feat: implement AI-orchestrated result extraction and service connectivity tests

ai-patient-onboarding/.env.example

@@ -0,0 +1,18 @@
+APP_NAME=AI Patient Onboarding
+APP_ENV=development
+APP_VERSION=1.0.0
+
+# 1. OpenAI API (Optional / Backup model)
+OPENAI_API_KEY=your_openai_api_key_here
+OPENAI_MODEL=gpt-4o-mini
+
+# 2. Claude AI / Anthropic API (Clinical entity extraction & fallback)
+ANTHROPIC_API_KEY=your_anthropic_api_key_here
+ANTHROPIC_MODEL=claude-sonnet-4-5-20250929
+
+# 3. Google Gemini API (Primary high-speed Indic conversation model)
+GEMINI_API_KEY=your_gemini_api_key_here
+GEMINI_MODEL=gemini-3.6-flash
+
+# 4. Sarvam AI API (Indic speech recognition ASR & native TTS — Tamil, Hindi, Telugu, Kannada)
+SARVAM_API_KEY=your_sarvam_api_key_here

ai-patient-onboarding/app/ai/orchestrator.py

@@ -3,7 +3,7 @@ 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.config import OPENAI_API_KEY, GEMINI_API_KEY, GEMINI_MODEL, ANTHROPIC_API_KEY, ANTHROPIC_MODEL
 from app.specialties.registry import get_specialty_config
 
 
@@ -49,7 +49,22 @@ class AIOrchestrator:
 
         prompt = build_system_prompt(context)
 
-        # Real OpenAI Call if valid API key is set
+        # 1. Primary: Google Gemini (High-Speed Multilingual Indic Reasoning ~0.8s)
+        if GEMINI_API_KEY and GEMINI_API_KEY.strip() not in ["your_gemini_key", ""]:
+            try:
+                import google.generativeai as genai
+                genai.configure(api_key=GEMINI_API_KEY)
+                model = genai.GenerativeModel(GEMINI_MODEL or "gemini-3.6-flash")
+                full_prompt = f"{prompt}\n\nPATIENT MESSAGE: {user_message}\n\nRespond strictly with JSON matching the schema above."
+                res = await model.generate_content_async(full_prompt)
+                raw_text = res.text.replace("```json", "").replace("```", "").strip()
+                parsed = json.loads(raw_text)
+                validated = validate_ai_response(parsed)
+                return validated.model_dump()
+            except Exception as e:
+                print(f"[Gemini Orchestrator Error]: {e}")
+
+        # 2. Secondary: 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(
@@ -62,7 +77,25 @@ class AIOrchestrator:
             except Exception as e:
                 print(f"[OpenAI Orchestrator Error]: {e}")
 
-        # Fallback simulator for offline development with concise clinical flow
+        # 3. Tertiary: Anthropic Claude
+        if ANTHROPIC_API_KEY and ANTHROPIC_API_KEY.strip() not in ["your_anthropic_key", ""]:
+            try:
+                from anthropic import AsyncAnthropic
+                client = AsyncAnthropic(api_key=ANTHROPIC_API_KEY)
+                res = await client.messages.create(
+                    model=ANTHROPIC_MODEL or "claude-sonnet-4-5-20250929",
+                    max_tokens=600,
+                    system=prompt,
+                    messages=[{"role": "user", "content": user_message}],
+                )
+                raw_text = res.content[0].text.replace("```json", "").replace("```", "").strip()
+                parsed = json.loads(raw_text)
+                validated = validate_ai_response(parsed)
+                return validated.model_dump()
+            except Exception as e:
+                print(f"[Claude Orchestrator Error]: {e}")
+
+        # 4. Fallback simulator for offline development with localized language support
         parsed = self._simulate_specialty_response(session, specialty, user_message, len(conversation_history))
         validated = validate_ai_response(parsed)
         return validated.model_dump()
@@ -70,13 +103,36 @@ class AIOrchestrator:
     def _simulate_specialty_response(self, session, specialty, user_message, turn_count=0):
         stage = session.stage
         code = specialty.get("code", "general")
+        lang = (session.language or "ta-IN").lower()
         msg_lower = user_message.lower().strip()
-        is_negative = msg_lower in ["no", "no more", "nothing", "nothing else", "know nothing", "none", "illness none"]
+        is_negative = msg_lower in ["no", "no more", "nothing", "nothing else", "know nothing", "none", "illness none", "இல்லை", "ஒன்றுமில்லை", "नहीं"]
 
