feat: complete production hardening implementation, specialty canonicalization, google-genai migration, voice reliability, sqlite concurrency, and test suite consolidation

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

@@ -8,7 +8,7 @@ class GeminiClient:
 
     def __init__(self):
         self.api_key = GEMINI_API_KEY
-        self.model = GEMINI_MODEL
+        self.model = GEMINI_MODEL or "gemini-1.5-flash"
 
     async def extract_from_document(
         self,
@@ -30,9 +30,10 @@ class GeminiClient:
             }
 
         try:
-            import google.generativeai as genai
-            genai.configure(api_key=self.api_key)
-            model = genai.GenerativeModel(self.model or "gemini-1.5-flash")
+            from google import genai
+            from google.genai import types
+
+            client = genai.Client(api_key=self.api_key)
 
             prompt = f"""
 You are a medical document extraction AI.
@@ -56,12 +57,12 @@ Return ONLY JSON:
     "summary": "short clinical summary of the document"
 }}
 """
-            part = {
-                "mime_type": mime_type,
-                "data": file_bytes,
-            }
+            part = types.Part.from_bytes(data=file_bytes, mime_type=mime_type)
             response = await asyncio.wait_for(
-                model.generate_content_async([part, prompt]),
+                client.aio.models.generate_content(
+                    model=self.model,
+                    contents=[part, prompt]
+                ),
                 timeout=4.0
             )
             cleaned_text = response.text.replace("```json", "").replace("```", "").strip()
@@ -84,15 +85,18 @@ Return ONLY JSON:
         if not self.api_key or self.api_key in ["your_gemini_key", "your_gemini_api_key", ""]:
             raise ValueError("Gemini API key not configured")
 
-        import google.generativeai as genai
-        genai.configure(api_key=self.api_key)
-        model = genai.GenerativeModel(
-            model_name=self.model or "gemini-1.5-flash",
-            generation_config={"response_mime_type": "application/json"},
-        )
+        from google import genai
+        from google.genai import types
+
+        client = genai.Client(api_key=self.api_key)
         full_prompt = f"{system_prompt}\n\nPATIENT SAID: {user_message}"
+        config = types.GenerateContentConfig(response_mime_type="application/json")
         response = await asyncio.wait_for(
-            model.generate_content_async(full_prompt),
+            client.aio.models.generate_content(
+                model=self.model,
+                contents=full_prompt,
+                config=config,
+            ),
             timeout=3.5
         )
         return response.text