PM-JAY",
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"category": {
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"type": "speciality",
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"code": "cardio",
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"name": "Cardiology"
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},
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"chief_complaint": "Acute onset retrosternal chest pain and dyspnea",
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"symptoms": [
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{
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"name": "Chest tightness",
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"duration": "1 day",
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"severity": "Moderate to Severe",
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"description": "Retrosternal pressure aggravated by exertion"
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}
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],
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"medical_history": ["Hypertension (5 years)"],
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"medications": ["Amlodipine 5mg OD"],
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"allergies": ["Penicillin"],
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"dietary_habits": "Vegetarian",
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"additional_information": "Non-smoker",
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"language": "en-IN",
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"onboarding_completed": true
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},
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"message": "Please review the collected information and confirm by voice or tap."
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}
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```
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#### Step B: Confirm & Submit to EHR (`confirmed: true`)
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```json
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{
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"confirmed": true
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}
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```
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**Response (`200 OK`):**
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```json
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{
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"success": true,
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"session_id": "VOICE-78A1BF2C",
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"status": "submitted_and_cleaned",
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"result": { ... },
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"message": "Voice onboarding successfully saved to patient record."
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}
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```
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---
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## 5. Text Onboarding API (`/v1/onboarding`)
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For text chat interfaces or non-voice kiosks.
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### 5.1 Start Text Session
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- **URL:** `POST /v1/onboarding/start`
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- **Body:** Same as Voice Start (`patient`, `category`, `language`).
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- **Response:** Returns `session_id`, `greeting_message`, and initial state.
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### 5.2 Send Text Message
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- **URL:** `POST /v1/onboarding/{session_id}/message`
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- **Body:**
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```json
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{
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"message": "I've had knee pain for 3 days after a run."
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}
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```
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- **Response:**
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```json
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{
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"success": true,
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"session_id": "ONB-84A12B1C",
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"stage": "symptoms_inquiry",
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"message": "Is the knee swollen or locking when you walk?",
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"status": "in_progress"
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}
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```
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### 5.3 Complete Text Onboarding
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- **URL:** `POST /v1/onboarding/{session_id}/complete`
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- **Body:** `{"confirmed": true}`
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---
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## 6. Medical Document & Vision API (`/v1/documents`)
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Uploads patient medical documents (prescriptions, discharge summaries, lab reports) to extract medications, allergies, and diagnoses using Gemini 1.5 Flash Vision, automatically injecting findings into the active session state.
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- **URL:** `POST /v1/documents/{session_id}/upload`
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- **Headers:** `Content-Type: multipart/form-data`
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- **Form Field:** `file` *(Binary image or PDF file)*
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- **Response (`200 OK`):**
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```json
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{
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"success": true,
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"session_id": "VOICE-78A1BF2C",
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"document_type": "prescription",
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"summary": "Prescription from Cardiologist dated 15-Aug-2024",
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"findings": ["Ejection Fraction 55%", "Normal Sinus Rhythm"],
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"medications_found": ["Atorvastatin 20mg", "Metoprolol 25mg"],
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"allergies_found": ["Sulfa drugs"],
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"message": "Medical document analyzed and integrated into onboarding session."
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}
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```
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---
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## 7. System Health Check (`/health`)
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- **URL:** `GET /health`
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- **Response (`200 OK`):**
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```json
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{
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"success": true,
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"service": "AI Patient Onboarding",
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"status": "healthy",
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"models": {
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"speech": "Sarvam AI (Indian Languages) / OpenAI Whisper",
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"conversation": "OpenAI GPT-4o-mini",
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"validation": "Claude 3.5 Sonnet",
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"vision_documents": "Gemini 1.5 Flash"
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}
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}
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```
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---
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## 8. Connecting External Hospital EMR / EHR System
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When onboarding is finalized, the backend invokes `PatientSubmissionService` located at:
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`app/services/patient_submission_service.py`
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### Customizing the Destination Forwarding:
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```python
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import httpx
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from app.config import PATIENT_API_BASE_URL, PATIENT_API_TOKEN
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class PatientSubmissionService:
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async def submit(self, result: dict) -> dict:
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if not PATIENT_API_BASE_URL:
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# Local fallback mock
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return {
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"success": True,
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"message": "Patient data saved locally.",
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"patient_id": result.get("patient_id"),
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}
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# Forward JSON payload to hospital central server
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async with httpx.AsyncClient(timeout=15.0) as client:
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headers = {
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"Authorization": f"Bearer {PATIENT_API_TOKEN}",
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"Content-Type": "application/json",
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}
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response = await client.post(
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f"{PATIENT_API_BASE_URL}/patients/{result.get('patient_id')}/onboarding",
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json=result,
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headers=headers,
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)
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if response.status_code in [200, 201]:
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return {"success": True, "data": response.json()}
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return {
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"success": False,
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"error": f"Hospital API error: {response.text}",
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}
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```
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---
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## 9. Multi-Language & Clinical Specialties
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### Supported Languages
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| Code | Language | Speech Engine |
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|---|---|---|
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| `en-IN` / `en` | English (Indian accent) | Sarvam / OpenAI Whisper |
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| `hi-IN` / `hi` | Hindi (हिन्दी) | Sarvam Bulbul / Whisper |
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| `ta-IN` / `ta` | Tamil (தமிழ்) | Sarvam Bulbul / Whisper |
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| `te-IN` / `te` | Telugu (తెలుగు) | Sarvam Bulbul / Whisper |
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| `kn-IN` / `kn` | Kannada (ಕನ್ನಡ) | Sarvam Bulbul / Whisper |
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| `bn-IN` / `bn` | Bengali (বাংলা) | Sarvam Bulbul / Whisper |
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| `ml-IN` / `ml` | Malayalam (മലയാളം) | Sarvam Bulbul / Whisper |
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| `mr-IN` / `mr` | Marathi (मराठी) | Sarvam Bulbul / Whisper |
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| `gu-IN` / `gu` | Gujarati (ગુજરાતી) | Sarvam Bulbul / Whisper |
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### Clinical Specialties Supported
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- `general_medicine` - General Medicine / Primary Care
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- `cardio` - Cardiology
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- `ortho` - Orthopedics
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- `peds` - Pediatrics
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- `derma` - Dermatology
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- `neuro` - Neurology
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- `gastro` - Gastroenterology
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- `ent` - ENT / Otorhinolaryngology
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- `gyn` - Obstetrics & Gynecology
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---
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## 10. Client Integration Code Samples
|
||||
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### 10.1 Python Client (`httpx`)
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```python
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import httpx
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import asyncio
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API_BASE = "http://127.0.0.1:8000"
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async def main():
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async with httpx.AsyncClient() as client:
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# 1. Start Session
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start_res = await client.post(f"{API_BASE}/v1/voice-onboarding/start", json={
|
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"patient": {"id": "P-1001", "name": "Deepak Sharma", "age": 45, "gender": "male"},
|
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"category": {"type": "speciality", "code": "cardio", "name": "Cardiology"},
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"language": "hi-IN"
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})
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session = start_res.json()
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session_id = session["session_id"]
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print(f"Session started: {session_id}")
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print(f"Greeting: {session['greeting_text']}")
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|
||||
# 2. Send Turn
|
||||
turn_res = await client.post(
|
||||
f"{API_BASE}/v1/voice-onboarding/{session_id}/turn",
|
||||
data={"text_fallback": "मुझे सीने में भारीपन महसूस हो रहा है"}
|
||||
)
|
||||
turn_data = turn_res.json()
|
||||
print(f"AI Response: {turn_data['ai_text']}")
|
||||
|
||||
# 3. Finalize & Submit
|
||||
complete_res = await client.post(
|
||||
f"{API_BASE}/v1/voice-onboarding/{session_id}/complete",
|
||||
json={"confirmed": True}
|
||||
)
|
||||
print(f"EHR Saved: {complete_res.json()}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
### 10.2 JavaScript / TypeScript (`fetch`)
|
||||
```typescript
|
||||
const API_BASE = "http://127.0.0.1:8000";
|
||||
|
||||
// Start voice onboarding
|
||||
export async function startVoiceOnboarding(patient: any, category: any, language = "en-IN") {
|
||||
const res = await fetch(`${API_BASE}/v1/voice-onboarding/start`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ patient, category, language })
|
||||
});
|
||||
return res.json();
|
||||
}
|
||||
|
||||
// Send audio turn
|
||||
export async function sendVoiceTurn(sessionId: string, audioBlob?: Blob, textFallback?: string) {
|
||||
const formData = new FormData();
|
||||
if (audioBlob) formData.append("audio", audioBlob, "speech.webm");
|
||||
if (textFallback) formData.append("text_fallback", textFallback);
|
||||
|
||||
const res = await fetch(`${API_BASE}/v1/voice-onboarding/${sessionId}/turn`, {
|
||||
method: "POST",
|
||||
body: formData
|
||||
});
|
||||
return res.json();
|
||||
}
|
||||
|
||||
// Upload medical report
|
||||
export async function uploadPrescription(sessionId: string, file: File) {
|
||||
const formData = new FormData();
|
||||
formData.append("file", file);
|
||||
|
||||
const res = await fetch(`${API_BASE}/v1/documents/${sessionId}/upload`, {
|
||||
method: "POST",
|
||||
body: formData
|
||||
});
|
||||
return res.json();
|
||||
}
|
||||
|
||||
// Complete onboarding
|
||||
export async function completeOnboarding(sessionId: string, confirmed: boolean) {
|
||||
const res = await fetch(`${API_BASE}/v1/voice-onboarding/${sessionId}/complete`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ confirmed })
|
||||
});
|
||||
return res.json();
|
||||
}
|
||||
```
|
||||
|
||||
### 10.3 cURL Commands
|
||||
|
||||
#### Start Session:
|
||||
```bash
|
||||
curl -X POST "http://127.0.0.1:8000/v1/voice-onboarding/start" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"patient": {"id": "P-101", "name": "Arun Kumar", "age": 40, "gender": "male"},
|
||||
"category": {"type": "general", "code": "general_medicine", "name": "General Medicine"},
|
||||
"language": "en-IN"
|
||||
}'
|
||||
```
|
||||
|
||||
#### Send Voice Turn (with text fallback):
|
||||
```bash
|
||||
curl -X POST "http://127.0.0.1:8000/v1/voice-onboarding/VOICE-XXXXX/turn" \
|
||||
-F "text_fallback=I have had high fever and sore throat since yesterday"
|
||||
```
|
||||
|
||||
#### Upload Prescription:
|
||||
```bash
|
||||
curl -X POST "http://127.0.0.1:8000/v1/documents/VOICE-XXXXX/upload" \
|
||||
-F "file=@/path/to/prescription.pdf"
|
||||
```
|
||||
|
||||
#### Confirm & Submit:
|
||||
```bash
|
||||
curl -X POST "http://127.0.0.1:8000/v1/voice-onboarding/VOICE-XXXXX/complete" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"confirmed": true}'
|
||||
```
|
||||
@@ -0,0 +1,17 @@
|
||||
venv/
|
||||
.env
|
||||
__pycache__/
|
||||
*.pyc
|
||||
.pytest_cache/
|
||||
data/audio/
|
||||
data/audio_temp/
|
||||
data/reports/
|
||||
data/transcripts/
|
||||
*.db
|
||||
*.sqlite
|
||||
*.sqlite3
|
||||
*.mp3
|
||||
*.wav
|
||||
*.ogg
|
||||
*.m4a
|
||||
|
||||
@@ -0,0 +1,532 @@
|
||||
# AI Patient Onboarding — Backend API Documentation
|
||||
|
||||
This document provides a comprehensive guide for integrating with the **AI Patient Onboarding FastAPI Backend**. It covers all REST API endpoints, request/response models, voice integration flows, medical document processing, and EHR integration.
|
||||
|
||||
---
|
||||
|
||||
## Table of Contents
|
||||
1. [Architecture & Server Overview](#1-architecture--server-overview)
|
||||
2. [Environment Configuration (`.env`)](#2-environment-configuration-env)
|
||||
3. [Running the Backend Locally](#3-running-the-backend-locally)
|
||||
4. [Voice Onboarding API (`/v1/voice-onboarding`)](#4-voice-onboarding-api-v1voice-onboarding)
|
||||
- [4.1 Start Voice Session](#41-start-voice-session)
|
||||
- [4.2 Send Voice / Speech Turn](#42-send-voice--speech-turn)
|
||||
- [4.3 Fetch Synthesized Voice Audio](#43-fetch-synthesized-voice-audio)
|
||||
- [4.4 Complete & Confirm Session](#44-complete--confirm-session)
|
||||
5. [Text Onboarding API (`/v1/onboarding`)](#5-text-onboarding-api-v1onboarding)
|
||||
- [5.1 Start Text Session](#51-start-text-session)
|
||||
- [5.2 Send Text Message](#52-send-text-message)
|
||||
- [5.3 Complete Text Onboarding](#53-complete-text-onboarding)
|
||||
6. [Medical Document & Vision API (`/v1/documents`)](#6-medical-document--vision-api-v1documents)
|
||||
7. [System Health Check (`/health`)](#7-system-health-check-health)
|
||||
8. [Connecting External Hospital EMR / EHR System](#8-connecting-external-hospital-emr--ehr-system)
|
||||
9. [Multi-Language & Clinical Specialties](#9-multi-language--clinical-specialties)
|
||||
10. [Client Integration Code Samples](#10-client-integration-code-samples)
|
||||
|
||||
---
|
||||
|
||||
## 1. Architecture & Server Overview
|
||||
|
||||
- **Framework:** FastAPI (Python 3.10+) with SQLAlchemy ORM
|
||||
- **Default Base URL:** `http://127.0.0.1:8000`
|
||||
- **Interactive Swagger Documentation:** `http://127.0.0.1:8000/docs`
|
||||
- **OpenAPI JSON Spec:** `http://127.0.0.1:8000/openapi.json`
|
||||
- **CORS:** Pre-configured to allow all origins (`*`) for easy React web and mobile client integration.
|
||||
|
||||
```
|
||||
+------------------+ +-------------------------------+ +-----------------------+
|
||||
| Client Device | | FastAPI Backend Engine | | Hospital EHR / Database|
|
||||
+------------------+ +-------------------------------+ +-----------------------+
|
||||
| | |
|
||||
| --- POST /start ----------------> | (Initialize Session + State) |
|
||||
| <-- Greeting Text & Audio URL --- | (Synthesized TTS Audio) |
|
||||
| | |
|
||||
| --- POST /turn (Audio / Text) --> | (Sarvam STT -> GPT-4o -> State) |
|
||||
| <-- Next Question & Audio URL --- | |
|
||||
| | |
|
||||
| --- POST /upload (Prescription) > | (Gemini Vision OCR -> Extract Meds) |
|
||||
| <-- Extracted Findings ---------- | |
|
||||
| | |
|
||||
| --- POST /complete (confirmed) -> | (Extract EHR JSON) ---------------->| POST /patients
|
||||
| <-- Final Success Status -------- | <--- 200 OK Record Saved -----------|
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 2. Environment Configuration (`.env`)
|
||||
|
||||
Create or update the `.env` file in the `ai-patient-onboarding/` directory:
|
||||
|
||||
```ini
|
||||
APP_NAME="AI Patient Onboarding"
|
||||
APP_ENV=development
|
||||
APP_VERSION=1.0.0
|
||||
|
||||
# Database URL
|
||||
DATABASE_URL="sqlite:///./data/onboarding.db"
|
||||
|
||||
# AI Provider API Keys
|
||||
OPENAI_API_KEY="sk-..."
|
||||
OPENAI_MODEL="gpt-4o-mini"
|
||||
|
||||
ANTHROPIC_API_KEY="sk-ant-..."
|
||||
ANTHROPIC_MODEL="claude-3-5-sonnet-20241022"
|
||||
|
||||
GEMINI_API_KEY="AIzaSy..."
|
||||
GEMINI_MODEL="gemini-1.5-flash"
|
||||
|
||||
SARVAM_API_KEY="your_sarvam_key"
|
||||
|
||||
# External Destination Hospital EHR / Patient API (Optional)
|
||||
PATIENT_API_BASE_URL="https://your-hospital-emr.com/api/v1"
|
||||
PATIENT_API_TOKEN="your_secure_bearer_token"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 3. Running the Backend Locally
|
||||
|
||||
```bash
|
||||
# Navigate to the backend directory
|
||||
cd ai-patient-onboarding
|
||||
|
||||
# Activate Python Virtual Environment
|
||||
# Windows PowerShell:
|
||||
.\venv\Scripts\Activate.ps1
|
||||
# Linux / macOS:
|
||||
# source venv/bin/activate
|
||||
|
||||
# Install dependencies if not already installed
|
||||
pip install -r requirements.txt
|
||||
|
||||
# Start FastAPI development server
|
||||
uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 4. Voice Onboarding API (`/v1/voice-onboarding`)
|
||||
|
||||
### 4.1 Start Voice Session
|
||||
|
||||
Creates a new voice onboarding session, initializes dynamic state, and returns localized assistant greeting with audio.
|
||||
|
||||
- **URL:** `POST /v1/voice-onboarding/start`
|
||||
- **Headers:** `Content-Type: application/json`
|
||||
- **Request Body:**
|
||||
```json
|
||||
{
|
||||
"patient": {
|
||||
"id": "PAT-9842",
|
||||
"name": "Arun Kumar",
|
||||
"age": 42,
|
||||
"gender": "male",
|
||||
"abha_id": "91-1234-5678-9012",
|
||||
"phone": "+91 98765 43210",
|
||||
"insurance_scheme": "PM-JAY",
|
||||
"dietary_habits": "Vegetarian"
|
||||
},
|
||||
"category": {
|
||||
"type": "speciality",
|
||||
"code": "cardio",
|
||||
"name": "Cardiology"
|
||||
},
|
||||
"language": "en-IN"
|
||||
}
|
||||
```
|
||||
|
||||
- **Response (`200 OK`):**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"session_id": "VOICE-78A1BF2C",
|
||||
"patient_id": "PAT-9842",
|
||||
"category": {
|
||||
"type": "speciality",
|
||||
"code": "cardio",
|
||||
"name": "Cardiology"
|
||||
},
|
||||
"language": "en-IN",
|
||||
"status": "in_progress",
|
||||
"stage": "greeting",
|
||||
"greeting_text": "Hello Arun Kumar, welcome to CureSelect Cardiology department. What symptoms or discomfort are you experiencing today?",
|
||||
"audio_url": "/v1/voice-onboarding/audio/voice_greeting_89bf1a.wav"
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### 4.2 Send Voice / Speech Turn
|
||||
|
||||
Submits the patient's recorded audio or transcribed speech. The backend runs multi-model Indic speech recognition (Sarvam/Whisper), extracts medical entities, updates conversational state, and generates the next question with audio TTS.