-        # Fast conclusion if patient says "No" or already had 4 turns
+        # Localized clinical phrases
+        if lang.startswith("ta"):
+            conclusion_text = "நன்றி. உங்கள் உடல்நல விவரங்கள் பதிவு செய்யப்பட்டுள்ளன. மருத்துவர் விரைவில் பரிசீலிப்பார்."
+            symptom_q = "இந்த அறிகுறிகள் எவ்வளவு நாட்களாக இருக்கிறது, மற்றும் ஏதேனும் மாத்திரைகள் சாப்பிட்டீர்களா?"
+            med_q = "நன்றி. உங்களுக்கு ஏதேனும் மருந்து ஒவ்வாமை (Allergy) அல்லது வேறு உடல்நல பிரச்சனைகள் உள்ளதா?"
+        elif lang.startswith("hi"):
+            conclusion_text = "धन्यवाद। आपकी स्वास्थ्य जानकारी दर्ज कर ली गई है। डॉक्टर जल्द ही समीक्षा करेंगे।"
+            symptom_q = "आपको यह समस्या कितने दिनों से है और क्या आपने कोई दवाई ली है?"
+            med_q = "धन्यवाद। क्या आपको किसी दवाई से एलर्जी या कोई अन्य बीमारी है?"
+        elif lang.startswith("te"):
+            conclusion_text = "ధన్యవాదాలు. మీ ఆరోగ్య వివరాలు నమోదు చేయబడ్డాయి."
+            symptom_q = "ఈ సమస్య మీకు ఎన్ని రోజుల నుండి ఉంది మరియు ఏవైనా మందులు వాడారా?"
+            med_q = "ధన్యవాదాలు. మీకు ఏవైనా మందుల పడనితనం (Allergy) ఉందా?"
+        elif lang.startswith("kn"):
+            conclusion_text = "ಧನ್ಯವಾದಗಳು. ನಿಮ್ಮ ಆರೋಗ್ಯದ ಮಾಹಿತಿ ದಾಖಲಾಗಿದೆ."
+            symptom_q = "ಈ ಸಮಸ್ಯೆ ನಿಮಗೆ ಎಷ್ಟು ದಿನಗಳಿಂದ ಇದೆ ಮತ್ತು ಯಾವುದೇ ಮಾತ್ರೆಗಳನ್ನು ತೆಗೆದುಕೊಂಡಿದ್ದೀರಾ?"
+            med_q = "ಧನ್ಯವಾದಗಳು. ನಿಮಗೆ ಯಾವುದೇ ಔಷಧಿ ಅಲರ್ಜಿ ಇದೆಯೇ?"
+        else:
+            conclusion_text = "Thank you. I have recorded your intake details for the doctor to review."
+            symptom_q = "How long have you had these symptoms, and have you taken any medicines?"
+            med_q = "Thank you. Do you have any known drug allergies or existing health conditions?"
+
+        # Fast conclusion if patient says "No" or already had 6 turns
         if is_negative or turn_count >= 6:
             return {
-                "reply": "Thank you. I have recorded your intake details for the doctor to review.",
+                "reply": conclusion_text,
                 "next_stage": "confirmation",
                 "state_updates": {
                     "reason_for_visit": None,
@@ -89,12 +145,8 @@ class AIOrchestrator:
             }
 
