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

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,
-                "next_stage": "allergies",
+                "reply": conclusion_text,
+                "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,