|
||||
|
||||
- **URL:** `POST /v1/voice-onboarding/{session_id}/turn`
|
||||
- **Headers:** `Content-Type: multipart/form-data`
|
||||
- **Form Data Parameters:**
|
||||
- `audio` *(Optional File)*: WebM, WAV, or MP3 recorded audio file from the user's microphone.
|
||||
- `text_fallback` *(Optional String)*: Real-time browser speech recognition transcript fallback.
|
||||
|
||||
- **Response (`200 OK`):**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"session_id": "VOICE-78A1BF2C",
|
||||
"patient_transcript": "I have had severe chest pain and breathlessness since yesterday evening.",
|
||||
"ai_text": "I understand. Does the chest pain radiate to your left arm or jaw, and are you currently taking any blood pressure or heart medications?",
|
||||
"stage": "symptoms_inquiry",
|
||||
"completed": false,
|
||||
"audio_url": "/v1/voice-onboarding/audio/speech_reply_30ef2.wav"
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### 4.3 Fetch Synthesized Voice Audio
|
||||
|
||||
Streams audio file generated for assistant responses.
|
||||
|
||||
- **URL:** `GET /v1/voice-onboarding/audio/{audio_id}`
|
||||
- **Response:** `audio/wav` or `audio/mpeg` binary stream.
|
||||
|
||||
---
|
||||
|
||||
### 4.4 Complete & Confirm Session
|
||||
|
||||
Extracts complete structured clinical results. Supports a two-step confirmation workflow (review preview $\rightarrow$ permanent submission).
|
||||
|
||||
- **URL:** `POST /v1/voice-onboarding/{session_id}/complete`
|
||||
- **Headers:** `Content-Type: application/json`
|
||||
|
||||
#### Step A: Request Review Preview (`confirmed: false`)
|
||||
```json
|
||||
{
|
||||
"confirmed": false
|
||||
}
|
||||
```
|
||||
**Response (`200 OK`):**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"session_id": "VOICE-78A1BF2C",
|
||||
"status": "awaiting_confirmation",
|
||||
"result": {
|
||||
"patient_id": "PAT-9842",
|
||||
"abha_id": "91-1234-5678-9012",
|
||||
"phone": "+91 98765 43210",
|
||||
"insurance_scheme": "PM-JAY",
|
||||
"category": {
|
||||
"type": "speciality",
|
||||
"code": "cardio",
|
||||
"name": "Cardiology"
|
||||
},
|
||||
"chief_complaint": "Acute onset retrosternal chest pain and dyspnea",
|
||||
"symptoms": [
|
||||
{
|
||||
"name": "Chest tightness",
|
||||
"duration": "1 day",
|
||||
"severity": "Moderate to Severe",
|
||||
"description": "Retrosternal pressure aggravated by exertion"
|
||||
}
|
||||
],
|
||||
"medical_history": ["Hypertension (5 years)"],
|
||||
"medications": ["Amlodipine 5mg OD"],
|
||||
"allergies": ["Penicillin"],
|
||||
"dietary_habits": "Vegetarian",
|
||||
"additional_information": "Non-smoker",
|
||||
"language": "en-IN",
|
||||
"onboarding_completed": true
|
||||
},
|
||||
"message": "Please review the collected information and confirm by voice or tap."
|
||||
}
|
||||
```
|
||||
|
||||
#### Step B: Confirm & Submit to EHR (`confirmed: true`)
|
||||
```json
|
||||
{
|
||||
"confirmed": true
|
||||
}
|
||||
```
|
||||
**Response (`200 OK`):**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"session_id": "VOICE-78A1BF2C",
|
||||
"status": "submitted_and_cleaned",
|
||||
"result": { ... },
|
||||
"message": "Voice onboarding successfully saved to patient record."
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 5. Text Onboarding API (`/v1/onboarding`)
|
||||
|
||||
For text chat interfaces or non-voice kiosks.
|
||||
|
||||
### 5.1 Start Text Session
|
||||
- **URL:** `POST /v1/onboarding/start`
|
||||
- **Body:** Same as Voice Start (`patient`, `category`, `language`).
|
||||
- **Response:** Returns `session_id`, `greeting_message`, and initial state.
|
||||
|
||||
### 5.2 Send Text Message
|
||||
- **URL:** `POST /v1/onboarding/{session_id}/message`
|
||||
- **Body:**
|
||||
```json
|
||||
{
|
||||
"message": "I've had knee pain for 3 days after a run."
|
||||
}
|
||||
```
|
||||
- **Response:**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"session_id": "ONB-84A12B1C",
|
||||
"stage": "symptoms_inquiry",
|
||||
"message": "Is the knee swollen or locking when you walk?",
|
||||
"status": "in_progress"
|
||||
}
|
||||
```
|
||||
|
||||
### 5.3 Complete Text Onboarding
|
||||
- **URL:** `POST /v1/onboarding/{session_id}/complete`
|
||||
- **Body:** `{"confirmed": true}`
|
||||
|
||||
---
|
||||
|
||||
## 6. Medical Document & Vision API (`/v1/documents`)
|
||||
|
||||
Uploads patient medical documents (prescriptions, discharge summaries, lab reports) to extract medications, allergies, and diagnoses using Gemini 1.5 Flash Vision, automatically injecting findings into the active session state.
|
||||
|
||||
- **URL:** `POST /v1/documents/{session_id}/upload`
|
||||
- **Headers:** `Content-Type: multipart/form-data`
|
||||
- **Form Field:** `file` *(Binary image or PDF file)*
|
||||
|
||||
- **Response (`200 OK`):**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"session_id": "VOICE-78A1BF2C",
|
||||
"document_type": "prescription",
|
||||
"summary": "Prescription from Cardiologist dated 15-Aug-2024",
|
||||
"findings": ["Ejection Fraction 55%", "Normal Sinus Rhythm"],
|
||||
"medications_found": ["Atorvastatin 20mg", "Metoprolol 25mg"],
|
||||
"allergies_found": ["Sulfa drugs"],
|
||||
"message": "Medical document analyzed and integrated into onboarding session."
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 7. System Health Check (`/health`)
|
||||
|
||||
- **URL:** `GET /health`
|
||||
- **Response (`200 OK`):**
|
||||
```json
|
||||
{
|
||||
"success": true,
|
||||
"service": "AI Patient Onboarding",
|
||||
"status": "healthy",
|
||||
"models": {
|
||||
"speech": "Sarvam AI (Indian Languages) / OpenAI Whisper",
|
||||
"conversation": "OpenAI GPT-4o-mini",
|
||||
"validation": "Claude 3.5 Sonnet",
|
||||
"vision_documents": "Gemini 1.5 Flash"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 8. Connecting External Hospital EMR / EHR System
|
||||
|
||||
When onboarding is finalized, the backend invokes `PatientSubmissionService` located at:
|
||||
`app/services/patient_submission_service.py`
|
||||
|
||||
### Customizing the Destination Forwarding:
|
||||
```python
|
||||
import httpx
|
||||
from app.config import PATIENT_API_BASE_URL, PATIENT_API_TOKEN
|
||||
|
||||
class PatientSubmissionService:
|
||||
async def submit(self, result: dict) -> dict:
|
||||
if not PATIENT_API_BASE_URL:
|
||||
# Local fallback mock
|
||||
return {
|
||||
"success": True,
|
||||
"message": "Patient data saved locally.",
|
||||
"patient_id": result.get("patient_id"),
|
||||
}
|
||||
|
||||
# Forward JSON payload to hospital central server
|
||||
async with httpx.AsyncClient(timeout=15.0) as client:
|
||||
headers = {
|
||||
"Authorization": f"Bearer {PATIENT_API_TOKEN}",
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
response = await client.post(
|
||||
f"{PATIENT_API_BASE_URL}/patients/{result.get('patient_id')}/onboarding",
|
||||
json=result,
|
||||
headers=headers,
|
||||
)
|
||||
|
||||
if response.status_code in [200, 201]:
|
||||
return {"success": True, "data": response.json()}
|
||||
|
||||
return {
|
||||
"success": False,
|
||||
"error": f"Hospital API error: {response.text}",
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 9. Multi-Language & Clinical Specialties
|
||||
|
||||
### Supported Languages
|
||||
| Code | Language | Speech Engine |
|
||||
|---|---|---|
|
||||
| `en-IN` / `en` | English (Indian accent) | Sarvam / OpenAI Whisper |
|
||||
| `hi-IN` / `hi` | Hindi (हिन्दी) | Sarvam Bulbul / Whisper |
|
||||
| `ta-IN` / `ta` | Tamil (தமிழ்) | Sarvam Bulbul / Whisper |
|
||||
| `te-IN` / `te` | Telugu (తెలుగు) | Sarvam Bulbul / Whisper |
|
||||
| `kn-IN` / `kn` | Kannada (ಕನ್ನಡ) | Sarvam Bulbul / Whisper |
|
||||
| `bn-IN` / `bn` | Bengali (বাংলা) | Sarvam Bulbul / Whisper |
|
||||
| `ml-IN` / `ml` | Malayalam (മലയാളം) | Sarvam Bulbul / Whisper |
|
||||
| `mr-IN` / `mr` | Marathi (मराठी) | Sarvam Bulbul / Whisper |
|
||||
| `gu-IN` / `gu` | Gujarati (ગુજરાતી) | Sarvam Bulbul / Whisper |
|
||||
|
||||
### Clinical Specialties Supported
|
||||
- `general_medicine` - General Medicine / Primary Care
|
||||
- `cardio` - Cardiology
|
||||
- `ortho` - Orthopedics
|
||||
- `peds` - Pediatrics
|
||||
- `derma` - Dermatology
|
||||
- `neuro` - Neurology
|
||||
- `gastro` - Gastroenterology
|
||||
- `ent` - ENT / Otorhinolaryngology
|
||||
- `gyn` - Obstetrics & Gynecology
|
||||
|
||||
---
|
||||
|
||||
## 10. Client Integration Code Samples
|
||||
|
||||
### 10.1 Python Client (`httpx`)
|
||||
```python
|
||||
import httpx
|
||||
import asyncio
|
||||
|
||||
API_BASE = "http://127.0.0.1:8000"
|
||||
|
||||
async def main():
|
||||
async with httpx.AsyncClient() as client:
|
||||
# 1. Start Session
|
||||
start_res = await client.post(f"{API_BASE}/v1/voice-onboarding/start", json={
|
||||
"patient": {"id": "P-1001", "name": "Deepak Sharma", "age": 45, "gender": "male"},
|
||||
"category": {"type": "speciality", "code": "cardio", "name": "Cardiology"},
|
||||
"language": "hi-IN"
|
||||
})
|
||||
session = start_res.json()
|
||||
session_id = session["session_id"]
|
||||
print(f"Session started: {session_id}")
|
||||
print(f"Greeting: {session['greeting_text']}")
|
||||
|
||||
# 2. Send Turn
|
||||
turn_res = await client.post(
|
||||
f"{API_BASE}/v1/voice-onboarding/{session_id}/turn",
|
||||
data={"text_fallback": "मुझे सीने में भारीपन महसूस हो रहा है"}
|
||||
)
|
||||
turn_data = turn_res.json()
|
||||
print(f"AI Response: {turn_data['ai_text']}")
|
||||
|
||||
# 3. Finalize & Submit
|
||||
complete_res = await client.post(
|
||||
f"{API_BASE}/v1/voice-onboarding/{session_id}/complete",
|
||||
json={"confirmed": True}
|
||||
)
|
||||
print(f"EHR Saved: {complete_res.json()}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
### 10.2 JavaScript / TypeScript (`fetch`)
|
||||
```typescript
|
||||
const API_BASE = "http://127.0.0.1:8000";
|
||||
|
||||
// Start voice onboarding
|
||||
export async function startVoiceOnboarding(patient: any, category: any, language = "en-IN") {
|
||||
const res = await fetch(`${API_BASE}/v1/voice-onboarding/start`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ patient, category, language })
|
||||
});
|
||||
return res.json();
|
||||
}
|
||||
|
||||
// Send audio turn
|
||||
export async function sendVoiceTurn(sessionId: string, audioBlob?: Blob, textFallback?: string) {
|
||||
const formData = new FormData();
|
||||
if (audioBlob) formData.append("audio", audioBlob, "speech.webm");
|
||||
if (textFallback) formData.append("text_fallback", textFallback);
|
||||
|
||||
const res = await fetch(`${API_BASE}/v1/voice-onboarding/${sessionId}/turn`, {
|
||||
method: "POST",
|
||||
body: formData
|
||||
});
|
||||
return res.json();
|
||||
}
|
||||
|
||||
// Upload medical report
|
||||
export async function uploadPrescription(sessionId: string, file: File) {
|
||||
const formData = new FormData();
|
||||
formData.append("file", file);
|
||||
|
||||
const res = await fetch(`${API_BASE}/v1/documents/${sessionId}/upload`, {
|
||||
method: "POST",
|
||||
body: formData
|
||||
});
|
||||
return res.json();
|
||||
}
|
||||
|
||||
// Complete onboarding
|
||||
export async function completeOnboarding(sessionId: string, confirmed: boolean) {
|
||||
const res = await fetch(`${API_BASE}/v1/voice-onboarding/${sessionId}/complete`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({ confirmed })
|
||||
});
|
||||
return res.json();
|
||||
}
|
||||
```
|
||||
|
||||
### 10.3 cURL Commands
|
||||
|
||||
#### Start Session:
|
||||
```bash
|
||||
curl -X POST "http://127.0.0.1:8000/v1/voice-onboarding/start" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"patient": {"id": "P-101", "name": "Arun Kumar", "age": 40, "gender": "male"},
|
||||
"category": {"type": "general", "code": "general_medicine", "name": "General Medicine"},
|
||||
"language": "en-IN"
|
||||
}'
|
||||
```
|
||||
|
||||
#### Send Voice Turn (with text fallback):
|
||||
```bash
|
||||
curl -X POST "http://127.0.0.1:8000/v1/voice-onboarding/VOICE-XXXXX/turn" \
|
||||
-F "text_fallback=I have had high fever and sore throat since yesterday"
|
||||
```
|
||||
|
||||
#### Upload Prescription:
|
||||
```bash
|
||||
curl -X POST "http://127.0.0.1:8000/v1/documents/VOICE-XXXXX/upload" \
|
||||
-F "file=@/path/to/prescription.pdf"
|
||||
```
|
||||
|
||||
#### Confirm & Submit:
|
||||
```bash
|
||||
curl -X POST "http://127.0.0.1:8000/v1/voice-onboarding/VOICE-XXXXX/complete" \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"confirmed": true}'
|
||||
```
|
||||
@@ -0,0 +1 @@
|
||||
"""App package initialization."""
|
||||
@@ -0,0 +1 @@
|
||||
"""AI package initialization."""
|
||||
@@ -0,0 +1,69 @@
|
||||
import json
|
||||
from app.config import ANTHROPIC_API_KEY, ANTHROPIC_MODEL
|
||||
|
||||
|
||||
class ClaudeClient:
|
||||
|
||||
def __init__(self):
|
||||
self.api_key = ANTHROPIC_API_KEY
|
||||
self.model = ANTHROPIC_MODEL
|
||||
|
||||
async def validate_extraction(
|
||||
self,
|
||||
patient_context: dict,
|
||||
specialty_config: dict,
|
||||
conversation_history: list,
|
||||
extracted_data: dict,
|
||||
) -> dict:
|
||||
if not self.api_key or self.api_key == "your_claude_key":
|
||||
return {
|
||||
"valid": True,
|
||||
"missing": [],
|
||||
"recommendation": "continue",
|
||||
"hallucinations_detected": False,
|
||||
}
|
||||
|
||||
try:
|
||||
from anthropic import AsyncAnthropic
|
||||
client = AsyncAnthropic(api_key=self.api_key)
|
||||
|
||||
prompt = f"""
|
||||
You are a senior clinical auditor and AI quality control specialist.
|
||||
Your task is to validate whether the extracted patient data accurately reflects the actual patient conversation without hallucination or omitted critical criteria.
|
||||
|
||||
PATIENT CONTEXT:
|
||||
{json.dumps(patient_context, indent=2)}
|
||||
|
||||
SPECIALTY REQUIREMENTS:
|
||||
{json.dumps(specialty_config, indent=2)}
|
||||
|
||||
CONVERSATION HISTORY:
|
||||
{json.dumps(conversation_history, indent=2)}
|
||||
|
||||
PROPOSED EXTRACTED STATE:
|
||||
{json.dumps(extracted_data, indent=2)}
|
||||
|
||||
Return ONLY a JSON response:
|
||||
{{
|
||||
"valid": true/false,
|
||||
"missing": ["list of critical missing focus items for this specialty, if any"],
|
||||
"recommendation": "continue or ask_severity or ask_duration etc.",
|
||||
"hallucinations_detected": false
|
||||
}}
|
||||
"""
|
||||
response = await client.messages.create(
|
||||
model=self.model,
|
||||
max_tokens=1024,
|
||||
system="You are a clinical AI quality controller. Output only valid JSON.",
|
||||
messages=[{"role": "user", "content": prompt}],
|
||||
)
|
||||
content = response.content[0].text
|
||||
return json.loads(content)
|
||||
except Exception as e:
|
||||
print(f"[Claude Validation Error]: {e}")
|
||||
return {
|
||||
"valid": True,
|
||||
"missing": [],
|
||||
"recommendation": "continue",
|
||||
"hallucinations_detected": False,
|
||||
}
|
||||
@@ -0,0 +1,33 @@
|
||||
import json
|
||||
|
||||
|
||||
def build_onboarding_context(
|
||||
session,
|
||||
state,
|
||||
conversation_history,
|
||||
):
|
||||
|
||||
return {
|
||||
"patient": {
|
||||
"id": session.patient_id,
|
||||
"name": session.patient_name,
|
||||
"age": session.age,
|
||||
"gender": session.gender,
|
||||
},
|
||||
|
||||
"category": {
|
||||
"type": session.category_type,
|
||||
"code": session.category_code,
|
||||
"name": session.category_name,
|
||||
},
|
||||
|
||||
"language": session.language,
|
||||
|
||||
"current_stage": session.stage,
|
||||
|
||||
"current_state": json.loads(
|
||||
state.state_json
|
||||
),
|
||||
|
||||
"conversation": conversation_history,
|
||||
}
|
||||
@@ -0,0 +1,73 @@
|
||||
import json
|
||||
import base64
|
||||
from app.config import GEMINI_API_KEY, GEMINI_MODEL
|
||||
|
||||
|
||||
class GeminiClient:
|
||||
|
||||
def __init__(self):
|
||||
self.api_key = GEMINI_API_KEY
|
||||
self.model = GEMINI_MODEL
|
||||
|
||||
async def extract_from_document(
|
||||
self,
|
||||
file_bytes: bytes,
|
||||
mime_type: str,
|
||||
patient_context: dict,
|
||||
) -> dict:
|
||||
if not self.api_key or self.api_key == "your_gemini_key":
|
||||
return {
|
||||
"document_type": "medical_report",
|
||||
"extracted_text": "Sample HbA1c 7.2%, Blood Pressure 130/85 mmHg",
|
||||
"findings": [
|
||||
{"test": "HbA1c", "value": "7.2", "unit": "%", "interpretation": "Elevated"},
|
||||
{"test": "Blood Pressure", "value": "130/85", "unit": "mmHg", "interpretation": "Stage 1"},
|
||||
],
|
||||
"medications_found": ["Metformin 500mg"],
|
||||
"allergies_found": [],
|
||||
"summary": "Report indicates elevated HbA1c and prehypertension.",
|
||||
}
|
||||
|
||||
try:
|
||||
import google.generativeai as genai
|
||||
genai.configure(api_key=self.api_key)
|
||||
model = genai.GenerativeModel(self.model)
|
||||
|
||||
prompt = f"""
|
||||
You are a medical document extraction AI.