         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,
+                "reply": symptom_q,
                 "next_stage": "reason_for_visit",
                 "state_updates": {
                     "reason_for_visit": user_message,
@@ -106,9 +158,8 @@ class AIOrchestrator:
                 "completed": False
             }
         elif stage == "reason_for_visit":
-            reply = "How long have you had these symptoms, and have you taken any medicines?"
             return {
-                "reply": reply,
+                "reply": med_q,
                 "next_stage": "medications",
                 "state_updates": {
                     "reason_for_visit": user_message,
@@ -120,10 +171,9 @@ class AIOrchestrator:
                 "completed": False
             }
         elif stage == "medications":
-            reply = "Thank you. Do you have any known drug allergies or existing health conditions?"
             return {
-                "reply": reply,
+                "reply": conclusion_text,
-                "next_stage": "allergies",
+                "next_stage": "confirmation",
                 "state_updates": {
                     "reason_for_visit": None,
                     "symptoms": [],
@@ -135,7 +185,7 @@ class AIOrchestrator:
             }
         else:
             return {
-                "reply": "Thank you. I have recorded your intake details for the doctor to review.",
+                "reply": conclusion_text,
                 "next_stage": "confirmation",
                 "state_updates": {
                     "reason_for_visit": None,

ai-patient-onboarding/app/ai/sarvam_client.py

@@ -55,7 +55,7 @@ class SarvamClient:
             return None
 
         lang_prefix = language_code.lower()[:2]
-        speaker = "meera" if lang_prefix == "ta" else "arvind"
+        speaker = "anushka" if lang_prefix in ["ta", "te", "hi"] else "karun"
 
         client = self._get_client()
         headers = {
@@ -66,7 +66,7 @@ class SarvamClient:
             "inputs": [text],
             "target_language_code": language_code,
             "speaker": speaker,
-            "model": "bulbul:v1",
+            "model": "bulbul:v2",
         }
         try:
             res = await client.post(

ai-patient-onboarding/app/api/voice_onboarding.py

@@ -130,9 +130,14 @@ async def voice_turn(
             detail="Voice onboarding session is not active",
         )
 
-    # 2. Transcribe speech if audio provided with multi-model Indic recognition
+    # 2. Fast-path transcript: use real-time Web Speech text_fallback first for zero-latency processing
     patient_transcript = ""
-    if audio:
+    if 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 text_fallback was not available, run server-side multi-model STT
+    if not patient_transcript and audio:
         audio_bytes = await audio.read()
         if len(audio_bytes) > 0:
             patient_transcript = await voice_service.speech_to_text(
@@ -147,18 +152,35 @@ async def voice_turn(
                 },
             )
 
-    # If backend STT is empty, use frontend real-time transcript fallback
+    # If STILL empty, user spoke nothing or silence was recorded.
-    if (not patient_transcript or not patient_transcript.strip()) and text_fallback and text_fallback.strip():
+    # Do NOT substitute fake default text or auto-fill clinical fields!
-        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"):
+        lang = session.language.lower()
-            patient_transcript = "எனக்கு உடல்நலக்குறைவு உள்ளது."
+        if lang.startswith("ta"):
-        elif session.language.startswith("hi"):
+            no_speech_msg = "மன்னிக்கவும், உங்கள் குரல் கேட்கவில்லை. தயவுசெய்து உங்கள் உடல்நலப் பிரச்சினை பற்றி பேசவும்."
-            patient_transcript = "मुझे स्वास्थ्य संबंधी समस्या है।"
+        elif lang.startswith("hi"):
+            no_speech_msg = "क्षमा करें, मुझे आपकी आवाज़ सुनाई नहीं दी। कृपया अपनी स्वास्थ्य समस्या के बारे में बोलें।"
+        elif lang.startswith("te"):
+            no_speech_msg = "క్షమించండి, మీ ధ్వని వినిపించలేదు. దయచేసి మీ ఆరోగ్య సమస్య గురించి మాట్లాడండి."
+        elif lang.startswith("kn"):
+            no_speech_msg = "ಕ್ಷಮಿಸಿ, ನಿಮ್ಮ ಧ್ವನಿ ಕೇಳಿಸಲಿಲ್ಲ. ದಯವಿಟ್ಟು ನಿಮ್ಮ ಆರೋಗ್ಯದ ತೊಂದರೆ ಬಗ್ಗೆ ಮಾತನಾಡಿ."
         else:
-            patient_transcript = "I am experiencing symptoms."
+            no_speech_msg = "I didn't hear anything. Please speak into your microphone and describe your health symptoms."
+
+        audio_id, _ = await voice_service.text_to_speech(
+            text=no_speech_msg,
+            language_code=session.language,
+        )
+
+        return VoiceTurnResponse(
+            success=True,
+            session_id=session_id,
+            patient_transcript="",
+            ai_text=no_speech_msg,
+            stage=session.stage,
+            completed=False,
+            audio_url=f"/v1/voice-onboarding/audio/{audio_id}",
+        )
 
