code: def load_from_pipeline(pipeline: pipelines.base.Pipeline) -> dict:
      """
      Gets the appropriate Interface kwargs for a given Hugging Face transformers.Pipeline.
      pipeline (transformers.Pipeline): the transformers.Pipeline from which to create an interface
      Returns:
      (dict): a dictionary of kwargs that can be used to construct an Interface object
      """
      try:
          import transformers
          from transformers import pipelines
      except ImportError as ie:
          raise ImportError(
              "transformers not installed. Please try `pip install transformers`"
          ) from ie
      if not isinstance(pipeline, pipelines.base.Pipeline):
          raise ValueError("pipeline must be a transformers.Pipeline")
  
      # Handle the different pipelines. The has_attr() checks to make sure the pipeline exists in the
      # version of the transformers library that the user has installed.
      if hasattr(
          transformers, "AudioClassificationPipeline"
      ) and isinstance(
          pipeline,
          pipelines.audio_classification.AudioClassificationPipeline,
      ):
          pipeline_info = {
              "inputs": components.Audio(
                  sources=["microphone"],
                  type="filepath",
                  label="Input",
                  render=False,
              ),
              "outputs": components.Label(label="Class", render=False),
              "preprocess": lambda i: {"inputs": i},
              "postprocess": lambda r: {
                  i["label"].split(", ")[0]: i["score"] for i in r
              },
          }
      elif hasattr(
          transformers, "AutomaticSpeechRecognitionPipeline"
      ) and isinstance(
          pipeline,
          pipelines.automatic_speech_recognition.AutomaticSpeechRecognitionPipeline,
      ):
          pipeline_info = {
              "inputs": components.Audio(
                  sources=["microphone"],
                  type="filepath",
                  label="Input",
                  render=False,
              ),
              "outputs": components.Textbox(
                  label="Output", render=False
              ),
              "preprocess": lambda i: {"inputs": i},
              "postprocess": lambda r: r["text"],
          }
      elif hasattr(
          transformers, "FeatureExtractionPipeline"
      ) and isinstance(
          pipeline,
          pipelines.feature_extraction.FeatureExtractionPipeline,
      ):
          pipeline_info = {
              "inputs": components.Textbox(label="Input", render=False),
              "outputs": components.Dataframe(
                  label="Output", render=False
              ),
              "preprocess": lambda x: {"inputs": x},
              "postprocess": lambda r: r[0],
          }
      elif hasattr(transformers, "FillMaskPipeline") and isinstance(
          pipeline, pipelines.fill_mask.FillMaskPipeline
      ):
          pipeline_info = {
              "inputs": components.Textbox(label="Input", render=False),
              "outputs": components.Label(
                  label="Classification", render=False
              ),
              "preprocess": lambda x: {"inputs": x},
              "postprocess": lambda r: {
                  i["token_str"]: i["score"] for i in r
              },
          }
      elif hasattr(
          transformers, "ImageClassificationPipeline"
      ) and isinstance(
          pipeline,
          pipelines.image_classification.ImageClassificationPipeline,
      ):
          pipeline_info = {
              "inputs": components.Image(
                  type="filepath", label="Input Image", render=False
              ),
              "outputs": components.Label(
                  label="Classification", render=False
              ),
              "preprocess": lambda i: {"images": i},
              "postprocess": lambda r: {
                  i["label"].split(", ")[0]: i["score"] for i in r
              },
          }
      elif hasattr(
          transformers, "QuestionAnsweringPipeline"
      ) and isinstance(
          pipeline,
          pipelines.question_answering.QuestionAnsweringPipeline,
      ):
          pipeline_info = {
              "inputs": [
                  components.Textbox(
                      lines=7, label="Context", render=False
                  ),
                  components.Textbox(label="Question", render=False),
              ],
              "outputs": [
                  components.Textbox(label="Answer", render=False),
                  components.Label(label="Score", render=False),
              ],
              "preprocess": lambda c, q: {"context": c, "question": q},
              "postprocess": lambda r: (r["answer"], r["score"]),
          }
      elif hasattr(
          transformers, "SummarizationPipeline"
      ) and isinstance(
          pipeline, pipelines.text2text_generation.SummarizationPipeline
      ):
          pipeline_info = {
              "inputs": components.Textbox(
                  lines=7, label="Input", render=False
              ),
              "outputs": components.Textbox(
                  label="Summary", render=False
              ),
              "preprocess": lambda x: {"inputs": x},
              "postprocess": lambda r: r[0]["summary_text"],
          }
      elif hasattr(
          transformers, "TextClassificationPipeline"
      ) and isinstance(
          pipeline,
          pipelines.text_classification.TextClassificationPipeline,
      ):
          pipeline_info = {
              "inputs": components.Textbox(label="Input", render=False),
              "outputs": components.Label(
                  label="Classification", render=False
              ),
              "preprocess": lambda x: [x],
              "postprocess": lambda r: {
                  i["label"].split(", ")[0]: i["score"] for i in r
              },
          }
      elif hasattr(
          transformers, "TextGenerationPipeline"