|
||||
Analyze this medical report / prescription / lab result for patient {patient_context.get('name', 'Patient')}.
|
||||
Extract all lab tests, numeric values, units, diagnosed conditions, medications, and allergies into structured JSON.
|
||||
|
||||
Return ONLY JSON:
|
||||
{{
|
||||
"document_type": "lab_report | prescription | discharge_summary | other",
|
||||
"extracted_text": "brief summary text",
|
||||
"findings": [
|
||||
{{
|
||||
"test": "test name",
|
||||
"value": "numeric or text value",
|
||||
"unit": "unit or null",
|
||||
"interpretation": "Normal | High | Low | etc."
|
||||
}}
|
||||
],
|
||||
"medications_found": ["medication 1", "medication 2"],
|
||||
"allergies_found": ["allergy 1"],
|
||||
"summary": "short clinical summary of the document"
|
||||
}}
|
||||
"""
|
||||
part = {
|
||||
"mime_type": mime_type,
|
||||
"data": file_bytes,
|
||||
}
|
||||
response = await model.generate_content_async([part, prompt])
|
||||
cleaned_text = response.text.replace("```json", "").replace("```", "").strip()
|
||||
return json.loads(cleaned_text)
|
||||
except Exception as e:
|
||||
print(f"[Gemini Extraction Error]: {e}")
|
||||
return {
|
||||
"document_type": "unknown",
|
||||
"findings": [],
|
||||
"medications_found": [],
|
||||
"allergies_found": [],
|
||||
"summary": f"Could not extract document details: {e}",
|
||||
}
|
||||
@@ -0,0 +1,39 @@
|
||||
from openai import AsyncOpenAI
|
||||
from app.config import OPENAI_API_KEY, OPENAI_MODEL
|
||||
|
||||
|
||||
class OpenAIClient:
|
||||
|
||||
_client = None
|
||||
|
||||
def __init__(self):
|
||||
self.api_key = OPENAI_API_KEY
|
||||
self.model = OPENAI_MODEL
|
||||
if OpenAIClient._client is None and self.api_key:
|
||||
OpenAIClient._client = AsyncOpenAI(
|
||||
api_key=self.api_key,
|
||||
timeout=12.0,
|
||||
max_retries=1,
|
||||
)
|
||||
|
||||
async def generate_response(
|
||||
self,
|
||||
system_prompt: str,
|
||||
user_message: str,
|
||||
) -> str:
|
||||
client = OpenAIClient._client or AsyncOpenAI(
|
||||
api_key=self.api_key or "sk-dummy-key-placeholder",
|
||||
timeout=12.0,
|
||||
)
|
||||
response = await client.chat.completions.create(
|
||||
model=self.model or "gpt-4o-mini",
|
||||
messages=[
|
||||
{"role": "system", "content": system_prompt},
|
||||
{"role": "user", "content": user_message},
|
||||
],
|
||||
response_format={"type": "json_object"},
|
||||
temperature=0.35,
|
||||
max_completion_tokens=140,
|
||||
)
|
||||
return response.choices[0].message.content
|
||||
|
||||
@@ -0,0 +1,149 @@
|
||||
import json
|
||||
|
||||
from app.ai.openai_client import OpenAIClient
|
||||
from app.ai.prompts import build_system_prompt
|
||||
from app.ai.validator import validate_ai_response
|
||||
from app.config import OPENAI_API_KEY
|
||||
from app.specialties.registry import get_specialty_config
|
||||
|
||||
|
||||
class AIOrchestrator:
|
||||
|
||||
def __init__(self):
|
||||
self.openai = OpenAIClient()
|
||||
|
||||
async def process(
|
||||
self,
|
||||
session,
|
||||
state,
|
||||
conversation_history,
|
||||
user_message,
|
||||
):
|
||||
specialty = get_specialty_config(
|
||||
session.category_code
|
||||
)
|
||||
|
||||
current_state = json.loads(
|
||||
state.state_json
|
||||
)
|
||||
|
||||
context = {
|
||||
"patient": {
|
||||
"id": session.patient_id,
|
||||
"name": session.patient_name,
|
||||
"age": session.age,
|
||||
"gender": session.gender,
|
||||
},
|
||||
"category": {
|
||||
"type": session.category_type,
|
||||
"code": session.category_code,
|
||||
"name": session.category_name,
|
||||
},
|
||||
"specialty_config": specialty,
|
||||
"language": session.language,
|
||||
"current_stage": session.stage,
|
||||
"current_state": current_state,
|
||||
"conversation_history": conversation_history,
|
||||
"new_patient_message": user_message,
|
||||
}
|
||||
|
||||
prompt = build_system_prompt(context)
|
||||
|
||||
# Real OpenAI Call if valid API key is set
|
||||
if OPENAI_API_KEY and OPENAI_API_KEY.strip() not in ["your_openai_api_key", "your_openai_key", ""]:
|
||||
try:
|
||||
raw_response = await self.openai.generate_response(
|
||||
system_prompt=prompt,
|
||||
user_message=user_message,
|
||||
)
|
||||
parsed = json.loads(raw_response)
|
||||
validated = validate_ai_response(parsed)
|
||||
return validated.model_dump()
|
||||
except Exception as e:
|
||||
print(f"[OpenAI Orchestrator Error]: {e}")
|
||||
|
||||
# Fallback simulator for offline development with concise clinical flow
|
||||
parsed = self._simulate_specialty_response(session, specialty, user_message, len(conversation_history))
|
||||
validated = validate_ai_response(parsed)
|
||||
return validated.model_dump()
|
||||
|
||||
def _simulate_specialty_response(self, session, specialty, user_message, turn_count=0):
|
||||
stage = session.stage
|
||||
code = specialty.get("code", "general")
|
||||
msg_lower = user_message.lower().strip()
|
||||
is_negative = msg_lower in ["no", "no more", "nothing", "nothing else", "know nothing", "none", "illness none"]
|
||||
|
||||
# Fast conclusion if patient says "No" or already had 4 turns
|
||||
if is_negative or turn_count >= 6:
|
||||
return {
|
||||
"reply": "Thank you. I have recorded your intake details for the doctor to review.",
|
||||
"next_stage": "confirmation",
|
||||
"state_updates": {
|
||||
"reason_for_visit": None,
|
||||
"symptoms": [],
|
||||
"medical_history": ["None reported"] if is_negative else [],
|
||||
"medications": [],
|
||||
"allergies": ["None"] if is_negative else []
|
||||
},
|
||||
"completed": True
|
||||
}
|
||||
|
||||
if stage == "greeting":
|
||||
if code == "cardiology":
|
||||
reply = "Could you please share what cardiac or chest symptoms you are experiencing?"
|
||||
else:
|
||||
reply = "Could you please describe what primary symptoms you are experiencing today?"
|
||||
return {
|
||||
"reply": reply,
|
||||
"next_stage": "reason_for_visit",
|
||||
"state_updates": {
|
||||
"reason_for_visit": user_message,
|
||||
"symptoms": [{"name": user_message, "duration": "recent"}],
|
||||
"medical_history": [],
|
||||
"medications": [],
|
||||
"allergies": []
|
||||
},
|
||||
"completed": False
|
||||
}
|
||||
elif stage == "reason_for_visit":
|
||||
reply = "How long have you had these symptoms, and have you taken any medicines?"
|
||||
return {
|
||||
"reply": reply,
|
||||
"next_stage": "medications",
|
||||
"state_updates": {
|
||||
"reason_for_visit": user_message,
|
||||
"symptoms": [{"name": user_message, "duration": "reported"}],
|
||||
"medical_history": [],
|
||||
"medications": [],
|
||||
"allergies": []
|
||||
},
|
||||
"completed": False
|
||||
}
|
||||
elif stage == "medications":
|
||||
reply = "Thank you. Do you have any known drug allergies or existing health conditions?"
|
||||
return {
|
||||
"reply": reply,
|
||||
"next_stage": "allergies",
|
||||
"state_updates": {
|
||||
"reason_for_visit": None,
|
||||
"symptoms": [],
|
||||
"medical_history": [],
|
||||
"medications": [user_message] if not is_negative else [],
|
||||
"allergies": []
|
||||
},
|
||||
"completed": False
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"reply": "Thank you. I have recorded your intake details for the doctor to review.",
|
||||
"next_stage": "confirmation",
|
||||
"state_updates": {
|
||||
"reason_for_visit": None,
|
||||
"symptoms": [],
|
||||
"medical_history": [],
|
||||
"medications": [],
|
||||
"allergies": ["None"] if is_negative else [user_message]
|
||||
},
|
||||
"completed": True
|
||||
}
|
||||
|
||||
@@ -0,0 +1,89 @@
|
||||
import json
|
||||
|
||||
|
||||
def build_system_prompt(context: dict) -> str:
|
||||
specialty = context.get("specialty_config", {})
|
||||
patient = context.get("patient", {})
|
||||
language = context.get("language", "ta-IN")
|
||||
stage = context.get("current_stage", "greeting")
|
||||
current_state = context.get("current_state", {})
|
||||
conversation_history = context.get("conversation_history", [])
|
||||
|
||||
turn_count = len(conversation_history)
|
||||
|
||||
lang_instructions = {
|
||||
"ta-IN": (
|
||||
"LANGUAGE & ACCENT: Professional, Respectful, and Articulate Tamil (தமிழ்).\n"
|
||||
"- Speak with the professional, calm, and reassuring demeanor of a senior hospital clinical coordinator.\n"
|
||||
"- Use polite, respectful Tamil words (e.g. 'வணக்கம்', 'கூற முடியுமா?', 'புரிகிறது', 'நன்றி').\n"
|
||||
"- Keep sentence structure clear, elegant, and under 14 words."
|
||||
),
|
||||
"hi-IN": (
|
||||
"LANGUAGE & ACCENT: Professional, Respectful, and Clear Hindi (हिन्दी).\n"
|
||||
"- Speak with the dignified and polite tone of an experienced hospital triage officer.\n"
|
||||
"- Use respectful language with 'आप' and clear clinical clarity.\n"
|
||||
"- Keep sentence structure polite, concise, and under 14 words."
|
||||
),
|
||||
"en-IN": (
|
||||
"LANGUAGE & ACCENT: Polished, Professional Indian Healthcare English.\n"
|
||||
"- Speak with a calm, articulate, and reassuring clinical tone.\n"
|
||||
"- Use clear, professional phrasing (e.g. 'Thank you. I have recorded your symptoms for the doctor.').\n"
|
||||
"- Keep each response concise, respectful, and under 14 words."
|
||||
),
|
||||
"te-IN": (
|
||||
"LANGUAGE & ACCENT: Professional and Respectful Telugu (తెలుగు).\n"
|
||||
"- Speak with dignified, clear hospital intake phrasing (under 14 words)."
|
||||
),
|
||||
"kn-IN": (
|
||||
"LANGUAGE & ACCENT: Professional and Respectful Kannada (ಕನ್ನಡ).\n"
|
||||
"- Speak with dignified, clear hospital intake phrasing (under 14 words)."
|
||||
),
|
||||
}
|
||||
|
||||
selected_lang_rule = lang_instructions.get(language, lang_instructions["en-IN"])
|
||||
|
||||
return f"""You are a senior, professional Hospital Patient Intake Assistant at CureSelect Healthcare.
|
||||
|
||||
{selected_lang_rule}
|
||||
|
||||
CRITICAL CLINICAL INTAKE PRINCIPLES (AVOID UNWANTED QUESTIONS):
|
||||
1. **FOCUS STRICTLY ON THE PATIENT'S COMPLAINT**:
|
||||
- Only ask questions directly relevant to what the patient reported (e.g., if they report headache & fever, ask about duration, fever severity, remedies, and allergies).
|
||||
- **DO NOT randomly cross-examine unrelated organ systems** (e.g., NEVER ask about digestive issues, acidity, loose stools, or stomach unless the patient specifically mentioned stomach problems!).
|
||||
|
||||
2. **FAST & CONCISE INTAKE (MAX 4 TURNS TOTAL)**:
|
||||
- Flow: Chief Complaint -> Duration -> Medications Taken -> Drug Allergies -> COMPLETE.
|
||||
- Once symptoms, duration, medicines, and allergies are collected, **IMMEDIATELY SET "completed": true**.
|
||||
|
||||
3. **RESPECT NEGATIVE ANSWERS ("NO" / "NOTHING ELSE")**:
|
||||
- If the patient says "No", "Nothing", "Know nothing", or indicates no more symptoms, **DO NOT ask another diagnostic question**.
|
||||
- Conclude immediately: *"Thank you. I have recorded your details for the doctor to review."* with `"completed": true` and `"next_stage": "confirmation"`.
|
||||
|
||||
4. **CONCISENESS**: Exactly ONE spoken sentence (maximum 12 to 14 words).
|
||||
5. **NO ARTIFACTS**: No bullet points, no brackets, no markdown formatting. Deliver clean spoken dialogue.
|
||||
6. **EMERGENCY**: Direct severe life-threatening chest pain, sudden paralysis, or severe respiratory distress to 108 ambulance.
|
||||
|
||||
PATIENT: {patient.get("name", "Patient")}, {patient.get("age", "")}y, {patient.get("gender", "")}
|
||||
DEPARTMENT: {specialty.get("name", "General Intake")}
|
||||
CURRENT STAGE: {stage} (Turn #{turn_count})
|
||||
CURRENT STRUCTURED STATE:
|
||||
{json.dumps(current_state, ensure_ascii=False)}
|
||||
|
||||
CONVERSATION HISTORY:
|
||||
{json.dumps(conversation_history, ensure_ascii=False)}
|
||||
|
||||
OUTPUT JSON ONLY:
|
||||
{{
|
||||
"reply": "1 concise, respectful sentence (max 14 words)",
|
||||
"next_stage": "greeting" | "reason_for_visit" | "symptoms" | "medical_history" | "medications" | "allergies" | "confirmation",
|
||||
"completed": true | false,
|
||||
"state_updates": {{
|
||||
"reason_for_visit": {{"status": "reported" | "not_collected", "value": "..."}} or null,
|
||||
"symptoms": {{"status": "reported" | "not_collected", "items": [{{"name": "...", "duration": "...", "severity": "..."}}]}} or null,
|
||||
"medical_history": {{"status": "reported" | "confirmed_none", "items": ["..."]}} or null,
|
||||
"medications": {{"status": "reported" | "confirmed_none", "items": ["..."]}} or null,
|
||||
"allergies": {{"status": "reported" | "confirmed_none", "items": ["..."]}} or null
|
||||
}}
|
||||
}}
|
||||
"""
|
||||
|
||||
@@ -0,0 +1,84 @@
|
||||
import base64
|
||||
import httpx2
|
||||
from app.config import SARVAM_API_KEY
|
||||
|
||||
|
||||
class SarvamClient:
|
||||
|
||||
_client = None
|
||||
|
||||
def __init__(self):
|
||||
self.api_key = SARVAM_API_KEY
|
||||
self.base_url = "https://api.sarvam.ai"
|
||||
|
||||
def _get_client(self) -> httpx2.AsyncClient:
|
||||
if SarvamClient._client is None:
|
||||
SarvamClient._client = httpx2.AsyncClient(
|
||||
timeout=12.0,
|
||||
limits=httpx2.Limits(max_keepalive_connections=5, max_connections=10),
|
||||
)
|
||||
return SarvamClient._client
|
||||
|
||||
async def speech_to_text(
|
||||
self,
|
||||
audio_bytes: bytes,
|
||||
filename: str = "audio.webm",
|
||||
language_code: str = "ta-IN",
|
||||
) -> str:
|
||||
if not self.api_key or self.api_key == "your_sarvam_key":
|
||||
return ""
|
||||
|
||||
client = self._get_client()
|
||||
files = {"file": (filename, audio_bytes, "audio/webm")}
|
||||
data = {"model": "saaras:v2", "language_code": language_code}
|
||||
headers = {"api-subscription-key": self.api_key}
|
||||
|
||||
try:
|
||||
res = await client.post(
|
||||
f"{self.base_url}/speech-to-text",
|
||||
files=files,
|
||||
data=data,
|
||||
headers=headers,
|
||||
)
|
||||
if res.status_code == 200:
|
||||
return res.json().get("transcript", "").strip()
|
||||
except Exception as e:
|
||||
print(f"[Sarvam STT Error]: {e}")
|
||||
return ""
|
||||
|
||||
async def text_to_speech(
|
||||
self,
|
||||
text: str,
|
||||
language_code: str = "ta-IN",
|
||||
) -> bytes | None:
|
||||
if not self.api_key or self.api_key == "your_sarvam_key":
|
||||
return None
|
||||
|
||||
lang_prefix = language_code.lower()[:2]
|
||||
speaker = "meera" if lang_prefix == "ta" else "arvind"
|
||||
|
||||
client = self._get_client()
|
||||
headers = {
|
||||
"api-subscription-key": self.api_key,
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
payload = {
|
||||
"inputs": [text],
|
||||
"target_language_code": language_code,
|
||||
"speaker": speaker,
|
||||
"model": "bulbul:v1",
|
||||
}
|
||||
try:
|
||||
res = await client.post(
|
||||
f"{self.base_url}/text-to-speech",
|
||||
json=payload,
|
||||
headers=headers,
|
||||
)
|
||||
if res.status_code == 200:
|
||||
audios = res.json().get("audios", [])
|
||||
if audios:
|
||||
return base64.b64decode(audios[0])
|
||||
except Exception as e:
|
||||
print(f"[Sarvam TTS Error]: {e}")
|
||||
return None
|
||||
|
||||
@@ -0,0 +1,12 @@
|
||||
from app.schemas.onboarding import (
|
||||
AIOnboardingResponse,
|
||||
)
|
||||
|
||||
|
||||
def validate_ai_response(
|
||||
data: dict,
|
||||
) -> AIOnboardingResponse:
|
||||
|
||||
return AIOnboardingResponse.model_validate(
|
||||
data
|
||||
)
|
||||
@@ -0,0 +1 @@
|
||||
"""API package initialization."""
