Agilizar respuestas

Dockerfile

@@ -37,4 +37,4 @@ COPY --chown=user . .
 EXPOSE 7860
 
 # 7. Arranque optimizado para 16GB de RAM (4 workers)
-CMD ["uvicorn", "src.app.app:app", "--host", "0.0.0.0", "--port", "7860", "--workers", "2"]
+CMD ["uvicorn", "src.app.app:app", "--host", "0.0.0.0", "--port", "7860", "--workers", "1"]

src/app/app.py

@@ -130,7 +130,7 @@ def detection_vef(image: UploadFile):
     debug_buffer = io.BytesIO() 
     imageFile.save(debug_buffer, format="JPEG") 
 
-    which_currency = models["INFERENCIA"].predict(imageFile, verbose=False, imgsz=416, conf=0.25)
+    which_currency = models["INFERENCIA"].predict(imageFile, verbose=False, imgsz=320, conf=0.25)
     
     if len(which_currency[0].boxes) == 0:
         return {"message": "No objects detected"}
@@ -142,7 +142,7 @@ def detection_vef(image: UploadFile):
     else:
         currency = "USD"
         
-    results = models[currency].predict(imageFile, verbose=False, imgsz=416, conf=0.25)
+    results = models[currency].predict(imageFile, verbose=False, imgsz=320, conf=0.25)
 
     if len(results[0].boxes) > 0:
         # Tomamos el label y la confianza de la primera detección (la de mayor confianza usualmente)