Update app.py

app.py

@@ -17,49 +17,31 @@ async def root():
 
 @app.post("/predict")
 async def predict(file: UploadFile = File(...)):
-    # Save temp file
     temp_file = Path(f"/tmp/{uuid.uuid4()}_{file.filename}")
     with temp_file.open("wb") as buffer:
         shutil.copyfileobj(file.file, buffer)
 
-    # Run inference
     results = model.predict(source=str(temp_file), save=False, conf=0.5)
 
-    # Collect detections (bounding boxes)
     detections = []
-    frame_level = {"healthy": 0, "yellow_disease": 0}
+    disease_found = False
 
     for r in results:
         for box in r.boxes:
             cls_name = model.names[int(box.cls)]
             conf = float(box.conf)
-            frame_level[cls_name] += 1  # count detections
-
             detections.append({
                 "class": cls_name,
                 "confidence": conf,
-                "bbox": [float(v) for v in box.xyxy[0].tolist()]  # [x1,y1,x2,y2]
+                "bbox": [float(v) for v in box.xyxy[0].tolist()]
             })
+            if cls_name == "yellow_disease":
+                disease_found = True
 
-    # Decide frame-level classification
-    frame_class = "yellow_disease" if frame_level["yellow_disease"] > 0 else "healthy"
-
-    # Optional: encode image to base64 (so you can preview annotated frame in Postman if needed)
-    # r.save_dir contains annotated images if save=True
-    img_b64 = None
-    try:
-        import cv2, base64
-        annotated = results[0].plot()  # numpy array (BGR)
-        _, buf = cv2.imencode(".jpg", annotated)
-        img_b64 = base64.b64encode(buf).decode("utf-8")
-    except Exception:
-        pass
-
-    # Clean up
     temp_file.unlink(missing_ok=True)
 
     return {
-        "frame_classification": frame_class,  # overall yes/no for spraying
-        "detections": detections,             # bounding boxes if nozzle precision needed
-        "image_base64": img_b64               # optional preview (can remove if not needed)
+        "spray_signal": 1 if disease_found else 0,   # ESC reads this
+        "frame_classification": "yellow_disease" if disease_found else "healthy",
+        "detections": detections                     # keep for logs/debugging
     }