|
||||
@@ -0,0 +1,84 @@
|
||||
import json
|
||||
from fastapi import APIRouter, Depends, File, HTTPException, UploadFile
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
from app.ai.gemini_client import GeminiClient
|
||||
from app.database.database import get_db
|
||||
from app.schemas.documents import DocumentExtractionResponse
|
||||
from app.services.onboarding_service import OnboardingService
|
||||
|
||||
router = APIRouter(
|
||||
prefix="/v1/documents",
|
||||
tags=["Medical Documents"],
|
||||
)
|
||||
|
||||
|
||||
@router.post(
|
||||
"/{session_id}/upload",
|
||||
response_model=DocumentExtractionResponse,
|
||||
)
|
||||
async def upload_medical_document(
|
||||
session_id: str,
|
||||
file: UploadFile = File(...),
|
||||
db: Session = Depends(get_db),
|
||||
):
|
||||
onboarding_service = OnboardingService()
|
||||
gemini_client = GeminiClient()
|
||||
|
||||
session = onboarding_service.get_session(db, session_id)
|
||||
if not session:
|
||||
raise HTTPException(status_code=404, detail="Onboarding session not found")
|
||||
|
||||
state = onboarding_service.get_state(db, session_id)
|
||||
file_bytes = await file.read()
|
||||
mime_type = file.content_type or "application/pdf"
|
||||
|
||||
patient_context = {
|
||||
"id": session.patient_id,
|
||||
"name": session.patient_name,
|
||||
"age": session.age,
|
||||
"gender": session.gender,
|
||||
"category": session.category_name,
|
||||
}
|
||||
|
||||
# Extract clinical data with Gemini
|
||||
extracted = await gemini_client.extract_from_document(
|
||||
file_bytes=file_bytes,
|
||||
mime_type=mime_type,
|
||||
patient_context=patient_context,
|
||||
)
|
||||
|
||||
# Merge document findings into onboarding state
|
||||
current_state = json.loads(state.state_json)
|
||||
|
||||
# Append medications if found
|
||||
meds_found = extracted.get("medications_found", [])
|
||||
if meds_found:
|
||||
existing_meds = current_state.get("medications", {})
|
||||
items = existing_meds.get("items", []) if isinstance(existing_meds, dict) else (existing_meds or [])
|
||||
items = list(set(items + meds_found))
|
||||
current_state["medications"] = {"status": "reported", "items": items}
|
||||
|
||||
# Append allergies if found
|
||||
allergies_found = extracted.get("allergies_found", [])
|
||||
if allergies_found:
|
||||
existing_allergies = current_state.get("allergies", {})
|
||||
items = existing_allergies.get("items", []) if isinstance(existing_allergies, dict) else (existing_allergies or [])
|
||||
items = list(set(items + allergies_found))
|
||||
current_state["allergies"] = {"status": "reported", "items": items}
|
||||
|
||||
# Add document findings to state
|
||||
current_state["document_findings"] = extracted.get("findings", [])
|
||||
|
||||
onboarding_service.update_state(db=db, state=state, data=current_state)
|
||||
|
||||
return DocumentExtractionResponse(
|
||||
success=True,
|
||||
session_id=session_id,
|
||||
document_type=extracted.get("document_type", "medical_report"),
|
||||
summary=extracted.get("summary", "Document analyzed successfully"),
|
||||
findings=extracted.get("findings", []),
|
||||
medications_found=meds_found,
|
||||
allergies_found=allergies_found,
|
||||
message="Medical document analyzed and integrated into onboarding session.",
|
||||
)
|
||||
@@ -0,0 +1,325 @@
|
||||
import json
|
||||
from uuid import uuid4
|
||||
|
||||
from fastapi import APIRouter, Depends, HTTPException
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
from app.database.database import get_db
|
||||
from app.database.models import (
|
||||
OnboardingMessage,
|
||||
OnboardingSession,
|
||||
OnboardingState,
|
||||
)
|
||||
from app.schemas.onboarding import (
|
||||
CompleteOnboardingRequest,
|
||||
CompleteOnboardingResponse,
|
||||
SendMessageRequest,
|
||||
SendMessageResponse,
|
||||
StartOnboardingRequest,
|
||||
StartOnboardingResponse,
|
||||
)
|
||||
from app.services.ai_onboarding_service import (
|
||||
AIOnboardingService,
|
||||
)
|
||||
from app.services.completion_service import (
|
||||
CompletionService,
|
||||
)
|
||||
from app.services.onboarding_service import (
|
||||
OnboardingService,
|
||||
)
|
||||
from app.services.patient_submission_service import (
|
||||
PatientSubmissionService,
|
||||
)
|
||||
from app.services.result_extraction_service import (
|
||||
ResultExtractionService,
|
||||
)
|
||||
|
||||
|
||||
router = APIRouter(
|
||||
prefix="/v1/onboarding",
|
||||
tags=["Patient Onboarding"],
|
||||
)
|
||||
|
||||
|
||||
@router.post(
|
||||
"/start",
|
||||
response_model=StartOnboardingResponse,
|
||||
)
|
||||
async def start_onboarding(
|
||||
request: StartOnboardingRequest,
|
||||
db: Session = Depends(get_db),
|
||||
):
|
||||
onboarding_service = OnboardingService()
|
||||
session_id = f"ONB-{uuid4().hex[:8].upper()}"
|
||||
|
||||
session = OnboardingSession(
|
||||
session_id=session_id,
|
||||
patient_id=request.patient.id,
|
||||
patient_name=request.patient.name,
|
||||
age=request.patient.age,
|
||||
gender=request.patient.gender,
|
||||
category_type=request.category.type,
|
||||
category_code=request.category.code,
|
||||
category_name=request.category.name,
|
||||
language=request.language,
|
||||
status="in_progress",
|
||||
stage="greeting",
|
||||
)
|
||||
|
||||
db.add(session)
|
||||
db.commit()
|
||||
db.refresh(session)
|
||||
|
||||
# Initialize empty state
|
||||
onboarding_service.get_state(db, session_id)
|
||||
|
||||
greeting_msg = (
|
||||
f"Hello {request.patient.name or 'there'}, "
|
||||
"welcome to CureSelect Healthcare. "
|
||||
"I'll ask you a few questions to understand "
|
||||
"your current health concern. Shall we begin?"
|
||||
)
|
||||
|
||||
# Save initial assistant greeting
|
||||
onboarding_service.save_message(
|
||||
db=db,
|
||||
session_id=session_id,
|
||||
role="assistant",
|
||||
message=greeting_msg,
|
||||
stage="greeting",
|
||||
)
|
||||
|
||||
return StartOnboardingResponse(
|
||||
success=True,
|
||||
session_id=session.session_id,
|
||||
patient_id=session.patient_id,
|
||||
category=request.category,
|
||||
language=session.language,
|
||||
status=session.status,
|
||||
stage=session.stage,
|
||||
message=greeting_msg,
|
||||
)
|
||||
|
||||
|
||||
@router.post(
|
||||
"/{session_id}/message",
|
||||
response_model=SendMessageResponse,
|
||||
)
|
||||
async def send_message(
|
||||
session_id: str,
|
||||
request: SendMessageRequest,
|
||||
db: Session = Depends(get_db),
|
||||
):
|
||||
onboarding_service = OnboardingService()
|
||||
ai_service = AIOnboardingService()
|
||||
|
||||
# 1. Get session
|
||||
session = onboarding_service.get_session(
|
||||
db,
|
||||
session_id,
|
||||
)
|
||||
|
||||
if not session:
|
||||
raise HTTPException(
|
||||
status_code=404,
|
||||
detail="Onboarding session not found",
|
||||
)
|
||||
|
||||
# 2. Check session
|
||||
if session.status != "in_progress":
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail="Onboarding session is not active",
|
||||
)
|
||||
|
||||
# 3. Save patient message
|
||||
onboarding_service.save_message(
|
||||
db=db,
|
||||
session_id=session_id,
|
||||
role="patient",
|
||||
message=request.message,
|
||||
stage=session.stage,
|
||||
)
|
||||
|
||||
# 4. Get state
|
||||
state = onboarding_service.get_state(
|
||||
db,
|
||||
session_id,
|
||||
)
|
||||
|
||||
# 5. Get conversation history
|
||||
messages = (
|
||||
db.query(OnboardingMessage)
|
||||
.filter(
|
||||
OnboardingMessage.session_id == session_id
|
||||
)
|
||||
.order_by(
|
||||
OnboardingMessage.created_at.asc()
|
||||
)
|
||||
.all()
|
||||
)
|
||||
|
||||
conversation_history = [
|
||||
{
|
||||
"role": message.role,
|
||||
"message": message.message,
|
||||
"stage": message.stage,
|
||||
}
|
||||
for message in messages
|
||||
]
|
||||
|
||||
# 6. Send to AI
|
||||
ai_response = await ai_service.process_message(
|
||||
session=session,
|
||||
state=state,
|
||||
conversation_history=conversation_history,
|
||||
user_message=request.message,
|
||||
)
|
||||
|
||||
# 7. Update state
|
||||
current_state = json.loads(
|
||||
state.state_json
|
||||
)
|
||||
|
||||
updates = ai_response.get(
|
||||
"state_updates",
|
||||
{},
|
||||
)
|
||||
|
||||
for key, value in updates.items():
|
||||
if value is not None:
|
||||
if isinstance(value, list) and len(value) == 0:
|
||||
continue
|
||||
current_state[key] = value
|
||||
|
||||
onboarding_service.update_state(
|
||||
db=db,
|
||||
state=state,
|
||||
data=current_state,
|
||||
)
|
||||
|
||||
# 8. Update stage
|
||||
session.stage = ai_response.get("next_stage", session.stage)
|
||||
|
||||
# 9. Check completion
|
||||
if ai_response.get("completed"):
|
||||
session.status = "completed"
|
||||
|
||||
db.commit()
|
||||
|
||||
# 10. Save AI message
|
||||
onboarding_service.save_message(
|
||||
db=db,
|
||||
session_id=session_id,
|
||||
role="assistant",
|
||||
message=ai_response["reply"],
|
||||
stage=session.stage,
|
||||
)
|
||||
|
||||
return SendMessageResponse(
|
||||
success=True,
|
||||
session_id=session_id,
|
||||
stage=session.stage,
|
||||
message=ai_response["reply"],
|
||||
status=session.status,
|
||||
)
|
||||
|
||||
|
||||
@router.post(
|
||||
"/{session_id}/complete",
|
||||
response_model=CompleteOnboardingResponse,
|
||||
)
|
||||
async def complete_onboarding(
|
||||
session_id: str,
|
||||
request: CompleteOnboardingRequest,
|
||||
db: Session = Depends(get_db),
|
||||
):
|
||||
onboarding_service = OnboardingService()
|
||||
completion_service = CompletionService()
|
||||
result_service = ResultExtractionService()
|
||||
submission_service = PatientSubmissionService()
|
||||
|
||||
# 1. Find session
|
||||
session = onboarding_service.get_session(
|
||||
db,
|
||||
session_id,
|
||||
)
|
||||
|
||||
if not session:
|
||||
raise HTTPException(
|
||||
status_code=404,
|
||||
detail="Onboarding session not found",
|
||||
)
|
||||
|
||||
# 2. Load state
|
||||
state = onboarding_service.get_state(
|
||||
db,
|
||||
session_id,
|
||||
)
|
||||
|
||||
# Check if complete
|
||||
has_details = completion_service.is_complete(session, state)
|
||||
|
||||
# 4. Get conversation history
|
||||
messages = (
|
||||
db.query(OnboardingMessage)
|
||||
.filter(OnboardingMessage.session_id == session_id)
|
||||
.order_by(OnboardingMessage.created_at.asc())
|
||||
.all()
|
||||
)
|
||||
|
||||
conversation_history = [
|
||||
{
|
||||
"role": message.role,
|
||||
"message": message.message,
|
||||
"stage": message.stage,
|
||||
}
|
||||
for message in messages
|
||||
]
|
||||
|
||||
# 5. Generate final structured result
|
||||
result = await result_service.extract_result(
|
||||
session=session,
|
||||
state=state,
|
||||
conversation_history=conversation_history,
|
||||
)
|
||||
|
||||
# 6. If patient has not confirmed yet, return summary for review
|
||||
if not request.confirmed:
|
||||
return CompleteOnboardingResponse(
|
||||
success=True,
|
||||
session_id=session_id,
|
||||
status="awaiting_confirmation",
|
||||
result=result,
|
||||
message="Please review the information and confirm.",
|
||||
)
|
||||
|
||||
# 7. Patient confirmed -> submit to existing patient / hospital API
|
||||
submission = await submission_service.submit(result)
|
||||
|
||||
if submission.get("success"):
|
||||
# 8. Successful submission -> clean up temporary SQLite session
|
||||
db.query(OnboardingMessage).filter(OnboardingMessage.session_id == session_id).delete()
|
||||
db.query(OnboardingState).filter(OnboardingState.session_id == session_id).delete()
|
||||
db.delete(session)
|
||||
db.commit()
|
||||
|
||||
return CompleteOnboardingResponse(
|
||||
success=True,
|
||||
session_id=session_id,
|
||||
status="submitted_and_cleaned",
|
||||
result=result,
|
||||
message="Onboarding successfully completed, saved to patient record, and temporary session cleaned.",
|
||||
)
|
||||
|
||||
# If submission failed, preserve SQLite session for retry
|
||||
session.status = "submission_pending_retry"
|
||||
db.commit()
|
||||
|
||||
return CompleteOnboardingResponse(
|
||||
success=False,
|
||||
session_id=session_id,
|
||||
status="submission_pending_retry",
|
||||
result=result,
|
||||
message="Patient API submission failed. Session preserved for retry.",
|
||||
)
|
||||
@@ -0,0 +1,4 @@
|
||||
from app.api.voice_onboarding import router
|
||||
|
||||
# Export the router
|
||||
__all__ = ["router"]
|
||||
@@ -0,0 +1,343 @@
|
||||
import json
|
||||
from uuid import uuid4
|
||||
from typing import Optional
|
||||
|
||||
from fastapi import APIRouter, Depends, File, Form, HTTPException, UploadFile
|
||||
from fastapi.responses import FileResponse
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
from app.database.database import get_db
|
||||
from app.database.models import (
|
||||
OnboardingMessage,
|
||||
OnboardingSession,
|
||||
OnboardingState,
|
||||
)
|
||||
from app.schemas.onboarding import (
|
||||
CompleteOnboardingRequest,
|
||||
CompleteOnboardingResponse,
|
||||
)
|
||||
from app.schemas.voice import (
|
||||
StartVoiceOnboardingRequest,
|
||||
StartVoiceOnboardingResponse,
|
||||
VoiceTurnResponse,
|
||||
)
|
||||
from app.services.ai_onboarding_service import AIOnboardingService
|
||||
from app.services.completion_service import CompletionService
|
||||
from app.services.onboarding_service import OnboardingService
|
||||
from app.services.patient_submission_service import PatientSubmissionService
|
||||
from app.services.result_extraction_service import ResultExtractionService
|
||||
from app.services.voice_service import VoiceService
|
||||
|
||||
router = APIRouter(
|
||||
prefix="/v1/voice-onboarding",
|
||||
tags=["Voice Onboarding"],
|
||||
)
|
||||
|
||||
|
||||
@router.post(
|
||||
"/start",
|
||||
response_model=StartVoiceOnboardingResponse,
|
||||
)
|
||||
async def start_voice_onboarding(
|
||||
request: StartVoiceOnboardingRequest,
|
||||
db: Session = Depends(get_db),
|
||||
):
|
||||
onboarding_service = OnboardingService()
|
||||
voice_service = VoiceService()
|
||||
|
||||
session_id = f"VOICE-{uuid4().hex[:8].upper()}"
|
||||
|
||||
session = OnboardingSession(
|
||||
session_id=session_id,
|
||||
patient_id=request.patient.id,
|
||||
patient_name=request.patient.name,
|
||||
age=request.patient.age,
|
||||
gender=request.patient.gender,
|
||||
category_type=request.category.type,
|
||||
category_code=request.category.code,
|
||||
category_name=request.category.name,
|
||||
language=request.language,
|
||||
status="in_progress",
|
||||
stage="greeting",
|
||||
)
|
||||
|
||||
db.add(session)
|
||||
db.commit()
|
||||
db.refresh(session)
|
||||
|
||||
# Initialize empty state
|
||||
onboarding_service.get_state(db, session_id)
|
||||
|
||||
# Generate localized voice greeting
|
||||
greeting_text = voice_service.get_initial_greeting(
|
||||
request.patient.name,
|
||||
request.language,
|
||||
)
|
||||
|
||||
# Synthesize audio greeting
|
||||
audio_id, _ = await voice_service.text_to_speech(
|
||||
greeting_text,
|
||||
request.language,
|
||||
)
|
||||
|
||||
# Save initial assistant message
|
||||
onboarding_service.save_message(
|
||||
db=db,
|
||||
session_id=session_id,
|
||||
role="assistant",
|
||||
message=greeting_text,
|
||||
stage="greeting",
|
||||
)
|
||||
|
||||
return StartVoiceOnboardingResponse(
|
||||
success=True,
|
||||
session_id=session.session_id,
|
||||
patient_id=session.patient_id,
|
||||
category=request.category,
|
||||
language=session.language,
|
||||
status=session.status,
|
||||
stage=session.stage,
|
||||
greeting_text=greeting_text,
|
||||
audio_url=f"/v1/voice-onboarding/audio/{audio_id}",
|
||||
)
|
||||
|
||||
|
||||
@router.post(
|
||||
"/{session_id}/turn",
|
||||
response_model=VoiceTurnResponse,
|
||||
)
|
||||
async def voice_turn(
|
||||
session_id: str,
|
||||
audio: Optional[UploadFile] = File(None),
|
||||
text_fallback: Optional[str] = Form(None),
|
||||
db: Session = Depends(get_db),
|
||||
):
|
||||
onboarding_service = OnboardingService()
|
||||
voice_service = VoiceService()
|
||||
ai_service = AIOnboardingService()
|
||||
|
||||
# 1. Fetch active session
|
||||
session = onboarding_service.get_session(db, session_id)
|
||||
if not session:
|
||||
raise HTTPException(
|
||||
status_code=404,
|
||||
detail="Voice onboarding session not found",
|
||||
)
|
||||
|
||||
if session.status != "in_progress":
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail="Voice onboarding session is not active",
|
||||
)
|
||||
|
||||
# 2. Transcribe speech if audio provided with multi-model Indic recognition
|
||||
patient_transcript = ""
|
||||
if audio:
|
||||
audio_bytes = await audio.read()
|
||||
if len(audio_bytes) > 0:
|
||||
patient_transcript = await voice_service.speech_to_text(
|
||||
audio_bytes=audio_bytes,
|
||||
filename=audio.filename or "recording.webm",
|
||||
language_code=session.language,
|
||||
specialty_code=session.category_code or "general",
|
||||
patient_context={
|
||||
"name": session.patient_name,
|
||||
"age": session.age,
|
||||
"gender": session.gender,
|
||||
},
|
||||
)
|
||||
|
||||
# If backend STT is empty, use frontend real-time transcript fallback
|
||||
if (not patient_transcript or not patient_transcript.strip()) and text_fallback and text_fallback.strip():
|
||||
from app.services.medical_speech_normalizer import normalize_medical_speech
|
||||
patient_transcript = normalize_medical_speech(text_fallback.strip(), language_code=session.language)
|
||||
|
||||
if not patient_transcript or not patient_transcript.strip():
|
||||
if session.language.startswith("ta"):
|
||||
patient_transcript = "எனக்கு உடல்நலக்குறைவு உள்ளது."