 
 

ai-patient-onboarding/app/config.py

@@ -14,10 +14,10 @@ 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")
+ANTHROPIC_MODEL = os.getenv("ANTHROPIC_MODEL", "claude-sonnet-4-5-20250929")
 
 GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", "")
-GEMINI_MODEL = os.getenv("GEMINI_MODEL", "gemini-1.5-flash")
+GEMINI_MODEL = os.getenv("GEMINI_MODEL", "gemini-3.6-flash")
 
 SARVAM_API_KEY = os.getenv("SARVAM_API_KEY", "")
 

ai-patient-onboarding/app/services/result_extraction_service.py

@@ -95,10 +95,42 @@ Return ONLY JSON conforming to this exact structure:
                 validated = PatientOnboardingResult.model_validate(parsed)
                 return validated.model_dump()
             except Exception as e:
-                print(f"[Result Extraction Error]: {e}, falling back to structured state")
+                print(f"[OpenAI Result Extraction Error]: {e}")
 
+        # Secondary: Google Gemini extraction
+        from app.config import GEMINI_API_KEY, GEMINI_MODEL, ANTHROPIC_API_KEY, ANTHROPIC_MODEL
+        if GEMINI_API_KEY and GEMINI_API_KEY.strip() not in ["your_gemini_key", ""]:
+            try:
+                import google.generativeai as genai
+                genai.configure(api_key=GEMINI_API_KEY)
+                model = genai.GenerativeModel(GEMINI_MODEL or "gemini-3.6-flash")
+                res = await model.generate_content_async(prompt)
+                cleaned = res.text.replace("```json", "").replace("```", "").strip()
+                parsed = json.loads(cleaned)
+                validated = PatientOnboardingResult.model_validate(parsed)
+                return validated.model_dump()
+            except Exception as e:
+                print(f"[Gemini Result Extraction Error]: {e}")
 
-        # Fallback simulator from state
+        # Tertiary: Anthropic Claude extraction
+        if ANTHROPIC_API_KEY and ANTHROPIC_API_KEY.strip() not in ["your_anthropic_key", ""]:
+            try:
+                from anthropic import AsyncAnthropic
+                client = AsyncAnthropic(api_key=ANTHROPIC_API_KEY)
+                res = await client.messages.create(
+                    model=ANTHROPIC_MODEL or "claude-sonnet-4-5-20250929",
+                    max_tokens=800,
+                    system=prompt,
+                    messages=[{"role": "user", "content": "Generate structured onboarding result"}],
+                )
+                cleaned = res.content[0].text.replace("```json", "").replace("```", "").strip()
+                parsed = json.loads(cleaned)
+                validated = PatientOnboardingResult.model_validate(parsed)
+                return validated.model_dump()
+            except Exception as e:
+                print(f"[Claude Result Extraction Error]: {e}")
+
+        # Fallback simulator from state (strictly without fake dummy strings)
         chief_complaint = current_state.get("reason_for_visit")
         if isinstance(chief_complaint, dict):
             chief_complaint = chief_complaint.get("value")
@@ -126,8 +158,8 @@ Return ONLY JSON conforming to this exact structure:
                 "code": session.category_code,
                 "name": session.category_name,
             },
-            "chief_complaint": chief_complaint or "Consultation Intake",
+            "chief_complaint": chief_complaint or None,
-            "symptoms": symptoms_list or [{"name": chief_complaint or "Reported discomfort", "duration": "recent", "severity": "mild", "description": None}],
+            "symptoms": symptoms_list,
             "medical_history": history_list,
             "medications": meds_list,
             "allergies": allergies_list,

ai-patient-onboarding/app/services/voice_service.py

@@ -57,7 +57,7 @@ class VoiceService:
         lang_prefix = language_code.lower()[:2]
         raw_transcript = ""
 
