feat: AI patient onboarding backend and documentation

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

@@ -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.",
+    )