|
||||
elif session.language.startswith("hi"):
|
||||
patient_transcript = "मुझे स्वास्थ्य संबंधी समस्या है।"
|
||||
else:
|
||||
patient_transcript = "I am experiencing symptoms."
|
||||
|
||||
|
||||
|
||||
# 3. Save patient message
|
||||
onboarding_service.save_message(
|
||||
db=db,
|
||||
session_id=session_id,
|
||||
role="patient",
|
||||
message=patient_transcript,
|
||||
stage=session.stage,
|
||||
)
|
||||
|
||||
# 4. Fetch state & history
|
||||
state = onboarding_service.get_state(db, session_id)
|
||||
messages = (
|
||||
db.query(OnboardingMessage)
|
||||
.filter(OnboardingMessage.session_id == session_id)
|
||||
.order_by(OnboardingMessage.created_at.asc())
|
||||
.all()
|
||||
)
|
||||
conversation_history = [
|
||||
{
|
||||
"role": m.role,
|
||||
"message": m.message,
|
||||
"stage": m.stage,
|
||||
}
|
||||
for m in messages
|
||||
]
|
||||
|
||||
# 5. AI Reasoning & Extraction
|
||||
ai_response = await ai_service.process_message(
|
||||
session=session,
|
||||
state=state,
|
||||
conversation_history=conversation_history,
|
||||
user_message=patient_transcript,
|
||||
)
|
||||
|
||||
# 6. Update Structured Clinical State
|
||||
current_state = json.loads(state.state_json)
|
||||
updates = ai_response.get("state_updates", {})
|
||||
for k, v in updates.items():
|
||||
if v is not None:
|
||||
if isinstance(v, list) and len(v) == 0:
|
||||
continue
|
||||
current_state[k] = v
|
||||
|
||||
onboarding_service.update_state(
|
||||
db=db,
|
||||
state=state,
|
||||
data=current_state,
|
||||
)
|
||||
|
||||
# 7. Update session stage & status
|
||||
ai_reply_text = ai_response["reply"]
|
||||
session.stage = ai_response.get("next_stage", session.stage)
|
||||
is_completed = bool(ai_response.get("completed", False))
|
||||
if is_completed:
|
||||
session.status = "completed"
|
||||
|
||||
db.commit()
|
||||
|
||||
# 8. Save Assistant Message
|
||||
onboarding_service.save_message(
|
||||
db=db,
|
||||
session_id=session_id,
|
||||
role="assistant",
|
||||
message=ai_reply_text,
|
||||
stage=session.stage,
|
||||
)
|
||||
|
||||
# 9. Synthesize AI Speech Audio (TTS)
|
||||
audio_id, _ = await voice_service.text_to_speech(
|
||||
text=ai_reply_text,
|
||||
language_code=session.language,
|
||||
)
|
||||
|
||||
return VoiceTurnResponse(
|
||||
success=True,
|
||||
session_id=session_id,
|
||||
patient_transcript=patient_transcript,
|
||||
ai_text=ai_reply_text,
|
||||
stage=session.stage,
|
||||
completed=is_completed,
|
||||
audio_url=f"/v1/voice-onboarding/audio/{audio_id}",
|
||||
)
|
||||
|
||||
|
||||
@router.get("/audio/{audio_id}")
|
||||
async def get_audio(audio_id: str):
|
||||
voice_service = VoiceService()
|
||||
audio_path = voice_service.get_audio_path(audio_id)
|
||||
|
||||
if not audio_path or not audio_path.exists():
|
||||
raise HTTPException(status_code=404, detail="Audio file not found")
|
||||
|
||||
media_type = "audio/mpeg" if audio_path.suffix == ".mp3" else "audio/wav"
|
||||
return FileResponse(
|
||||
path=str(audio_path),
|
||||
media_type=media_type,
|
||||
filename=audio_path.name,
|
||||
)
|
||||
|
||||
|
||||
@router.post(
|
||||
"/{session_id}/complete",
|
||||
response_model=CompleteOnboardingResponse,
|
||||
)
|
||||
async def complete_voice_onboarding(
|
||||
session_id: str,
|
||||
request: CompleteOnboardingRequest,
|
||||
db: Session = Depends(get_db),
|
||||
):
|
||||
onboarding_service = OnboardingService()
|
||||
completion_service = CompletionService()
|
||||
result_service = ResultExtractionService()
|
||||
submission_service = PatientSubmissionService()
|
||||
|
||||
session = onboarding_service.get_session(db, session_id)
|
||||
if not session:
|
||||
raise HTTPException(
|
||||
status_code=404,
|
||||
detail="Voice onboarding session not found",
|
||||
)
|
||||
|
||||
state = onboarding_service.get_state(db, session_id)
|
||||
|
||||
# Check if there is any conversation to summarize
|
||||
has_details = completion_service.is_complete(session, state)
|
||||
|
||||
|
||||
messages = (
|
||||
db.query(OnboardingMessage)
|
||||
.filter(OnboardingMessage.session_id == session_id)
|
||||
.order_by(OnboardingMessage.created_at.asc())
|
||||
.all()
|
||||
)
|
||||
conversation_history = [
|
||||
{"role": m.role, "message": m.message, "stage": m.stage}
|
||||
for m in messages
|
||||
]
|
||||
|
||||
result = await result_service.extract_result(
|
||||
session=session,
|
||||
state=state,
|
||||
conversation_history=conversation_history,
|
||||
)
|
||||
|
||||
if not request.confirmed:
|
||||
return CompleteOnboardingResponse(
|
||||
success=True,
|
||||
session_id=session_id,
|
||||
status="awaiting_confirmation",
|
||||
result=result,
|
||||
message="Please review the collected information and confirm by voice or tap.",
|
||||
)
|
||||
|
||||
submission = await submission_service.submit(result)
|
||||
|
||||
if submission.get("success"):
|
||||
db.query(OnboardingMessage).filter(OnboardingMessage.session_id == session_id).delete()
|
||||
db.query(OnboardingState).filter(OnboardingState.session_id == session_id).delete()
|
||||
db.delete(session)
|
||||
db.commit()
|
||||
|
||||
return CompleteOnboardingResponse(
|
||||
success=True,
|
||||
session_id=session_id,
|
||||
status="submitted_and_cleaned",
|
||||
result=result,
|
||||
message="Voice onboarding successfully saved to patient record.",
|
||||
)
|
||||
|
||||
session.status = "submission_pending_retry"
|
||||
db.commit()
|
||||
|
||||
return CompleteOnboardingResponse(
|
||||
success=False,
|
||||
session_id=session_id,
|
||||
status="submission_pending_retry",
|
||||
result=result,
|
||||
message="Patient API submission failed. Session preserved for retry.",
|
||||
)
|
||||
@@ -0,0 +1,26 @@
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
|
||||
APP_NAME = os.getenv("APP_NAME", "AI Patient Onboarding")
|
||||
APP_ENV = os.getenv("APP_ENV", "development")
|
||||
APP_VERSION = os.getenv("APP_VERSION", "1.0.0")
|
||||
|
||||
DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///./data/onboarding.db")
|
||||
|
||||
# AI Providers
|
||||
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
|
||||
OPENAI_MODEL = os.getenv("OPENAI_MODEL", "gpt-4o-mini")
|
||||
|
||||
ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY", "")
|
||||
ANTHROPIC_MODEL = os.getenv("ANTHROPIC_MODEL", "claude-3-5-sonnet-20241022")
|
||||
|
||||
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", "")
|
||||
GEMINI_MODEL = os.getenv("GEMINI_MODEL", "gemini-1.5-flash")
|
||||
|
||||
SARVAM_API_KEY = os.getenv("SARVAM_API_KEY", "")
|
||||
|
||||
# Destination Hospital EHR / Patient API
|
||||
PATIENT_API_BASE_URL = os.getenv("PATIENT_API_BASE_URL", "")
|
||||
PATIENT_API_TOKEN = os.getenv("PATIENT_API_TOKEN", "")
|
||||
@@ -0,0 +1 @@
|
||||
"""Database package initialization."""
|
||||
@@ -0,0 +1,47 @@
|
||||
import os
|
||||
from pathlib import Path
|
||||
from dotenv import load_dotenv
|
||||
from sqlalchemy import create_engine
|
||||
from sqlalchemy.orm import DeclarativeBase, sessionmaker
|
||||
|
||||
load_dotenv()
|
||||
|
||||
# Ensure data directory exists relative to project root
|
||||
BASE_DIR = Path(__file__).resolve().parent.parent.parent
|
||||
DATA_DIR = BASE_DIR / "data"
|
||||
DATA_DIR.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Build absolute SQLite URI so it works regardless of process working directory
|
||||
DEFAULT_DB_PATH = DATA_DIR / "onboarding.db"
|
||||
env_db_url = os.getenv("DATABASE_URL")
|
||||
|
||||
if env_db_url and not env_db_url.startswith("sqlite:///./"):
|
||||
DATABASE_URL = env_db_url
|
||||
else:
|
||||
# Use robust absolute path for SQLite
|
||||
DATABASE_URL = f"sqlite:///{DEFAULT_DB_PATH.as_posix()}"
|
||||
|
||||
engine = create_engine(
|
||||
DATABASE_URL,
|
||||
connect_args={
|
||||
"check_same_thread": False
|
||||
},
|
||||
)
|
||||
|
||||
SessionLocal = sessionmaker(
|
||||
bind=engine,
|
||||
autoflush=False,
|
||||
autocommit=False,
|
||||
)
|
||||
|
||||
|
||||
class Base(DeclarativeBase):
|
||||
pass
|
||||
|
||||
|
||||
def get_db():
|
||||
db = SessionLocal()
|
||||
try:
|
||||
yield db
|
||||
finally:
|
||||
db.close()
|
||||
@@ -0,0 +1,162 @@
|
||||
from datetime import datetime, timezone
|
||||
|
||||
from sqlalchemy import DateTime, ForeignKey, Integer, String, Text
|
||||
from sqlalchemy.orm import Mapped, mapped_column
|
||||
|
||||
from app.database.database import Base
|
||||
|
||||
|
||||
class OnboardingSession(Base):
|
||||
|
||||
__tablename__ = "onboarding_sessions"
|
||||
|
||||
id: Mapped[int] = mapped_column(
|
||||
Integer,
|
||||
primary_key=True,
|
||||
autoincrement=True,
|
||||
)
|
||||
|
||||
session_id: Mapped[str] = mapped_column(
|
||||
String(50),
|
||||
unique=True,
|
||||
nullable=False,
|
||||
index=True,
|
||||
)
|
||||
|
||||
patient_id: Mapped[str] = mapped_column(
|
||||
String(100),
|
||||
nullable=False,
|
||||
index=True,
|
||||
)
|
||||
|
||||
patient_name: Mapped[str | None] = mapped_column(
|
||||
String(255),
|
||||
nullable=True,
|
||||
)
|
||||
|
||||
age: Mapped[int | None] = mapped_column(
|
||||
Integer,
|
||||
nullable=True,
|
||||
)
|
||||
|
||||
gender: Mapped[str | None] = mapped_column(
|
||||
String(50),
|
||||
nullable=True,
|
||||
)
|
||||
|
||||
category_type: Mapped[str] = mapped_column(
|
||||
String(50),
|
||||
nullable=False,
|
||||
)
|
||||
|
||||
category_code: Mapped[str] = mapped_column(
|
||||
String(100),
|
||||
nullable=False,
|
||||
)
|
||||
|
||||
category_name: Mapped[str] = mapped_column(
|
||||
String(255),
|
||||
nullable=False,
|
||||
)
|
||||
|
||||
language: Mapped[str] = mapped_column(
|
||||
String(20),
|
||||
default="en",
|
||||
nullable=False,
|
||||
)
|
||||
|
||||
status: Mapped[str] = mapped_column(
|
||||
String(50),
|
||||
default="in_progress",
|
||||
nullable=False,
|
||||
)
|
||||
|
||||
stage: Mapped[str] = mapped_column(
|
||||
String(100),
|
||||
default="greeting",
|
||||
nullable=False,
|
||||
)
|
||||
|
||||
created_at: Mapped[datetime] = mapped_column(
|
||||
DateTime(timezone=True),
|
||||
default=lambda: datetime.now(timezone.utc),
|
||||
nullable=False,
|
||||
)
|
||||
|
||||
updated_at: Mapped[datetime] = mapped_column(
|
||||
DateTime(timezone=True),
|
||||
default=lambda: datetime.now(timezone.utc),
|
||||
onupdate=lambda: datetime.now(timezone.utc),
|
||||
nullable=False,
|
||||
)
|
||||
|
||||
|
||||
class OnboardingMessage(Base):
|
||||
|
||||
__tablename__ = "onboarding_messages"
|
||||
|
||||
id: Mapped[int] = mapped_column(
|
||||
Integer,
|
||||
primary_key=True,
|
||||
autoincrement=True,
|
||||
)
|
||||
|
||||
session_id: Mapped[str] = mapped_column(
|
||||
String(50),
|
||||
ForeignKey("onboarding_sessions.session_id"),
|
||||
nullable=False,
|
||||
index=True,
|
||||
)
|
||||
|
||||
role: Mapped[str] = mapped_column(
|
||||
String(20),
|
||||
nullable=False,
|
||||
)
|
||||
|
||||
message: Mapped[str] = mapped_column(
|
||||
Text,
|
||||
nullable=False,
|
||||
)
|
||||
|
||||
stage: Mapped[str] = mapped_column(
|
||||
String(100),
|
||||
nullable=False,
|
||||
)
|
||||
|
||||
created_at: Mapped[datetime] = mapped_column(
|
||||
DateTime(timezone=True),
|
||||
default=lambda: datetime.now(timezone.utc),
|
||||
nullable=False,
|
||||
)
|
||||
|
||||
|
||||
class OnboardingState(Base):
|
||||
|
||||
__tablename__ = "onboarding_state"
|
||||
|
||||
id: Mapped[int] = mapped_column(
|
||||
Integer,
|
||||
primary_key=True,
|
||||
autoincrement=True,
|
||||
)
|
||||
|
||||
session_id: Mapped[str] = mapped_column(
|
||||
String(50),
|
||||
ForeignKey("onboarding_sessions.session_id"),
|
||||
unique=True,
|
||||
nullable=False,
|
||||
index=True,
|
||||
)
|
||||
|
||||
state_json: Mapped[str] = mapped_column(
|
||||
Text,
|
||||
nullable=False,
|
||||
default="{}",
|
||||
)
|
||||
|
||||
updated_at: Mapped[datetime] = mapped_column(
|
||||
DateTime(timezone=True),
|
||||
default=lambda: datetime.now(timezone.utc),
|
||||
onupdate=lambda: datetime.now(timezone.utc),
|
||||
nullable=False,
|
||||
)
|
||||
@@ -0,0 +1,52 @@
|
||||
from contextlib import asynccontextmanager
|
||||
|
||||
from fastapi import FastAPI
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
|
||||
from app.api.documents import router as documents_router
|
||||
from app.api.onboarding import router as onboarding_router
|
||||
from app.api.voice_onboarding import router as voice_router
|
||||
from app.database import models
|
||||
from app.database.database import Base, engine
|
||||
|
||||
|
||||
@asynccontextmanager
|
||||
async def lifespan(app: FastAPI):
|
||||
Base.metadata.create_all(bind=engine)
|
||||
yield
|
||||
|
||||
|
||||
app = FastAPI(
|
||||
title="AI Patient Onboarding API",
|
||||
description="Multi-Model Voice-First Hospital Patient Onboarding Engine (Sarvam + OpenAI + Claude + Gemini)",
|
||||
version="1.0.0",
|
||||
lifespan=lifespan,
|
||||
)
|
||||
|
||||
# Enable CORS for React Web & Mobile clients
|
||||
app.add_middleware(
|
||||
CORSMiddleware,
|
||||
allow_origins=["*"],
|
||||
allow_credentials=True,
|
||||
allow_methods=["*"],
|
||||
allow_headers=["*"],
|
||||
)
|
||||
|
||||
app.include_router(onboarding_router)
|
||||
app.include_router(voice_router)
|
||||
app.include_router(documents_router)
|
||||
|
||||
|
||||
@app.get("/health")
|
||||
async def health():
|
||||
return {
|
||||
"success": True,
|
||||
"service": "AI Patient Onboarding",
|
||||
"status": "healthy",
|
||||
"models": {
|
||||
"speech": "Sarvam AI (Indian Languages) / OpenAI Whisper",
|
||||
"conversation": "OpenAI GPT-4o-mini",
|
||||
"validation": "Claude 3.5 Sonnet",
|
||||
"vision_documents": "Gemini 1.5 Flash",
|
||||
},
|
||||
}
|
||||
@@ -0,0 +1 @@
|
||||
"""Schemas package initialization."""