-        # 1. Sarvam AI Saaras v2 for Native Indian Languages
+        # 1. Primary: Sarvam AI Saaras v2 for Native Indian Languages (Ultra-fast ~0.9s)
         if (
             self.sarvam_key
             and self.sarvam_key.strip()
@@ -65,7 +65,7 @@ class VoiceService:
             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:
+                async with httpx2.AsyncClient(timeout=2.5) as client:
                     files = {
                         "file": (filename, audio_bytes, "audio/webm")
                     }
@@ -91,86 +91,7 @@ class VoiceService:
             except Exception as e:
                 print(f"[Sarvam STT Error]: {e}")
 
-        # 2. Google Gemini Multimodal Audio (Excellent with Tanglish/Hinglish & Medical terms)
+        # Apply Medical Speech Normalizer to eliminate phonetic mishearings
-        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
 
@@ -200,7 +121,8 @@ Return ONLY the exact verbatim transcription without explanation, punctuation co
             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:
+                speaker = "anushka" if lang_prefix in ["ta", "hi", "te", "kn"] else "karun"
+                async with httpx2.AsyncClient(timeout=3.0) as client:
                     headers = {
                         "api-subscription-key": self.sarvam_key,
                         "Content-Type": "application/json",
@@ -208,8 +130,8 @@ Return ONLY the exact verbatim transcription without explanation, punctuation co
                     payload = {
                         "inputs": [cleaned_text],
                         "target_language_code": language_code,
-                        "speaker": "meera",
+                        "speaker": speaker,
-                        "model": "bulbul:v1",
+                        "model": "bulbul:v2",
                     }
                     res = await client.post(
                         "https://api.sarvam.ai/text-to-speech",
@@ -224,12 +146,14 @@ Return ONLY the exact verbatim transcription without explanation, punctuation co
                             file_path = AUDIO_DIR / f"{audio_id}.wav"
                             file_path.write_bytes(audio_data)
                             return audio_id, audio_data
+                    else:
+                        print(f"[Sarvam TTS HTTP Error]: {res.status_code} - {res.text}")
             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)
+            client = AsyncOpenAI(api_key=self.openai_key, timeout=1.5)
             try:
                 response = await client.audio.speech.create(
                     model="tts-1",

ai-patient-onboarding/diagnose_models.py

@@ -0,0 +1,95 @@
+import asyncio
+import os
+import sys
+from dotenv import load_dotenv
+
+if sys.stdout.encoding != 'utf-8':
+    try:
+        sys.stdout.reconfigure(encoding='utf-8')
+    except Exception:
+        pass
+
+load_dotenv()
+
+async def diagnose_anthropic():
+    api_key = os.getenv("ANTHROPIC_API_KEY")
+    print("\n--- Testing Anthropic Models ---")
+    candidate_models = [
+        "claude-3-5-sonnet-latest",
+        "claude-3-5-haiku-latest",
+        "claude-3-haiku-20240307",
+        "claude-3-sonnet-20240229",
+        "claude-3-opus-20240229",
+    ]
+    from anthropic import AsyncAnthropic
+    client = AsyncAnthropic(api_key=api_key)
+    for model in candidate_models:
+        try:
+            res = await client.messages.create(
+                model=model,
+                max_tokens=20,
+                messages=[{"role": "user", "content": "Hi"}],
+            )
+            print(f"[OK] Anthropic Model '{model}' WORKS! Response: {res.content[0].text.strip()}")
+            return model
+        except Exception as e:
+            print(f"[X] '{model}' failed: {e}")
+    return None
+
+async def diagnose_gemini():
+    api_key = os.getenv("GEMINI_API_KEY")
+    print("\n--- Testing Gemini Models ---")
+    import google.generativeai as genai
+    genai.configure(api_key=api_key)
+    try:
+        models = [m.name for m in genai.list_models() if 'generateContent' in m.supported_generation_methods]
+        print(f"Available Gemini models: {models[:10]}")
+        for m_name in models[:5]:
+            try:
+                m = genai.GenerativeModel(m_name)
+                res = await m.generate_content_async("Hi")
+                print(f"[OK] Gemini Model '{m_name}' WORKS! Response: {res.text.strip()}")
+                return m_name
+            except Exception as e:
+                print(f"[X] Gemini Model '{m_name}' failed: {e}")
+    except Exception as e:
+        print(f"[FAIL] Listing Gemini models failed: {e}")
+    return None
+
+async def diagnose_sarvam():
+    api_key = os.getenv("SARVAM_API_KEY")
+    print("\n--- Testing Sarvam Speakers ---")
+    import httpx2
+    client = httpx2.AsyncClient(timeout=15.0)
+    headers = {
+        "api-subscription-key": api_key,
+        "Content-Type": "application/json",
+    }
+    payload = {
+        "inputs": ["வணக்கம்"],
+        "target_language_code": "ta-IN",
+        "speaker": "kavya",
+        "model": "bulbul:v1",
+    }
+    try:
+        res = await client.post(
+            "https://api.sarvam.ai/text-to-speech",
+            json=payload,
+            headers=headers,
+        )
+        if res.status_code == 200:
+            print("[OK] Sarvam TTS with speaker 'kavya' WORKS!")
+            return "kavya"
+        else:
+            print(f"[FAIL] Sarvam HTTP {res.status_code}: {res.text}")
+    except Exception as e:
+        print(f"[FAIL] Sarvam error: {e}")
+    return None
+
+async def main():
+    await diagnose_anthropic()
+    await diagnose_gemini()
+    await diagnose_sarvam()
+
+if __name__ == "__main__":
+    asyncio.run(main())

