Initial commit

rest_sse_service.py

@@ -0,0 +1,73 @@
+import asyncio
+import json
+import time
+import logging
+from fastapi import FastAPI, Request
+from sse_starlette.sse import EventSourceResponse
+from pydantic import BaseModel
+import uvicorn
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("RestSseService")
+
+app = FastAPI(title="AURA Geolocalization API")
+
+# Shared asynchronous event queue for SSE broadcasting
+pose_event_queue = asyncio.Queue()
+
+class UserInputFix(BaseModel):
+    frame_id: int
+    lat: float
+    lon: float
+
+@app.post("/api/v1/start")
+async def start_pipeline():
+    """Starts the ingestion and processing pipeline."""
+    logger.info("Pipeline started via REST API.")
+    # Integration trigger to start the main AI loop goes here
+    return {"status": "SUCCESS", "message": "Processing pipeline initialized."}
+
+@app.post("/api/v1/user_fix")
+async def user_fix(fix: UserInputFix):
+    """Accepts user input for location when system is absolutely incapable (AC-6)."""
+    logger.info(f"Received user fix for frame {fix.frame_id}: Lat {fix.lat}, Lon {fix.lon}")
+    # Pass this hard geodetic constraint to the Factor Graph Optimizer
+    return {"status": "SUCCESS", "message": f"User fix applied for frame {fix.frame_id}."}
+
+@app.get("/api/v1/stream")
+async def stream_poses(request: Request):
+    """
+    Server-Sent Events (SSE) endpoint for real-time streaming of poses.
+    Fulfills the requirement for immediate results and refined results.
+    """
+    async def event_generator():
+        while True:
+            if await request.is_disconnected():
+                logger.info("Client disconnected from SSE stream.")
+                break
+            
+            # Wait for the next pose event from the processing engine
+            pose_data = await pose_event_queue.get()
+            yield {
+                "event": "pose_update",
+                "id": str(pose_data.get("frame_id")),
+                "retry": 15000,
+                "data": json.dumps(pose_data)
+            }
+
+    return EventSourceResponse(event_generator())
+
+async def publish_pose_to_stream(frame_id: int, lat: float, lon: float, confidence: float, is_refined: bool = False):
+    """Helper function to push a new pose into the SSE queue from the Optimizer."""
+    payload = {
+        "frame_id": frame_id,
+        "gps": [lat, lon],
+        "confidence": confidence,
+        "type": "REFINED" if is_refined else "ESTIMATED",
+        "timestamp": time.time()
+    }
+    await pose_event_queue.put(payload)
+
+if __name__ == "__main__":
+    # Run the background service (typically deployed via a worker process)
+    uvicorn.run(app, host="0.0.0.0", port=8000)