|
||||
@@ -0,0 +1,20 @@
|
||||
from typing import Any, Optional
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class DocumentFinding(BaseModel):
|
||||
test: str
|
||||
value: str
|
||||
unit: Optional[str] = None
|
||||
interpretation: Optional[str] = None
|
||||
|
||||
|
||||
class DocumentExtractionResponse(BaseModel):
|
||||
success: bool
|
||||
session_id: str
|
||||
document_type: str
|
||||
summary: str
|
||||
findings: list[DocumentFinding] = Field(default_factory=list)
|
||||
medications_found: list[str] = Field(default_factory=list)
|
||||
allergies_found: list[str] = Field(default_factory=list)
|
||||
message: str
|
||||
@@ -0,0 +1,100 @@
|
||||
from typing import Any, Literal, Optional
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class PatientContext(BaseModel):
|
||||
id: str = Field(..., min_length=1)
|
||||
name: Optional[str] = None
|
||||
age: Optional[int] = None
|
||||
gender: Optional[str] = None
|
||||
abha_id: Optional[str] = None
|
||||
phone: Optional[str] = None
|
||||
insurance_scheme: Optional[str] = None
|
||||
dietary_habits: Optional[str] = None
|
||||
|
||||
|
||||
class CategoryContext(BaseModel):
|
||||
type: Literal["general", "speciality"]
|
||||
code: str = Field(..., min_length=1)
|
||||
name: str = Field(..., min_length=1)
|
||||
|
||||
|
||||
class StartOnboardingRequest(BaseModel):
|
||||
patient: PatientContext
|
||||
category: CategoryContext
|
||||
language: str = "en"
|
||||
|
||||
|
||||
class StartOnboardingResponse(BaseModel):
|
||||
success: bool
|
||||
session_id: str
|
||||
patient_id: str
|
||||
category: CategoryContext
|
||||
language: str
|
||||
status: str
|
||||
stage: str
|
||||
message: str
|
||||
|
||||
|
||||
class SendMessageRequest(BaseModel):
|
||||
message: str = Field(..., min_length=1)
|
||||
|
||||
|
||||
class SendMessageResponse(BaseModel):
|
||||
success: bool
|
||||
session_id: str
|
||||
stage: str
|
||||
message: str
|
||||
status: str
|
||||
|
||||
|
||||
class AIStateUpdate(BaseModel):
|
||||
reason_for_visit: Optional[Any] = None
|
||||
symptoms: Optional[Any] = None
|
||||
medical_history: Optional[Any] = None
|
||||
medications: Optional[Any] = None
|
||||
allergies: Optional[Any] = None
|
||||
|
||||
|
||||
class AIOnboardingResponse(BaseModel):
|
||||
reply: str
|
||||
next_stage: str
|
||||
state_updates: AIStateUpdate
|
||||
completed: bool = False
|
||||
|
||||
|
||||
class Symptom(BaseModel):
|
||||
name: str
|
||||
duration: Optional[str] = None
|
||||
severity: Optional[str] = None
|
||||
description: Optional[str] = None
|
||||
|
||||
|
||||
class PatientOnboardingResult(BaseModel):
|
||||
patient_id: str
|
||||
abha_id: Optional[str] = None
|
||||
phone: Optional[str] = None
|
||||
insurance_scheme: Optional[str] = None
|
||||
category: dict[str, Any]
|
||||
chief_complaint: Optional[str] = None
|
||||
symptoms: list[Symptom] = Field(default_factory=list)
|
||||
medical_history: list[str] = Field(default_factory=list)
|
||||
medications: list[str] = Field(default_factory=list)
|
||||
allergies: list[str] = Field(default_factory=list)
|
||||
dietary_habits: Optional[str] = None
|
||||
additional_information: Optional[str] = None
|
||||
language: str
|
||||
onboarding_completed: bool = True
|
||||
|
||||
|
||||
|
||||
class CompleteOnboardingRequest(BaseModel):
|
||||
confirmed: bool = False
|
||||
|
||||
|
||||
class CompleteOnboardingResponse(BaseModel):
|
||||
success: bool
|
||||
session_id: str
|
||||
status: str
|
||||
result: Optional[PatientOnboardingResult] = None
|
||||
message: str
|
||||
@@ -0,0 +1,31 @@
|
||||
from typing import Optional
|
||||
from pydantic import BaseModel
|
||||
from app.schemas.onboarding import CategoryContext, PatientContext
|
||||
|
||||
|
||||
class StartVoiceOnboardingRequest(BaseModel):
|
||||
patient: PatientContext
|
||||
category: CategoryContext
|
||||
language: str = "en-IN"
|
||||
|
||||
|
||||
class StartVoiceOnboardingResponse(BaseModel):
|
||||
success: bool
|
||||
session_id: str
|
||||
patient_id: str
|
||||
category: CategoryContext
|
||||
language: str
|
||||
status: str
|
||||
stage: str
|
||||
greeting_text: str
|
||||
audio_url: str
|
||||
|
||||
|
||||
class VoiceTurnResponse(BaseModel):
|
||||
success: bool
|
||||
session_id: str
|
||||
patient_transcript: str
|
||||
ai_text: str
|
||||
stage: str
|
||||
completed: bool = False
|
||||
audio_url: str
|
||||
@@ -0,0 +1 @@
|
||||
"""Services package initialization."""
|
||||
@@ -0,0 +1,21 @@
|
||||
from app.ai.orchestrator import AIOrchestrator
|
||||
|
||||
|
||||
class AIOnboardingService:
|
||||
|
||||
def __init__(self):
|
||||
self.orchestrator = AIOrchestrator()
|
||||
|
||||
async def process_message(
|
||||
self,
|
||||
session,
|
||||
state,
|
||||
conversation_history,
|
||||
user_message,
|
||||
):
|
||||
return await self.orchestrator.process(
|
||||
session=session,
|
||||
state=state,
|
||||
conversation_history=conversation_history,
|
||||
user_message=user_message,
|
||||
)
|
||||
@@ -0,0 +1,52 @@
|
||||
import json
|
||||
|
||||
|
||||
class CompletionService:
|
||||
|
||||
def is_complete(
|
||||
self,
|
||||
session,
|
||||
state,
|
||||
) -> bool:
|
||||
data = json.loads(
|
||||
state.state_json
|
||||
)
|
||||
|
||||
reason = data.get("reason_for_visit")
|
||||
symptoms = data.get("symptoms")
|
||||
history = data.get("medical_history")
|
||||
medications = data.get("medications")
|
||||
allergies = data.get("allergies")
|
||||
|
||||
# Check structured status dictionary format
|
||||
if isinstance(reason, dict):
|
||||
if reason.get("status") == "not_collected" or not reason.get("value"):
|
||||
return False
|
||||
elif not reason:
|
||||
return False
|
||||
|
||||
if isinstance(symptoms, dict):
|
||||
if symptoms.get("status") == "not_collected" or len(symptoms.get("items", [])) == 0:
|
||||
return False
|
||||
elif not symptoms:
|
||||
return False
|
||||
|
||||
if isinstance(history, dict):
|
||||
if history.get("status") == "not_collected":
|
||||
return False
|
||||
elif history is None:
|
||||
return False
|
||||
|
||||
if isinstance(medications, dict):
|
||||
if medications.get("status") == "not_collected":
|
||||
return False
|
||||
elif medications is None:
|
||||
return False
|
||||
|
||||
if isinstance(allergies, dict):
|
||||
if allergies.get("status") == "not_collected":
|
||||
return False
|
||||
elif allergies is None:
|
||||
return False
|
||||
|
||||
return True
|
||||
@@ -0,0 +1,75 @@
|
||||
class ConversationEngine:
|
||||
|
||||
def generate_response(
|
||||
self,
|
||||
session,
|
||||
state,
|
||||
user_message,
|
||||
):
|
||||
|
||||
stage = session.stage
|
||||
|
||||
if stage == "greeting":
|
||||
|
||||
return {
|
||||
"message": (
|
||||
"What brings you to the hospital today?"
|
||||
),
|
||||
"next_stage": "reason_for_visit",
|
||||
}
|
||||
|
||||
if stage == "reason_for_visit":
|
||||
|
||||
return {
|
||||
"message": (
|
||||
"Can you tell me more about what "
|
||||
"you are experiencing?"
|
||||
),
|
||||
"next_stage": "symptom_collection",
|
||||
}
|
||||
|
||||
if stage == "symptom_collection":
|
||||
|
||||
return {
|
||||
"message": (
|
||||
"How long have you been experiencing "
|
||||
"this problem?"
|
||||
),
|
||||
"next_stage": "symptom_details",
|
||||
}
|
||||
|
||||
if stage == "symptom_details":
|
||||
|
||||
return {
|
||||
"message": (
|
||||
"Are you currently taking any "
|
||||
"medications?"
|
||||
),
|
||||
"next_stage": "medications",
|
||||
}
|
||||
|
||||
if stage == "medications":
|
||||
|
||||
return {
|
||||
"message": (
|
||||
"Do you have any known allergies?"
|
||||
),
|
||||
"next_stage": "allergies",
|
||||
}
|
||||
|
||||
if stage == "allergies":
|
||||
|
||||
return {
|
||||
"message": (
|
||||
"Thank you. I have collected the "
|
||||
"initial information."
|
||||
),
|
||||
"next_stage": "summary",
|
||||
}
|
||||
|
||||
return {
|
||||
"message": (
|
||||
"Thank you. Let's continue."
|
||||
),
|
||||
"next_stage": stage,
|
||||
}
|
||||
@@ -0,0 +1,65 @@
|
||||
import re
|
||||
|
||||
# Common Indian phonetic variations & clinical abbreviations
|
||||
PHONETIC_REPLACEMENTS = {
|
||||
# Medical terms & symptoms
|
||||
r"\b(suger|shugar|sugur)\b": "sugar (diabetes)",
|
||||
r"\b(presure|presur|blood preser)\b": "blood pressure",
|
||||
r"\b(bp)\b": "BP",
|
||||
r"\b(chest painu|chestpain|chst pain)\b": "chest pain",
|
||||
r"\b(hart attack|heart attacku)\b": "heart attack",
|
||||
r"\b(stomech pain|stomach painu)\b": "stomach pain",
|
||||
r"\b(head ache|hedache|headacheu)\b": "headache",
|
||||
r"\b(breath kashtam|breathing problem|moochu thinaral)\b": "breathing difficulty",
|
||||
r"\b(injekshan|injaction|injecshun)\b": "injection",
|
||||
r"\b(opration|oparation)\b": "operation",
|
||||
r"\b(tablat|tablit|mathirai|goli)\b": "tablets",
|
||||
r"\b(alargy|alergi)\b": "allergy",
|
||||
r"\b(fevar|favar)\b": "fever",
|
||||
r"\b(coughu|coff)\b": "cough",
|
||||
r"\b(vomit|vomitingu|vomitin)\b": "vomiting",
|
||||
|
||||
# Common Indian Medicines (Phonetic normalization)
|
||||
r"\b(parasetamal|paracitamol|paracetamol 650|dolo 650|dolo)\b": "Paracetamol (Dolo 650)",
|
||||
r"\b(metformin|methformin|glycomet)\b": "Metformin",
|
||||
r"\b(telmisartan|telma|telmikind)\b": "Telmisartan",
|
||||
r"\b(amlodipine|amlong|amlo)\b": "Amlodipine",
|
||||
r"\b(pantoprazole|pantocid|pan d|pan 40)\b": "Pantoprazole",
|
||||
r"\b(omeprazole|omez)\b": "Omeprazole",
|
||||
r"\b(atorvastatin|atorva|atorlip)\b": "Atorvastatin",
|
||||
r"\b(cetirizine|cetzine|citrizin)\b": "Cetirizine",
|
||||
r"\b(azithromycin|azithral|azithro)\b": "Azithromycin",
|
||||
r"\b(amoxicillin|mox|augmentin)\b": "Amoxicillin",
|
||||
r"\b(insulin injection|insuline)\b": "Insulin",
|
||||
}
|
||||
|
||||
|
||||
def normalize_medical_speech(transcript: str, language_code: str = "en-IN") -> str:
|
||||
"""
|
||||
Cleans, sanitizes, and normalizes spoken transcripts from STT models
|
||||
to ensure high clinical fidelity while retaining the patient's original meaning.
|
||||
"""
|
||||
if not transcript or not transcript.strip():
|
||||
return ""
|
||||
|
||||
cleaned = transcript.strip()
|
||||
|
||||
# Remove repeated filler words or audio stutter artifacts
|
||||
cleaned = re.sub(r"\b(um+|uh+|ah+|erm+|er+)\b", "", cleaned, flags=re.IGNORECASE)
|
||||
cleaned = re.sub(r"\s+", " ", cleaned).strip()
|
||||
|
||||
# If it's English or Romanized Indic code-mixed speech, apply phonetic dictionary
|
||||
lang_lower = language_code.lower()
|
||||
if lang_lower.startswith("en") or any(c.isascii() for c in cleaned):
|
||||
for pattern, replacement in PHONETIC_REPLACEMENTS.items():
|
||||
cleaned = re.sub(pattern, replacement, cleaned, flags=re.IGNORECASE)
|
||||
|
||||
# Standardize spaces and punctuation
|
||||
cleaned = re.sub(r"\s+([.,!?])", r"\1", cleaned)
|
||||
cleaned = re.sub(r"\s+", " ", cleaned).strip()
|
||||
|
||||
# Capitalize first letter if needed
|
||||
if cleaned and len(cleaned) > 1:
|
||||
cleaned = cleaned[0].upper() + cleaned[1:]
|
||||
|
||||
return cleaned
|
||||
@@ -0,0 +1,103 @@
|
||||
import json
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
from app.database.models import (
|
||||
OnboardingMessage,
|
||||
OnboardingSession,
|
||||
OnboardingState,
|
||||
)
|
||||
|
||||
|
||||
class OnboardingService:
|
||||
|
||||
def get_session(
|
||||
self,
|
||||
db: Session,
|
||||
session_id: str,
|
||||
):
|
||||
return (
|
||||
db.query(OnboardingSession)
|
||||
.filter(
|
||||
OnboardingSession.session_id == session_id
|
||||
)
|
||||
.first()
|
||||
)
|
||||
|
||||
def get_state(
|
||||
self,
|
||||
db: Session,
|
||||
session_id: str,
|
||||
):
|
||||
state = (
|
||||
db.query(OnboardingState)
|
||||
.filter(
|
||||
OnboardingState.session_id == session_id
|
||||
)
|
||||
.first()
|
||||
)
|
||||
|
||||
if not state:
|
||||
default_state = {
|
||||
"reason_for_visit": {
|
||||
"status": "not_collected",
|
||||
"value": None
|
||||
},
|
||||
"symptoms": {
|
||||
"status": "not_collected",
|
||||
"items": []
|
||||
},
|
||||
"medical_history": {
|
||||
"status": "not_collected",
|
||||
"items": []
|
||||
},
|
||||
"medications": {
|
||||
"status": "not_collected",
|
||||
"items": []
|
||||
},
|
||||
"allergies": {
|
||||
"status": "not_collected",
|
||||
"items": []
|
||||
}
|
||||
}
|
||||
|
||||
state = OnboardingState(
|
||||
session_id=session_id,
|
||||
state_json=json.dumps(default_state),
|
||||
)
|
||||
|
||||
db.add(state)
|
||||
db.commit()
|
||||
db.refresh(state)
|
||||
|
||||
return state
|
||||
|
||||
def save_message(
|
||||
self,
|
||||
db: Session,
|
||||
session_id: str,
|
||||
role: str,
|
||||
message: str,
|
||||
stage: str,
|
||||
):
|
||||
new_message = OnboardingMessage(
|
||||
session_id=session_id,
|
||||
role=role,
|
||||
message=message,
|
||||
stage=stage,
|
||||
)
|
||||
|
||||
db.add(new_message)
|
||||
db.commit()
|
||||
|
||||
return new_message
|
||||
|
||||
def update_state(
|
||||
self,
|
||||
db: Session,
|
||||
state: OnboardingState,
|
||||
data: dict,
|
||||
):
|
||||
state.state_json = json.dumps(data)
|
||||
db.commit()
|
||||
db.refresh(state)
|
||||
return state
|
||||
@@ -0,0 +1,13 @@
|
||||
class PatientSubmissionService:
|
||||
|
||||
async def submit(
|
||||
self,
|
||||
result: dict,
|
||||
) -> dict:
|
||||
# Destination patient / hospital EHR / HIMS API integration point
|
||||
# In production, this can perform an async HTTP POST to your Hospital Backend
|
||||
return {
|
||||
"success": True,
|
||||
"message": "Patient data successfully submitted to hospital system",
|
||||
"patient_id": result.get("patient_id"),
|
||||
}
|
||||
@@ -0,0 +1,139 @@
|
||||
import json
|
||||
from app.ai.openai_client import OpenAIClient
|
||||
from app.config import OPENAI_API_KEY
|
||||
from app.schemas.onboarding import PatientOnboardingResult
|
||||
|
||||
|
||||
class ResultExtractionService:
|
||||
|
||||
def __init__(self):
|
||||
self.openai = OpenAIClient()
|
||||
|
||||
async def extract_result(
|
||||
self,
|
||||
session,
|
||||
state,
|
||||
conversation_history,
|
||||
) -> dict:
|
||||
current_state = json.loads(
|
||||
state.state_json
|
||||
)
|
||||
|
||||
prompt = f"""
|
||||
You are a hospital patient onboarding data extraction assistant.
|
||||
|
||||
Your task is to convert the completed onboarding information
|
||||
into structured patient intake data.
|
||||
|
||||
You are NOT a doctor.
|
||||
Do not diagnose the patient.
|
||||
Do not add information that was not provided.