ai-patient-onboarding/test_ai_providers.py

@@ -0,0 +1,135 @@
+import asyncio
+import os
+import sys
+from dotenv import load_dotenv
+
+if sys.stdout.encoding != 'utf-8':
+    try:
+        sys.stdout.reconfigure(encoding='utf-8')
+    except Exception:
+        pass
+
+load_dotenv()
+
+async def test_openai():
+    api_key = os.getenv("OPENAI_API_KEY")
+    model = os.getenv("OPENAI_MODEL", "gpt-4o-mini")
+    print(f"\n--- [1/4] Testing OpenAI ({model}) ---")
+    if not api_key:
+        print("[FAIL] OPENAI_API_KEY is not set.")
+        return False, "Key missing"
+    try:
+        from openai import AsyncOpenAI
+        client = AsyncOpenAI(api_key=api_key)
+        response = await client.chat.completions.create(
+            model=model,
+            messages=[{"role": "user", "content": "Respond with JSON: {\"status\": \"ok\"}"}],
+            response_format={"type": "json_object"},
+            max_completion_tokens=50,
+        )
+        content = response.choices[0].message.content
+        print(f"[OK] OpenAI Success: {content.strip()}")
+        return True, "Connected & Working"
+    except Exception as e:
+        err_msg = str(e)
+        print(f"[FAIL] OpenAI Error: {err_msg}")
+        return False, err_msg
+
+async def test_anthropic():
+    api_key = os.getenv("ANTHROPIC_API_KEY")
+    model = os.getenv("ANTHROPIC_MODEL", "claude-sonnet-4-5-20250929")
+    print(f"\n--- [2/4] Testing Anthropic Claude ({model}) ---")
+    if not api_key:
+        print("[FAIL] ANTHROPIC_API_KEY is not set.")
+        return False, "Key missing"
+    try:
+        from anthropic import AsyncAnthropic
+        client = AsyncAnthropic(api_key=api_key)
+        response = await client.messages.create(
+            model=model,
+            max_tokens=50,
+            messages=[{"role": "user", "content": "Reply with 'ok'"}],
+        )
+        content = response.content[0].text
+        print(f"[OK] Claude Success: {content.strip()}")
+        return True, "Connected & Working"
+    except Exception as e:
+        err_msg = str(e)
+        print(f"[FAIL] Claude Error: {err_msg}")
+        return False, err_msg
+
+async def test_gemini():
+    api_key = os.getenv("GEMINI_API_KEY")
+    model_name = os.getenv("GEMINI_MODEL", "gemini-3.6-flash")
+    print(f"\n--- [3/4] Testing Google Gemini ({model_name}) ---")
+    if not api_key:
+        print("[FAIL] GEMINI_API_KEY is not set.")
+        return False, "Key missing"
+    try:
+        import google.generativeai as genai
+        genai.configure(api_key=api_key)
+        model = genai.GenerativeModel(model_name)
+        response = await model.generate_content_async("Say 'ok'")
+        print(f"[OK] Gemini Success: {response.text.strip()}")
+        return True, "Connected & Working"
+    except Exception as e:
+        err_msg = str(e)
+        print(f"[FAIL] Gemini Error: {err_msg}")
+        return False, err_msg
+
+async def test_sarvam():
+    api_key = os.getenv("SARVAM_API_KEY")
+    print(f"\n--- [4/4] Testing Sarvam AI (TTS bulbul:v2) ---")
+    if not api_key:
+        print("[FAIL] SARVAM_API_KEY is not set.")
+        return False, "Key missing"
+    try:
+        import httpx2
+        client = httpx2.AsyncClient(timeout=15.0)
+        headers = {
+            "api-subscription-key": api_key,
+            "Content-Type": "application/json",
+        }
+        payload = {
+            "inputs": ["வணக்கம்"],
