[DEVX-896]: Update Model Predict CLI

clarifai/cli/model.py

@@ -173,8 +173,6 @@ def run_locally(model_path, port, mode, keep_env, keep_image):
 @click.option('--file_path', required=False, help='File path of file for the model to predict')
 @click.option('--url', required=False, help='URL to the file for the model to predict')
 @click.option('--bytes', required=False, help='Bytes to the file for the model to predict')
-@click.option(
-    '--input_id', required=False, help='Existing input id in the app for the model to predict')
 @click.option('--input_type', required=False, help='Type of input')
 @click.option(
     '-cc_id',
@@ -188,36 +186,28 @@ def run_locally(model_path, port, mode, keep_env, keep_image):
     '--inference_params', required=False, default='{}', help='Inference parameters to override')
 @click.option('--output_config', required=False, default='{}', help='Output config to override')
 @click.pass_context
-def predict(ctx, config, model_id, user_id, app_id, model_url, file_path, url, bytes, input_id,
-            input_type, compute_cluster_id, nodepool_id, deployment_id, inference_params,
-            output_config):
+def predict(ctx, config, model_id, user_id, app_id, model_url, file_path, url, bytes, input_type,
+            compute_cluster_id, nodepool_id, deployment_id, inference_params, output_config):
   """Predict using the given model"""
   import json
 
-  from clarifai.client.deployment import Deployment
-  from clarifai.client.input import Input
   from clarifai.client.model import Model
-  from clarifai.client.nodepool import Nodepool
   from clarifai.utils.cli import from_yaml
   if config:
     config = from_yaml(config)
-    model_id, user_id, app_id, model_url, file_path, url, bytes, input_id, input_type, compute_cluster_id, nodepool_id, deployment_id, inference_params, output_config = (
+    model_id, user_id, app_id, model_url, file_path, url, bytes, input_type, compute_cluster_id, nodepool_id, deployment_id, inference_params, output_config = (
         config.get(k, v)
         for k, v in [('model_id', model_id), ('user_id', user_id), ('app_id', app_id), (
             'model_url', model_url), ('file_path', file_path), ('url', url), ('bytes', bytes), (
-                'input_id',
-                input_id), ('input_type',
-                            input_type), ('compute_cluster_id',
-                                          compute_cluster_id), ('nodepool_id', nodepool_id), (
-                                              'deployment_id',
-                                              deployment_id), ('inference_params',
-                                                               inference_params), ('output_config',
-                                                                                   output_config)])
+                'input_type', input_type), ('compute_cluster_id', compute_cluster_id), (
+                    'nodepool_id',
+                    nodepool_id), ('deployment_id',
+                                   deployment_id), ('inference_params',
+                                                    inference_params), ('output_config',
+                                                                        output_config)])
   if sum([opt[1] for opt in [(model_id, 1), (user_id, 1), (app_id, 1), (model_url, 3)]
           if opt[0]]) != 3:
     raise ValueError("Either --model_id & --user_id & --app_id or --model_url must be provided.")
-  if sum([1 for opt in [file_path, url, bytes, input_id] if opt]) != 1:
-    raise ValueError("Exactly one of --file_path, --url, --bytes or --input_id must be provided.")
   if compute_cluster_id or nodepool_id or deployment_id:
     if sum([
         opt[1] for opt in [(compute_cluster_id, 0.5), (nodepool_id, 0.5), (deployment_id, 1)]
@@ -267,21 +257,5 @@ def predict(ctx, config, model_id, user_id, app_id, model_url, file_path, url, b
         nodepool_id=nodepool_id,
         deployment_id=deployment_id,
         inference_params=inference_params,
-        output_config=output_config)
-  elif input_id:
-    inputs = [Input.get_input(input_id)]
-    runner_selector = None
-    if deployment_id:
-      runner_selector = Deployment.get_runner_selector(
-          user_id=ctx.obj['user_id'], deployment_id=deployment_id)
-    elif compute_cluster_id and nodepool_id:
-      runner_selector = Nodepool.get_runner_selector(
-          user_id=ctx.obj['user_id'],
-          compute_cluster_id=compute_cluster_id,
-          nodepool_id=nodepool_id)
-    model_prediction = model.predict(
-        inputs=inputs,
-        runner_selector=runner_selector,
-        inference_params=inference_params,
-        output_config=output_config)
+        output_config=output_config)  ## TO DO: Add support for input_id
   click.echo(model_prediction)

clarifai/client/input.py

@@ -572,6 +572,24 @@ def get_mask_proto(input_id: str,
 
     return input_mask_proto
 
+  def get_input(self, input_id: str) -> Input:
+    """Get Input object of input with input_id provided from the app.
+
+    Args:
+        input_id (str): The input ID for the annotation to get.
+
+    Returns:
+        Input: An Input object for the specified input ID.
+
+    Example:
+        >>> from clarifai.client.input import Inputs
+        >>> input_obj = Inputs(user_id = 'user_id', app_id = 'demo_app')
+        >>> input_obj.get_input(input_id='demo')
+    """
+    request = service_pb2.GetInputRequest(user_app_id=self.user_app_id, input_id=input_id)
+    response = self._grpc_request(self.STUB.GetInput, request)
+    return response.input
+
   def upload_from_url(self,
                       input_id: str,
                       image_url: str = None,

tests/compute_orchestration/configs/example_deployment_config.yaml

@@ -10,11 +10,11 @@ deployment:
     disable_packing: false
   worker:
     model:
-      id: "apparel-clusterering"
+      id: "python_string_cat"
       model_version:
-        id: "cc911f6b0ed748efb89e3d1359c146c4"
+        id: "b7038e059a0c4ddca29c22aec561824d"
       user_id: "clarifai"
-      app_id: "main"
+      app_id: "Test-Model-Upload"
   scheduling_choice: 4
   nodepools:
     - id: "test-nodepool-6"

tests/runners/hf_mbart_model/config.yaml

@@ -17,4 +17,3 @@ inference_compute_info:
 checkpoints:
   type: "huggingface"
   repo_id: "sshleifer/tiny-mbart"
-  hf_token: ""

tests/test_data_upload.py

@@ -137,7 +137,7 @@ def test_upload_text_bytes(self, caplog):
       self.input_object.upload_from_bytes(input_id='input_13', text_bytes=text_bytes)
       assert "SUCCESS" in caplog.text
 
-  def test_get_multimodal_input(self, caplog):
+  def test_get_multimodal_input(self):
     input_object = self.input_object.get_multimodal_input(
         input_id='input_14', raw_text='This is a multimodal test text', image_url=IMAGE_URL)
     assert input_object.id == 'input_14' and input_object.data.text.raw == 'This is a multimodal test text'
@@ -146,6 +146,10 @@ def test_get_text_inputs_from_folder(self):
     text_inputs = self.input_object.get_text_inputs_from_folder(TEXTS_FOLDER_PATH)
     assert len(text_inputs) == 3
 
+  def test_get_input_from_app(self):
+    input_object = self.input_object.get_input(input_id='input_1')
+    assert input_object.id == 'input_1'
+
   def test_get_mask_proto(self):
     polygon_points = [[.2, .2], [.8, .2], [.8, .8], [.2, .8]]
     annotation = self.input_object.get_mask_proto(