How to use the mlpm.app.aidserver.solver.infer function in mlpm

request_type, target_folder):
    config = ImmutableMultiDict(await request.form)
    data = config.to_dict()
    results = {}
    if 'file' in await request.files:
        uploaded_file = await request.files['file']
        filename = secure_filename(uploaded_file.filename)
        # make sure the UPLOAD_FOLDER exsits
        if not os.path.isdir(upload_folder):
            os.makedirs(upload_folder)
        file_abs_path = os.path.join(upload_folder, filename)
        uploaded_file.save(file_abs_path)
        data['input_file_path'] = file_abs_path
    try:
        if request_type == "infer":
            results = aidserver.solver.infer(data)
        else:
            raise NotImplementedError
        if 'delete_after_process' in data:
            if str2bool(data['delete_after_process']):
                os.remove(file_abs_path)
        print(results)
        return json_resp(results, status=200)
    except Exception as e:
        traceback.print_exc()
        return json_resp({"error": str(e), "code": "500"}, status=500)

async def handle_batch_infer_request(request, upload_folder, target_folder):
    if 'file' in await request.files:
        uploaded_file = await request.files['file']
        filename = secure_filename(uploaded_file.filename)
        if not os.path.isdir(upload_folder):
            os.makedirs(upload_folder)
        file_abs_path = os.path.join(upload_folder, filename)
        uploaded_file.save(file_abs_path)
    try:
        with open(file_abs_path, 'r') as file_obj:
            csv_dict = list(DictReader(file_obj))
            results = [aidserver.solver.infer(row) for row in csv_dict]
            # merging results
            output = []
            for index, each in enumerate(csv_dict):
                each.update(results[index])
                output.append(each)
        if not os.path.isdir(target_folder):
            os.makedirs(target_folder)
        file_identifier = str(uuid.uuid4())
        output_file_path = os.path.join(target_folder,
                                        file_identifier + ".csv")
        head = output[0].keys()
        with open(output_file_path, 'w') as file_obj:
            writer = DictWriter(file_obj, fieldnames=head)
            writer.writeheader()
            for each in output:
                writer.writerow(each)

def handle_post_solver_train_or_infer(request, upload_folder, request_type):
    config = ImmutableMultiDict(request.form)
    data = config.to_dict()
    results = {}
    if 'file' in request.files:
        file = request.files['file']
        filename = secure_filename(file.filename)
        # make sure the UPLOAD_FOLDER exsits
        if not os.path.isdir(upload_folder):
            os.makedirs(upload_folder)
        file_abs_path = os.path.join(upload_folder, filename)
        file.save(file_abs_path)
        data['input_file_path'] = file_abs_path
    try:
        if request_type == "infer":
            results = aidserver.solver.infer(data)
        elif request_type == "train":
            results = aidserver.solver.train(data)
        else:
            raise NotImplementedError
        if 'delete_after_process' in data:
            if str2bool(data['delete_after_process']):
                os.remove(file_abs_path)
        print(results)
        return response.json(results, status=200)
    except Exception as e:
        traceback.print_exc()
        return response.json({"error": str(e), "code": "500"}, status=500)