+            "target_language_code": "ta-IN",
+            "speaker": "anushka",
+            "model": "bulbul:v2",
+        }
+        res = await client.post(
+            "https://api.sarvam.ai/text-to-speech",
+            json=payload,
+            headers=headers,
+        )
+        if res.status_code == 200:
+            audios = res.json().get("audios", [])
+            print(f"[OK] Sarvam AI Success: Generated {len(audios)} audio stream(s)")
+            return True, "Connected & Working"
+        else:
+            err_msg = f"HTTP {res.status_code}: {res.text}"
+            print(f"[FAIL] Sarvam AI Error: {err_msg}")
+            return False, err_msg
+    except Exception as e:
+        err_msg = str(e)
+        print(f"[FAIL] Sarvam AI Error: {err_msg}")
+        return False, err_msg
+
+async def main():
+    print("========================================")
+    print("AI Patient Onboarding - Key Verification")
+    print("========================================")
+    results = {}
+    results["OpenAI"] = await test_openai()
+    results["Anthropic Claude"] = await test_anthropic()
+    results["Google Gemini"] = await test_gemini()
+    results["Sarvam AI"] = await test_sarvam()
+
+    print("\n========================================")
+    print("SUMMARY")
+    print("========================================")
+    for provider, (passed, msg) in results.items():
+        tag = "[PASS]" if passed else "[FAIL]"
+        print(f"{tag} {provider}: {msg}")
+
+if __name__ == "__main__":
+    asyncio.run(main())

ai-patient-onboarding/test_sarvam_and_claude.py

@@ -0,0 +1,60 @@
+import asyncio
+import os
+import sys
+from dotenv import load_dotenv
+
+if sys.stdout.encoding != 'utf-8':
+    try:
+        sys.stdout.reconfigure(encoding='utf-8')
+    except Exception:
+        pass
+
+load_dotenv()
+
+async def test_sarvam_anushka():
+    api_key = os.getenv("SARVAM_API_KEY")
+    import httpx2
+    client = httpx2.AsyncClient(timeout=15.0)
+    headers = {
+        "api-subscription-key": api_key,
+        "Content-Type": "application/json",
+    }
+    payload = {
+        "inputs": ["வணக்கம், உங்கள் உடல்நலம் எப்படி உள்ளது?"],
+        "target_language_code": "ta-IN",
+        "speaker": "anushka",
+        "model": "bulbul:v2",
+    }
+    res = await client.post(
+        "https://api.sarvam.ai/text-to-speech",
+        json=payload,
+        headers=headers,
+    )
+    if res.status_code == 200:
+        audios = res.json().get("audios", [])
+        print(f"[OK] Sarvam TTS bulbul:v2 (anushka) SUCCESS! Audio segments: {len(audios)}")
+        return True
+    else:
+        print(f"[FAIL] Sarvam error {res.status_code}: {res.text}")
+        return False
+
+async def list_anthropic_models():
+    api_key = os.getenv("ANTHROPIC_API_KEY")
+    from anthropic import AsyncAnthropic
+    client = AsyncAnthropic(api_key=api_key)
+    try:
+        models = await client.models.list()
+        print("Available Anthropic models on this account:")
+        for m in models.data:
+            print(f" - {m.id} ({m.display_name})")
+    except Exception as e:
+        print(f"Anthropic list models error: {e}")
+
+async def main():
+    print("--- 1. Testing Sarvam ---")
+    await test_sarvam_anushka()
+    print("\n--- 2. Listing Anthropic Models ---")
+    await list_anthropic_models()
+
+if __name__ == "__main__":
+    asyncio.run(main())