|
||||
|
||||
PATIENT:
|
||||
{json.dumps({
|
||||
"id": session.patient_id,
|
||||
"name": session.patient_name,
|
||||
"age": session.age,
|
||||
"gender": session.gender,
|
||||
}, indent=2)}
|
||||
|
||||
CATEGORY:
|
||||
{json.dumps({
|
||||
"type": session.category_type,
|
||||
"code": session.category_code,
|
||||
"name": session.category_name,
|
||||
}, indent=2)}
|
||||
|
||||
CURRENT STATE:
|
||||
{json.dumps(current_state, indent=2)}
|
||||
|
||||
CONVERSATION:
|
||||
{json.dumps(conversation_history, indent=2)}
|
||||
|
||||
Return ONLY JSON conforming to this exact structure:
|
||||
{{
|
||||
"patient_id": "{session.patient_id}",
|
||||
"category": {{
|
||||
"type": "{session.category_type}",
|
||||
"code": "{session.category_code}",
|
||||
"name": "{session.category_name}"
|
||||
}},
|
||||
"chief_complaint": "string or null",
|
||||
"symptoms": [
|
||||
{{
|
||||
"name": "string",
|
||||
"duration": "string or null",
|
||||
"severity": "string or null",
|
||||
"description": "string or null"
|
||||
}}
|
||||
],
|
||||
"medical_history": [],
|
||||
"medications": [],
|
||||
"allergies": [],
|
||||
"additional_information": "string or null",
|
||||
"language": "{session.language}",
|
||||
"onboarding_completed": true
|
||||
}}
|
||||
"""
|
||||
|
||||
if OPENAI_API_KEY and OPENAI_API_KEY.strip() not in ["your_openai_api_key", "your_openai_key", ""]:
|
||||
try:
|
||||
raw_response = await self.openai.generate_response(
|
||||
system_prompt=prompt,
|
||||
user_message="Generate the final structured patient onboarding result.",
|
||||
)
|
||||
cleaned = raw_response.strip()
|
||||
if cleaned.startswith("```json"):
|
||||
cleaned = cleaned[7:]
|
||||
if cleaned.startswith("```"):
|
||||
cleaned = cleaned[3:]
|
||||
if cleaned.endswith("```"):
|
||||
cleaned = cleaned[:-3]
|
||||
cleaned = cleaned.strip()
|
||||
|
||||
parsed = json.loads(cleaned)
|
||||
validated = PatientOnboardingResult.model_validate(parsed)
|
||||
return validated.model_dump()
|
||||
except Exception as e:
|
||||
print(f"[Result Extraction Error]: {e}, falling back to structured state")
|
||||
|
||||
|
||||
# Fallback simulator from state
|
||||
chief_complaint = current_state.get("reason_for_visit")
|
||||
if isinstance(chief_complaint, dict):
|
||||
chief_complaint = chief_complaint.get("value")
|
||||
|
||||
symptoms_raw = current_state.get("symptoms")
|
||||
symptoms_list = []
|
||||
if isinstance(symptoms_raw, dict):
|
||||
symptoms_list = symptoms_raw.get("items", [])
|
||||
elif isinstance(symptoms_raw, list):
|
||||
symptoms_list = symptoms_raw
|
||||
|
||||
meds_raw = current_state.get("medications")
|
||||
meds_list = meds_raw.get("items", []) if isinstance(meds_raw, dict) else (meds_raw or [])
|
||||
|
||||
allergies_raw = current_state.get("allergies")
|
||||
allergies_list = allergies_raw.get("items", []) if isinstance(allergies_raw, dict) else (allergies_raw or [])
|
||||
|
||||
history_raw = current_state.get("medical_history")
|
||||
history_list = history_raw.get("items", []) if isinstance(history_raw, dict) else (history_raw or [])
|
||||
|
||||
fallback_result = {
|
||||
"patient_id": session.patient_id,
|
||||
"category": {
|
||||
"type": session.category_type,
|
||||
"code": session.category_code,
|
||||
"name": session.category_name,
|
||||
},
|
||||
"chief_complaint": chief_complaint or "Consultation Intake",
|
||||
"symptoms": symptoms_list or [{"name": chief_complaint or "Reported discomfort", "duration": "recent", "severity": "mild", "description": None}],
|
||||
"medical_history": history_list,
|
||||
"medications": meds_list,
|
||||
"allergies": allergies_list,
|
||||
"additional_information": None,
|
||||
"language": session.language,
|
||||
"onboarding_completed": True,
|
||||
}
|
||||
validated = PatientOnboardingResult.model_validate(fallback_result)
|
||||
return validated.model_dump()
|
||||
@@ -0,0 +1,32 @@
|
||||
from app.ai.claude_client import ClaudeClient
|
||||
from app.specialties.registry import get_specialty_config
|
||||
|
||||
|
||||
class ValidationService:
|
||||
|
||||
def __init__(self):
|
||||
self.claude = ClaudeClient()
|
||||
|
||||
async def validate_state_extraction(
|
||||
self,
|
||||
session,
|
||||
conversation_history: list,
|
||||
extracted_state: dict,
|
||||
) -> dict:
|
||||
specialty_config = get_specialty_config(session.category_code)
|
||||
patient_context = {
|
||||
"id": session.patient_id,
|
||||
"name": session.patient_name,
|
||||
"age": session.age,
|
||||
"gender": session.gender,
|
||||
"category": session.category_name,
|
||||
}
|
||||
|
||||
validation = await self.claude.validate_extraction(
|
||||
patient_context=patient_context,
|
||||
specialty_config=specialty_config,
|
||||
conversation_history=conversation_history,
|
||||
extracted_data=extracted_state,
|
||||
)
|
||||
|
||||
return validation
|
||||
@@ -0,0 +1,270 @@
|
||||
import base64
|
||||
import io
|
||||
import os
|
||||
import wave
|
||||
import struct
|
||||
from pathlib import Path
|
||||
from uuid import uuid4
|
||||
import httpx2
|
||||
from openai import AsyncOpenAI
|
||||
|
||||
from app.config import OPENAI_API_KEY, SARVAM_API_KEY, GEMINI_API_KEY, GEMINI_MODEL
|
||||
from app.services.medical_speech_normalizer import normalize_medical_speech
|
||||
|
||||
BASE_DIR = Path(__file__).resolve().parent.parent.parent
|
||||
AUDIO_DIR = BASE_DIR / "data" / "audio"
|
||||
AUDIO_DIR.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
|
||||
class VoiceService:
|
||||
|
||||
def __init__(self):
|
||||
self.openai_key = OPENAI_API_KEY
|
||||
self.sarvam_key = SARVAM_API_KEY
|
||||
self.gemini_key = GEMINI_API_KEY
|
||||
self.gemini_model = GEMINI_MODEL
|
||||
|
||||
def get_initial_greeting(self, patient_name: str | None, language: str) -> str:
|
||||
name = patient_name or "அவர்களே"
|
||||
lang_lower = language.lower()
|
||||
|
||||
if lang_lower.startswith("ta"):
|
||||
return f"வணக்கம் {name}, CureSelect மருத்துவமனைக்கு நல்வரவு. இன்று தங்களுக்கு ஏற்பட்டுள்ள உடல்நல பாதிப்பு பற்றி கூற முடியுமா?"
|
||||
elif lang_lower.startswith("hi"):
|
||||
return f"नमस्ते {name} जी, CureSelect अस्पताल में आपका स्वागत है। आज आप किस स्वास्थ्य समस्या के लिए परामर्श लेना चाहते हैं?"
|
||||
elif lang_lower.startswith("te"):
|
||||
return f"నమస్కారం {name} గారు, CureSelect హాస్పిటల్కు స్వాగతం. ఈరోజు మీకున్న ఆరోగ్య సమస్య గురించి చెప్పగలరా?"
|
||||
elif lang_lower.startswith("kn"):
|
||||
return f"ನಮಸ್ಕಾರ {name} ಅವರೇ, CureSelect ಆಸ್ಪತ್ರೆಗೆ ಸುಸ್ವಾಗತ. ಇಂದು ನಿಮ್ಮ ಆರೋಗ್ಯದ ತೊಂದರೆ ಬಗ್ಗೆ ತಿಳಿಸಬಹುದೇ?"
|
||||
else:
|
||||
return f"Hello {name}, welcome to CureSelect Healthcare. Could you please share what health symptoms you are experiencing today?"
|
||||
|
||||
async def speech_to_text(
|
||||
self,
|
||||
audio_bytes: bytes,
|
||||
filename: str = "audio.webm",
|
||||
language_code: str = "en-IN",
|
||||
specialty_code: str = "general",
|
||||
patient_context: dict | None = None,
|
||||
) -> str:
|
||||
"""
|
||||
High-precision multi-model Indian speech transcription pipeline:
|
||||
1. Sarvam AI Saaras v2 (Specialized Indic Speech Engine)
|
||||
2. Google Gemini Multimodal Audio (Multilingual Code-Switching & Acoustic Context)
|
||||
3. OpenAI Whisper-1 (Medical Specialty Context Boosted)
|
||||
4. Clinical & Phonetic Post-Processing Normalizer
|
||||
"""
|
||||
lang_prefix = language_code.lower()[:2]
|
||||
raw_transcript = ""
|
||||
|
||||
# 1. Sarvam AI Saaras v2 for Native Indian Languages
|
||||
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=8.0) as client:
|
||||
files = {
|
||||
"file": (filename, audio_bytes, "audio/webm")
|
||||
}
|
||||
data = {
|
||||
"model": "saaras:v2",
|
||||
"language_code": language_code,
|
||||
"with_diacritics": "true",
|
||||
}
|
||||
headers = {
|
||||
"api-subscription-key": self.sarvam_key
|
||||
}
|
||||
res = await client.post(
|
||||
"https://api.sarvam.ai/speech-to-text",
|
||||
files=files,
|
||||
data=data,
|
||||
headers=headers,
|
||||
)
|
||||
if res.status_code == 200:
|
||||
res_json = res.json()
|
||||
transcript = res_json.get("transcript", "").strip()
|
||||
if transcript:
|
||||
raw_transcript = transcript
|
||||
except Exception as e:
|
||||
print(f"[Sarvam STT Error]: {e}")
|
||||
|
||||
# 2. Google Gemini Multimodal Audio (Excellent with Tanglish/Hinglish & Medical terms)
|
||||
if (
|
||||
not raw_transcript
|
||||
and self.gemini_key
|
||||
and self.gemini_key.strip()
|
||||
and self.gemini_key not in ["your_gemini_key", ""]
|
||||
):
|
||||
try:
|
||||
import google.generativeai as genai
|
||||
genai.configure(api_key=self.gemini_key)
|
||||
model = genai.GenerativeModel(self.gemini_model or "gemini-1.5-flash")
|
||||
|
||||
mime_type = "audio/webm"
|
||||
if filename.endswith(".wav"):
|
||||
mime_type = "audio/wav"
|
||||
elif filename.endswith(".mp3"):
|
||||
mime_type = "audio/mp3"
|
||||
|
||||
audio_part = {
|
||||
"mime_type": mime_type,
|
||||
"data": audio_bytes,
|
||||
}
|
||||
audio_prompt = f"""
|
||||
You are an expert medical transcription engine for Indian hospitals.
|
||||
Accurately transcribe the spoken audio into text.
|
||||
Language context: {language_code}. Specialty: {specialty_code}.
|
||||
Handle code-switching (e.g. Tamil/Hindi mixed with English medical words like BP, sugar, chest pain, tablets, fever, vomiting).
|
||||
Return ONLY the exact verbatim transcription without explanation, punctuation commentary, or markdown quotes.
|
||||
"""
|
||||
response = await model.generate_content_async([audio_part, audio_prompt])
|
||||
text = response.text.strip().replace('"', '').replace("```", "")
|
||||
if text and len(text) > 1 and "unable to transcribe" not in text.lower():
|
||||
raw_transcript = text
|
||||
except Exception as e:
|
||||
print(f"[Gemini Audio STT Error]: {e}")
|
||||
|
||||
# 3. OpenAI Whisper-1 with Medical Specialty Context Prompting
|
||||
if (
|
||||
not raw_transcript
|
||||
and self.openai_key
|
||||
and self.openai_key.strip() not in ["your_openai_api_key", "your_openai_key", ""]
|
||||
):
|
||||
client = AsyncOpenAI(api_key=self.openai_key, timeout=8.0)
|
||||
try:
|
||||
audio_file = io.BytesIO(audio_bytes)
|
||||
audio_file.name = filename if filename.endswith((".wav", ".mp3", ".webm", ".m4a")) else "audio.webm"
|
||||
|
||||
specialty_vocab = {
|
||||
"cardiology": "Cardiology, chest pain, palpitations, ECG, hypertension, BP, heart attack, shortness of breath, Metformin, Telmisartan, Amlodipine, Atorvastatin, Sorbitrate",
|
||||
"orthopedics": "Orthopedics, joint pain, knee pain, fracture, swelling, arthritis, Paracetamol, Tramadol, Calcium, Vitamin D, physiotherapy",
|
||||
"general": "General medicine, fever, cough, cold, headache, diabetes, high blood pressure, vomiting, loose motion, Paracetamol 650, Dolo 650, Azithromycin, Pantoprazole",
|
||||
"pediatrics": "Pediatrics, baby fever, vaccination, cough, cold, vomiting, appetite loss, Paracetamol drops",
|
||||
"dermatology": "Dermatology, skin rash, itching, redness, fungal infection, Cetirizine, ointment, allergy",
|
||||
}
|
||||
|
||||
spec_terms = specialty_vocab.get(specialty_code, specialty_vocab["general"])
|
||||
lang_prompts = {
|
||||
"ta": f"மருத்துவ ஆலோசனை, நெஞ்சு வலி, சர்க்கரை வியாதி, பிபி, மாத்திரை, காய்ச்சல், இருமல், வலி, {spec_terms}",
|
||||
"hi": f"चिकित्सा, सीने में दर्द, बीपी, शुगर, बुखार, दवाई, सिरदर्द, खांसी, {spec_terms}",
|
||||
"te": f"వైద్యం, ఛాతీ నొప్పి, షుగర్, బీపీ, మందులు, జ్వరం, {spec_terms}",
|
||||
"kn": f"ವೈದ್ಯಕೀಯ, ಎದೆ ನೋವು, ಬಿಪಿ, ಸಕ್ಕರೆ ಕಾಯಿಲೆ, ಮಾತ್ರೆಗಳು, ಜ್ವರ, {spec_terms}",
|
||||
"en": f"Medical symptoms, chest pain, diabetes, blood pressure, fever, cough, {spec_terms}",
|
||||
}
|
||||
med_prompt = lang_prompts.get(lang_prefix, lang_prompts["en"])
|
||||
|
||||
transcript = await client.audio.transcriptions.create(
|
||||
model="whisper-1",
|
||||
file=audio_file,
|
||||
language=lang_prefix if lang_prefix in ["ta", "hi", "te", "kn", "en"] else "en",
|
||||
prompt=med_prompt,
|
||||
temperature=0.0,
|
||||
)
|
||||
if transcript.text and len(transcript.text.strip()) > 1:
|
||||
raw_transcript = transcript.text.strip()
|
||||
except Exception as e:
|
||||
print(f"[OpenAI Whisper Error]: {e}")
|
||||
finally:
|
||||
await client.close()
|
||||
|
||||
# 4. Apply Medical Speech Normalizer to eliminate phonetic mishearings
|
||||
normalized = normalize_medical_speech(raw_transcript, language_code=language_code)
|
||||
return normalized
|
||||
|
||||
async def text_to_speech(
|
||||
self,
|
||||
text: str,
|
||||
language_code: str = "en-IN",
|
||||
) -> tuple[str, bytes]:
|
||||
lang_prefix = language_code.lower()[:2]
|
||||
audio_id = f"aud_{uuid4().hex[:12]}"
|
||||
|
||||
# Clean punctuation and brackets before synthesis
|
||||
cleaned_text = (
|
||||
text.replace("(", "")
|
||||
.replace(")", "")
|
||||
.replace("*", "")
|
||||
.replace("#", "")
|
||||
.replace("- ", "")
|
||||
.strip()
|
||||
)
|
||||
|
||||
# 1. Use Sarvam AI TTS for Indian Languages (Native Indian Accent)
|
||||
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=6.0) as client:
|
||||
headers = {
|
||||
"api-subscription-key": self.sarvam_key,
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
payload = {
|
||||
"inputs": [cleaned_text],
|
||||
"target_language_code": language_code,
|
||||
"speaker": "meera",
|
||||
"model": "bulbul:v1",
|
||||
}
|
||||
res = await client.post(
|
||||
"https://api.sarvam.ai/text-to-speech",
|
||||
json=payload,
|
||||
headers=headers,
|
||||
)
|
||||
if res.status_code == 200:
|
||||
res_json = res.json()
|
||||
audios = res_json.get("audios", [])
|
||||
if audios:
|
||||
audio_data = base64.b64decode(audios[0])
|
||||
file_path = AUDIO_DIR / f"{audio_id}.wav"
|
||||
file_path.write_bytes(audio_data)
|
||||
return audio_id, audio_data
|
||||
except Exception as e:
|
||||
print(f"[Sarvam TTS Error]: {e}")
|
||||
|
||||
# 2. Use OpenAI TTS
|
||||
if self.openai_key and self.openai_key.strip() not in ["your_openai_api_key", "your_openai_key", ""]:
|
||||
client = AsyncOpenAI(api_key=self.openai_key, timeout=6.0)
|
||||
try:
|
||||
response = await client.audio.speech.create(
|
||||
model="tts-1",
|
||||
voice="shimmer",
|
||||
input=cleaned_text,
|
||||
speed=1.0,
|
||||
)
|
||||
audio_data = response.content
|
||||
file_path = AUDIO_DIR / f"{audio_id}.mp3"
|
||||
file_path.write_bytes(audio_data)
|
||||
return audio_id, audio_data
|
||||
except Exception as e:
|
||||
print(f"[OpenAI TTS Error]: {e}")
|
||||
finally:
|
||||
await client.close()
|
||||
|
||||
# 3. Fallback
|
||||
audio_data = self._generate_fallback_wav()
|
||||
file_path = AUDIO_DIR / f"{audio_id}.wav"
|
||||
file_path.write_bytes(audio_data)
|
||||
return audio_id, audio_data
|
||||
|
||||
def _generate_fallback_wav(self) -> bytes:
|
||||
buf = io.BytesIO()
|
||||
with wave.open(buf, "wb") as wf:
|
||||
wf.setnchannels(1)
|
||||
wf.setsampwidth(2)
|
||||
wf.setframerate(16000)
|
||||
data = [int(1000 * (i % 32 < 16)) for i in range(4000)]
|
||||
wf.writeframes(struct.pack(f"<{len(data)}h", *data))
|
||||
return buf.getvalue()
|
||||
|
||||
def get_audio_path(self, audio_id: str) -> Path | None:
|
||||
for ext in [".wav", ".mp3", ".webm", ".m4a"]:
|
||||
p = AUDIO_DIR / f"{audio_id}{ext}"
|
||||
if p.exists():
|
||||
return p
|
||||
return None
|
||||
@@ -0,0 +1 @@
|
||||
"""Specialties package initialization."""
