refactor(framework) Update NumPy template (#4292)

src/py/flwr/cli/new/new.py

@@ -240,6 +240,7 @@ def new(
             MlFramework.HUGGINGFACE.value,
             MlFramework.MLX.value,
             MlFramework.TENSORFLOW.value,
+            MlFramework.NUMPY.value,
         ]
         if framework_str in frameworks_with_tasks:
             files[f"{import_name}/task.py"] = {

src/py/flwr/cli/new/templates/app/code/client.numpy.py.tpl

@@ -1,16 +1,15 @@
 """$project_name: A Flower / $framework_str app."""
 
-from flwr.client import NumPyClient, ClientApp
+from flwr.client import ClientApp, NumPyClient
 from flwr.common import Context
-import numpy as np
+from $import_name.task import get_dummy_model
 
 
 class FlowerClient(NumPyClient):
-    def get_parameters(self, config):
-        return [np.ones((1, 1))]
 
     def fit(self, parameters, config):
-        return ([np.ones((1, 1))], 1, {})
+        model = get_dummy_model()
+        return [model], 1, {}
 
     def evaluate(self, parameters, config):
         return float(0.0), 1, {"accuracy": float(1.0)}

src/py/flwr/cli/new/templates/app/code/server.numpy.py.tpl

@@ -1,16 +1,21 @@
 """$project_name: A Flower / $framework_str app."""
 
-from flwr.common import Context
+from flwr.common import Context, ndarrays_to_parameters
 from flwr.server import ServerApp, ServerAppComponents, ServerConfig
 from flwr.server.strategy import FedAvg
+from $import_name.task import get_dummy_model
 
 
 def server_fn(context: Context):
     # Read from config
     num_rounds = context.run_config["num-server-rounds"]
 
+    # Initial model
+    model = get_dummy_model()
+    dummy_parameters = ndarrays_to_parameters([model])
+
     # Define strategy
-    strategy = FedAvg()
+    strategy = FedAvg(initial_parameters=dummy_parameters)
     config = ServerConfig(num_rounds=num_rounds)
 
     return ServerAppComponents(strategy=strategy, config=config)

src/py/flwr/cli/new/templates/app/code/task.numpy.py.tpl

@@ -0,0 +1,7 @@
+"""$project_name: A Flower / $framework_str app."""
+
+import numpy as np
+
+
+def get_dummy_model():
+    return np.ones((1, 1))