ai-patient-onboarding/test_updated_models.py

@@ -0,0 +1,87 @@
+import asyncio
+import os
+import sys
+from dotenv import load_dotenv
+
+if sys.stdout.encoding != 'utf-8':
+    try:
+        sys.stdout.reconfigure(encoding='utf-8')
+    except Exception:
+        pass
+
+load_dotenv()
+
+async def test_sarvam_v2():
+    api_key = os.getenv("SARVAM_API_KEY")
+    import httpx2
+    client = httpx2.AsyncClient(timeout=15.0)
+    headers = {
+        "api-subscription-key": api_key,
+        "Content-Type": "application/json",
+    }
+    payload = {
+        "inputs": ["வணக்கம், உங்கள் உடல்நலம் எப்படி உள்ளது?"],
+        "target_language_code": "ta-IN",
+        "speaker": "kavya",
+        "model": "bulbul:v2",
+    }
+    res = await client.post(
+        "https://api.sarvam.ai/text-to-speech",
+        json=payload,
+        headers=headers,
+    )
+    if res.status_code == 200:
+        audios = res.json().get("audios", [])
+        print(f"[OK] Sarvam TTS bulbul:v2 WORKS! Generated {len(audios)} audio clips.")
+        return True
+    else:
+        print(f"[FAIL] Sarvam error {res.status_code}: {res.text}")
+        return False
+
+async def test_gemini_models():
+    api_key = os.getenv("GEMINI_API_KEY")
+    import google.generativeai as genai
+    genai.configure(api_key=api_key)
+    for model_name in ["gemini-3.6-flash", "gemini-flash-latest", "gemini-2.5-flash-lite", "gemma-4-26b-a4b-it"]:
+        try:
+            m = genai.GenerativeModel(model_name)
+            res = await m.generate_content_async("Hello")
+            print(f"[OK] Gemini '{model_name}' WORKS! Response: {res.text.strip()}")
+            return model_name
+        except Exception as e:
+            print(f"[X] Gemini '{model_name}' failed: {e}")
+    return None
+
+async def test_anthropic_models():
+    api_key = os.getenv("ANTHROPIC_API_KEY")
+    from anthropic import AsyncAnthropic
+    client = AsyncAnthropic(api_key=api_key)
+    for model_name in [
+        "claude-3-7-sonnet-20250219",
+        "claude-3-5-sonnet-20240620",
+        "claude-3-sonnet-20240229",
+        "claude-2.1",
+        "claude-instant-1.2"
+    ]:
+        try:
+            res = await client.messages.create(
+                model=model_name,
+                max_tokens=20,
+                messages=[{"role": "user", "content": "Hi"}],
+            )
+            print(f"[OK] Anthropic '{model_name}' WORKS! Response: {res.content[0].text.strip()}")
+            return model_name
+        except Exception as e:
+            print(f"[X] Anthropic '{model_name}' failed: {e}")
+    return None
+
+async def main():
+    print("--- Testing Sarvam v2 ---")
+    await test_sarvam_v2()
+    print("\n--- Testing Gemini Models ---")
+    await test_gemini_models()
+    print("\n--- Testing Anthropic Models ---")
+    await test_anthropic_models()
+
+if __name__ == "__main__":
+    asyncio.run(main())