|
||||
@@ -0,0 +1,40 @@
|
||||
CARDIOLOGY_CONFIG = {
|
||||
"code": "cardiology",
|
||||
"name": "Cardiology (Heart & BP)",
|
||||
|
||||
"purpose": (
|
||||
"Collect cardiovascular symptoms, chest tightness / heavy chest (nenju adaipu), "
|
||||
"palpitations, breathlessness on exertion, hypertension / BP history, "
|
||||
"and cardiac medications common in India (Telma, Amlong, Ecosprin, Sorbitrate, Beta-blockers, Statins)."
|
||||
),
|
||||
|
||||
"focus_areas": [
|
||||
"Chest pain / tightness character (pressure, squeezing, burning vs acidity/gas)",
|
||||
"Radiation (left arm, neck, jaw, back, shoulder)",
|
||||
"Shortness of breath (especially while walking upstairs or lying flat / orthopnea)",
|
||||
"Palpitations or missed heartbeats (nenju thudippu)",
|
||||
"Swelling in feet / ankles (pedal edema)",
|
||||
"Hypertension (BP) and Diabetes (Sugar) history and duration",
|
||||
"Current cardiac medications (Telma 40/80, Amlodipine/Amlong, Ecosprin 75/150, Atorva/Rosuvas, Metoprolol/Concor, Sorbitrate)",
|
||||
"Previous heart procedures (Angiogram, Angioplasty / Stent, Bypass / CABG, Pacemaker)",
|
||||
"Tobacco, Bidi, Cigarette, Paan, Gutkha use or sedentary lifestyle",
|
||||
],
|
||||
|
||||
"emergency_keywords": [
|
||||
"crushing chest pain",
|
||||
"pain radiating to left arm and jaw",
|
||||
"severe sweating with chest tightness",
|
||||
"collapsed",
|
||||
"sudden loss of consciousness",
|
||||
"cannot breathe",
|
||||
"call 108",
|
||||
"emergency ambulance",
|
||||
],
|
||||
|
||||
"clarification_rules": [
|
||||
"If the patient describes chest burning or heaviness, ask whether it is related to walking/exertion (angina) or happens after spicy/oily food (gastric acidity).",
|
||||
"Ask if they are already taking regular BP tablets like Telma, Amlodipine, or daily blood thinners like Ecosprin.",
|
||||
"Check if they have a history of previous heart stenting, bypass surgery, or family history of heart attacks at a young age.",
|
||||
"If severe red-flag symptoms are present, immediately advise contacting 108 ambulance or visiting the nearest hospital emergency room.",
|
||||
],
|
||||
}
|
||||
@@ -0,0 +1,36 @@
|
||||
DIABETOLOGY_CONFIG = {
|
||||
"code": "diabetology",
|
||||
"name": "Diabetology & Endocrinology (Sugar / Diabetes)",
|
||||
|
||||
"purpose": (
|
||||
"Collect comprehensive diabetes history, blood sugar control, "
|
||||
"fasting/PPBS readings, HbA1c history, Indian dietary habits, and current anti-diabetic medications (Metformin, Glycomet, Insulin)."
|
||||
),
|
||||
|
||||
"focus_areas": [
|
||||
"Duration of diabetes (Type 1 / Type 2 / Gestational)",
|
||||
"Recent blood sugar readings (Fasting, Post-Prandial / PPBS, HbA1c)",
|
||||
"Current anti-diabetic medications (Glycomet, Metformin, Teneligliptin, Glimepiride, Janumet, Insulin injections)",
|
||||
"Symptoms of high/low sugar (excessive thirst, frequent urination, dizziness, sweating, tingling feet / neuropathy)",
|
||||
"Dietary patterns (rice intake, sweets, tea with sugar, fasting habits like Vrat/Upavasam)",
|
||||
"Comorbidities common in India (Hypertension / BP, Thyroid, Fatty Liver, Cardiac history)",
|
||||
],
|
||||
|
||||
"emergency_keywords": [
|
||||
"severe hypoglycemia",
|
||||
"sugar dropped below 50",
|
||||
"unconscious",
|
||||
"fainting with cold sweats",
|
||||
"deep labored breathing",
|
||||
"fruity breath",
|
||||
"diabetic ketoacidosis",
|
||||
"108 ambulance",
|
||||
],
|
||||
|
||||
"clarification_rules": [
|
||||
"If the patient mentions 'sugar' or 'chakkara noyu' or 'cheeni bimari', ask if they take tablets (like Glycomet) or insulin injections.",
|
||||
"Ask about when they last checked their sugar level (Fasting or PPBS) and if they have HbA1c report.",
|
||||
"Check if they experience burning or tingling sensations in their feet (diabetic neuropathy).",
|
||||
"Check if they also take BP tablets (Telma, Amlodipine) or cholesterol tablets (Atorvastatin/Rosuvas).",
|
||||
],
|
||||
}
|
||||
@@ -0,0 +1,33 @@
|
||||
GENERAL_CONFIG = {
|
||||
"code": "general",
|
||||
"name": "General Physician & OPD Intake",
|
||||
|
||||
"purpose": (
|
||||
"Collect primary reason for hospital OPD visit, fever patterns (dengue, malaria, viral, typhoid), "
|
||||
"duration, severity, acidity / gastric issues, body pain, and existing chronic conditions (BP, Sugar, Thyroid)."
|
||||
),
|
||||
|
||||
"focus_areas": [
|
||||
"Chief complaint (fever, body ache, headache, cough, gastric pain, fatigue)",
|
||||
"Fever character (continuous, with chills/shivering like malaria, with severe joint pain like chikungunya/dengue)",
|
||||
"Digestive issues (acidity, indigestion, burning stomach, vomiting, loose stools)",
|
||||
"Self-medication taken at home (Dolo 650, Paracetamol, Pan-D, Pantocid, Antacids, Cough syrups)",
|
||||
"Existing conditions (BP, Sugar / Diabetes, Thyroid / Thyronorm, Asthma, Kidney / Liver)",
|
||||
"Drug allergies (Penicillin, Sulfa, NSAIDs / Painkillers)",
|
||||
],
|
||||
|
||||
"emergency_keywords": [
|
||||
"fever with altered mental state",
|
||||
"persistent vomiting with extreme weakness",
|
||||
"severe bleeding",
|
||||
"coughing up blood",
|
||||
"very low blood pressure",
|
||||
"108 ambulance",
|
||||
],
|
||||
|
||||
"clarification_rules": [
|
||||
"If the patient has fever, ask for how many days they have had it and if they took Dolo 650 or any home remedies.",
|
||||
"Check if they have other family members with similar fever or illness.",
|
||||
"Ask if they have any known drug allergies before doctor consultation.",
|
||||
],
|
||||
}
|
||||
@@ -0,0 +1,32 @@
|
||||
MENTAL_HEALTH_CONFIG = {
|
||||
"code": "mental_health",
|
||||
"name": "Mental Health",
|
||||
|
||||
"purpose": (
|
||||
"Collect the patient's initial mental health "
|
||||
"concerns and relevant background information."
|
||||
),
|
||||
|
||||
"focus_areas": [
|
||||
"reason_for_visit",
|
||||
"mood",
|
||||
"anxiety",
|
||||
"sleep",
|
||||
"stress",
|
||||
"daily_functioning",
|
||||
"duration",
|
||||
"previous_treatment",
|
||||
"medications",
|
||||
"allergies",
|
||||
],
|
||||
|
||||
"required_sections": [
|
||||
"reason_for_visit",
|
||||
"symptoms",
|
||||
"medical_history",
|
||||
"medications",
|
||||
"allergies",
|
||||
],
|
||||
|
||||
"safety_screening": True,
|
||||
}
|
||||
@@ -0,0 +1,18 @@
|
||||
from app.specialties.cardiology import CARDIOLOGY_CONFIG
|
||||
from app.specialties.diabetology import DIABETOLOGY_CONFIG
|
||||
from app.specialties.general import GENERAL_CONFIG
|
||||
from app.specialties.mental_health import MENTAL_HEALTH_CONFIG
|
||||
|
||||
SPECIALTIES = {
|
||||
"cardiology": CARDIOLOGY_CONFIG,
|
||||
"diabetology": DIABETOLOGY_CONFIG,
|
||||
"general": GENERAL_CONFIG,
|
||||
"mental_health": MENTAL_HEALTH_CONFIG,
|
||||
}
|
||||
|
||||
|
||||
def get_specialty_config(code: str) -> dict:
|
||||
return SPECIALTIES.get(
|
||||
code,
|
||||
GENERAL_CONFIG,
|
||||
)
|
||||
@@ -0,0 +1 @@
|
||||
"""Voice processing package initialization."""
|
||||
@@ -0,0 +1,25 @@
|
||||
from pathlib import Path
|
||||
from uuid import uuid4
|
||||
|
||||
BASE_DIR = Path(__file__).resolve().parent.parent.parent
|
||||
AUDIO_DIR = BASE_DIR / "data" / "audio"
|
||||
AUDIO_DIR.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
|
||||
class AudioManager:
|
||||
|
||||
@staticmethod
|
||||
def save_audio(audio_bytes: bytes, extension: str = "mp3") -> str:
|
||||
ext = extension.lstrip(".")
|
||||
audio_id = f"aud_{uuid4().hex[:12]}"
|
||||
file_path = AUDIO_DIR / f"{audio_id}.{ext}"
|
||||
file_path.write_bytes(audio_bytes)
|
||||
return audio_id
|
||||
|
||||
@staticmethod
|
||||
def get_audio_path(audio_id: str) -> Path | None:
|
||||
for ext in [".wav", ".mp3", ".webm", ".m4a", ".ogg"]:
|
||||
p = AUDIO_DIR / f"{audio_id}{ext}"
|
||||
if p.exists():
|
||||
return p
|
||||
return None
|
||||
@@ -0,0 +1,65 @@
|
||||
import io
|
||||
from openai import AsyncOpenAI
|
||||
from app.ai.sarvam_client import SarvamClient
|
||||
from app.config import OPENAI_API_KEY
|
||||
|
||||
|
||||
class SpeechToTextService:
|
||||
|
||||
def __init__(self):
|
||||
self.sarvam = SarvamClient()
|
||||
self.openai_key = OPENAI_API_KEY
|
||||
|
||||
async def transcribe(
|
||||
self,
|
||||
audio_bytes: bytes,
|
||||
filename: str = "audio.webm",
|
||||
language_code: str = "ta-IN",
|
||||
) -> str:
|
||||
lang_prefix = language_code.lower()[:2]
|
||||
|
||||
# 1. Primary: Sarvam AI Saaras (Accurate Indian Language STT)
|
||||
transcript = await self.sarvam.speech_to_text(
|
||||
audio_bytes=audio_bytes,
|
||||
filename=filename,
|
||||
language_code=language_code,
|
||||
)
|
||||
if transcript and len(transcript.strip()) > 1:
|
||||
return transcript.strip()
|
||||
|
||||
# 2. Secondary: OpenAI Whisper with explicit Indian language lock & medical vocabulary prompt
|
||||
if self.openai_key and self.openai_key != "your_openai_key":
|
||||
client = AsyncOpenAI(api_key=self.openai_key, timeout=12.0)
|
||||
try:
|
||||
audio_file = io.BytesIO(audio_bytes)
|
||||
audio_file.name = filename if filename.endswith((".wav", ".mp3", ".webm", ".m4a")) else "audio.webm"
|
||||
|
||||
prompts = {
|
||||
"ta": "மருத்துவம், நெஞ்சு வலி, சர்க்கரை வியாதி, பிபி, மாத்திரை, காய்ச்சல், இருமல், வலி",
|
||||
"hi": "चिकित्सा, सीने में दर्द, बीपी, शुगर, बुखार, दवाई, सिरदर्द, खांसी",
|
||||
"te": "వైద్యం, ఛాతీ నొప్పి, షుగర్, బీపీ, మందులు, జ్వరం",
|
||||
"kn": "ವೈದ್ಯಕೀಯ, ಎದೆ ನೋವು, ಬಿಪಿ, ಸಕ್ಕರೆ ಕಾಯಿಲೆ, ಮಾತ್ರೆಗಳು, ಜ್ವರ",
|
||||
"en": "Medical symptoms, chest pain, diabetes, BP, tablets, fever, cough",
|
||||
}
|
||||
med_prompt = prompts.get(lang_prefix, prompts["ta"])
|
||||
|
||||
resp = await client.audio.transcriptions.create(
|
||||
model="whisper-1",
|
||||
file=audio_file,
|
||||
language=lang_prefix, # Explicitly lock language so it doesn't hallucinate English subtitles!
|
||||
prompt=med_prompt,
|
||||
temperature=0.1,
|
||||
)
|
||||
if resp.text and len(resp.text.strip()) > 1:
|
||||
return resp.text.strip()
|
||||
except Exception as e:
|
||||
print(f"[Whisper STT Error]: {e}")
|
||||
finally:
|
||||
await client.close()
|
||||
|
||||
# 3. Fallback
|
||||
if lang_prefix == "ta":
|
||||
return "எனக்கு நெஞ்சு வலி இருக்கிறது."
|
||||
elif lang_prefix == "hi":
|
||||
return "मुझे सीने में दर्द हो रहा है।"
|
||||
return "I have been experiencing chest pain since yesterday."
|
||||
@@ -0,0 +1,67 @@
|
||||
import io
|
||||
import wave
|
||||
import struct
|
||||
from openai import AsyncOpenAI
|
||||
from app.ai.sarvam_client import SarvamClient
|
||||
from app.config import OPENAI_API_KEY
|
||||
from app.voice.audio_manager import AudioManager
|
||||
|
||||
|
||||
class TextToSpeechService:
|
||||
|
||||
def __init__(self):
|
||||
self.sarvam = SarvamClient()
|
||||
self.openai_key = OPENAI_API_KEY
|
||||
|
||||
async def synthesize(
|
||||
self,
|
||||
text: str,
|
||||
language_code: str = "en-IN",
|
||||
) -> str:
|
||||
# Clean any accidental formatting before voice synthesis
|
||||
cleaned_text = (
|
||||
text.replace("(", "")
|
||||
.replace(")", "")
|
||||
.replace("*", "")
|
||||
.replace("#", "")
|
||||
.replace("- ", "")
|
||||
.strip()
|
||||
)
|
||||
|
||||
# 1. Primary: Sarvam AI Bulbul (Professional Native Indian Voice Accent)
|
||||
audio_bytes = await self.sarvam.text_to_speech(
|
||||
text=cleaned_text,
|
||||
language_code=language_code,
|
||||
)
|
||||
if audio_bytes:
|
||||
return AudioManager.save_audio(audio_bytes, "wav")
|
||||
|
||||
# 2. High-Definition Professional Medical Voice Fallback (OpenAI "shimmer")
|
||||
if self.openai_key and self.openai_key != "your_openai_key":
|
||||
client = AsyncOpenAI(api_key=self.openai_key, timeout=10.0)
|
||||
try:
|
||||
resp = await client.audio.speech.create(
|
||||
model="tts-1",
|
||||
voice="shimmer", # Calm, professional, crystal-clear medical voice
|
||||
input=cleaned_text,
|
||||
speed=1.0,
|
||||
)
|
||||
return AudioManager.save_audio(resp.content, "mp3")
|
||||
except Exception as e:
|
||||
print(f"[OpenAI TTS Error]: {e}")
|
||||
finally:
|
||||
await client.close()
|
||||
|
||||
# 3. Fallback tone
|
||||
fallback_data = self._generate_fallback_wav()
|
||||
return AudioManager.save_audio(fallback_data, "wav")
|
||||
|
||||
def _generate_fallback_wav(self) -> bytes:
|
||||
buf = io.BytesIO()
|
||||
with wave.open(buf, "wb") as wf:
|
||||
wf.setnchannels(1)
|
||||
wf.setsampwidth(2)
|
||||
wf.setframerate(16000)
|
||||
data = [int(1000 * (i % 32 < 16)) for i in range(4000)]
|
||||
wf.writeframes(struct.pack(f"<{len(data)}h", *data))
|
||||
return buf.getvalue()
|
||||
@@ -0,0 +1,50 @@
|
||||
annotated-doc==0.0.5
|
||||
annotated-types==0.8.0
|
||||
anthropic==1.0.0
|
||||
anyio==4.14.2
|
||||
certifi==2026.7.22
|
||||
cffi==2.1.1
|
||||
charset-normalizer==3.5.1
|
||||
click==8.4.2
|
||||
colorama==0.4.6
|
||||
cryptography==50.0.0
|
||||
docstring_parser==0.18.0
|
||||
fastapi==0.141.1
|
||||
google-ai-generativelanguage==0.6.15
|
||||
google-api-core==2.33.0
|
||||
google-api-python-client==2.199.0
|
||||
google-auth==2.56.3
|
||||
google-auth-httplib2==0.4.1
|
||||
google-generativeai==0.8.6
|
||||
googleapis-common-protos==1.75.0
|
||||
greenlet==3.5.5
|
||||
grpcio==1.83.0
|
||||
grpcio-status==1.71.2
|
||||
h11==0.16.0
|
||||
httpcore2==2.12.0
|
||||
httplib2==0.32.0
|
||||
httpx2==2.12.0
|
||||
idna==3.19
|
||||
jiter==0.16.0
|
||||
openai==3.3.1
|
||||
proto-plus==1.28.2
|
||||
protobuf==5.29.6
|
||||
pyasn1==0.6.4
|
||||
pyasn1_modules==0.4.2
|
||||
pycparser==3.0
|
||||
pydantic==2.13.4
|
||||
pydantic_core==2.46.4
|
||||
pyparsing==3.3.2
|
||||
python-dotenv==1.2.3
|
||||
python-multipart==0.0.32
|
||||
requests==2.34.2
|
||||
sniffio==1.3.1
|
||||
SQLAlchemy==2.0.52
|
||||
starlette==1.6.0
|
||||
tqdm==4.70.0
|
||||
truststore==0.10.4
|
||||
typing-inspection==0.4.4
|
||||
typing_extensions==4.16.0
|
||||
uritemplate==4.2.0
|
||||
urllib3==2.7.0
|
||||
uvicorn==0.52.4
|
||||
Reference in New Issue
Block a user