async def plot_curves(class_id: int = Form(...), model_name: str = Form(None)):
model_op_obj = Model_output(model_path=model_name,
first_time=False,
is_plot=True)
path1, path2, path3 = model_op_obj.plot_curves(class_id)
return {
"precision_vs_recall_path": SERVER_BASE_URL + path1,
"precision_recall_vs_confidence_path": SERVER_BASE_URL + path2,
"roc_curve_path": SERVER_BASE_URL + path3,
}
async def get_most_confused_classes(model_name: str = Form(...),
no_most: Optional[int] = Form(None)):
model_op_obj = Model_output(model_path=model_name,
first_time=False,
is_plot=False,
is_run=False,
is_most_conf_classes=True)
return {
"most_confused_classes":
model_op_obj.most_confused_classes(no_most=no_most)
}
async def generate_heatmap(
model_name: str = Form(...),
image: Optional[UploadFile] = File(None),
img_path: Optional[str] = Form(None),
):
model_op_obj = Model_output(model_path=model_name,
first_time=False,
is_plot=False,
is_run=True)
if img_path:
image = cv2.imread(img_path.split(SERVER_BASE_URL)[1])
else:
image = load_image_into_numpy_array(await image.read())
path = model_op_obj.generate_heatmap(image)
return {"path": SERVER_BASE_URL + path}
async def test_model(
model_name: str = Form(...),
image: Optional[UploadFile] = File(None),
img_path: Optional[str] = Form(None),
):
model_op_obj = Model_output(model_path=model_name,
first_time=False,
is_plot=False,
is_run=True)
if img_path:
image = cv2.imread(img_path.split(SERVER_BASE_URL)[1])
else:
image = load_image_into_numpy_array(await image.read())
onx, ony = model_op_obj.test_model(image)
return {"x": onx, "y": ony}
async def pred_model_output(model: Optional[UploadFile] = File(None),
model_name: Optional[str] = Form(None)):
first_time = model_name is None
if first_time:
if model is None:
raise HTTPException(status_code=400,
detail="Model has to be uploaded or selected")
model_name = str(uuid.uuid1()) + "_" + model.filename
with open("models/" + model_name, "wb") as buffer:
shutil.copyfileobj(model.file, buffer)
model_op_obj = Model_output(model_name, first_time)
output = {}
if first_time:
output = {"model_name": model_name}
train_metrics, test_metrics = model_op_obj.get_metrics()
output["train_metrics"] = train_metrics
output["test_metrics"] = test_metrics
output["top_5_classes"] = model_op_obj.top_5_classes(SERVER_BASE_URL +
"test_dataset/")
output["wrong_pred"] = model_op_obj.wrong_pred()
output["confusion_matrix_path"] = SERVER_BASE_URL + model_op_obj.confusion(
)
x, y = model_op_obj.worst_acc_classes()
output["wrost_acc_classes"] = {"x": x, "y": y}
output["most_confused_classes"] = model_op_obj.most_confused_classes()
output["conf_matrix"] = model_op_obj.get_conf_matrix()
return output