      ) and isinstance(
          pipeline, pipelines.text_generation.TextGenerationPipeline
      ):
          pipeline_info = {
              "inputs": components.Textbox(label="Input", render=False),
              "outputs": components.Textbox(
                  label="Output", render=False
              ),
              "preprocess": lambda x: {"text_inputs": x},
              "postprocess": lambda r: r[0]["generated_text"],
          }
      elif hasattr(transformers, "TranslationPipeline") and isinstance(
          pipeline, pipelines.text2text_generation.TranslationPipeline
      ):
          pipeline_info = {
              "inputs": components.Textbox(label="Input", render=False),
              "outputs": components.Textbox(
                  label="Translation", render=False
              ),
              "preprocess": lambda x: [x],
              "postprocess": lambda r: r[0]["translation_text"],
          }
      elif hasattr(
          transformers, "Text2TextGenerationPipeline"
      ) and isinstance(
          pipeline,
          pipelines.text2text_generation.Text2TextGenerationPipeline,
      ):
          pipeline_info = {
              "inputs": components.Textbox(label="Input", render=False),
              "outputs": components.Textbox(
                  label="Generated Text", render=False
              ),
              "preprocess": lambda x: [x],
              "postprocess": lambda r: r[0]["generated_text"],
          }
      elif hasattr(
          transformers, "ZeroShotClassificationPipeline"
      ) and isinstance(
          pipeline,
          pipelines.zero_shot_classification.ZeroShotClassificationPipeline,
      ):
          pipeline_info = {
              "inputs": [
                  components.Textbox(label="Input", render=False),
                  components.Textbox(
                      label="Possible class names (" "comma-separated)",
                      render=False,
                  ),
                  components.Checkbox(
                      label="Allow multiple true classes", render=False
                  ),
              ],
              "outputs": components.Label(
                  label="Classification", render=False
              ),
              "preprocess": lambda i, c, m: {
                  "sequences": i,
                  "candidate_labels": c,
                  "multi_label": m,
              },
              "postprocess": lambda r: {
                  r["labels"][i]: r["scores"][i]
                  for i in range(len(r["labels"]))
              },
          }
      elif hasattr(
          transformers, "DocumentQuestionAnsweringPipeline"
      ) and isinstance(
          pipeline,
          pipelines.document_question_answering.DocumentQuestionAnsweringPipeline,  # type: ignore
      ):
          pipeline_info = {
              "inputs": [
                  components.Image(
                      type="filepath",
                      label="Input Document",
                      render=False,
                  ),
                  components.Textbox(label="Question", render=False),
              ],
              "outputs": components.Label(label="Label", render=False),
              "preprocess": lambda img, q: {
                  "image": img,
                  "question": q,
              },
              "postprocess": lambda r: {
                  i["answer"]: i["score"] for i in r
              },
          }
      elif hasattr(
          transformers, "VisualQuestionAnsweringPipeline"
      ) and isinstance(
          pipeline,
          pipelines.visual_question_answering.VisualQuestionAnsweringPipeline,
      ):
          pipeline_info = {
              "inputs": [
                  components.Image(
                      type="filepath", label="Input Image", render=False
                  ),
                  components.Textbox(label="Question", render=False),
              ],
              "outputs": components.Label(label="Score", render=False),
              "preprocess": lambda img, q: {
                  "image": img,
                  "question": q,
              },
              "postprocess": lambda r: {
                  i["answer"]: i["score"] for i in r
              },
          }
      elif hasattr(transformers, "ImageToTextPipeline") and isinstance(
          pipeline,
          pipelines.image_to_text.ImageToTextPipeline,  # type: ignore
      ):
          pipeline_info = {
              "inputs": components.Image(
                  type="filepath", label="Input Image", render=False
              ),
              "outputs": components.Textbox(label="Text", render=False),
              "preprocess": lambda i: {"images": i},
              "postprocess": lambda r: r[0]["generated_text"],
          }
      elif hasattr(
          transformers, "ObjectDetectionPipeline"
      ) and isinstance(
          pipeline, pipelines.object_detection.ObjectDetectionPipeline
      ):
          pipeline_info = {
              "inputs": components.Image(
                  type="filepath", label="Input Image", render=False
              ),
              "outputs": components.AnnotatedImage(
                  label="Objects Detected", render=False
              ),
              "preprocess": lambda i: {"inputs": i},
              "postprocess": lambda r, img: (
                  img,
                  [
                      (
                          (
                              i["box"]["xmin"],
                              i["box"]["ymin"],
                              i["box"]["xmax"],
                              i["box"]["ymax"],
                          ),
                          i["label"],
                      )
                      for i in r
                  ],
              ),
          }
      else:
          raise ValueError(
              f"Unsupported pipeline type: {type(pipeline)}"
          )
  
      # define the function that will be called by the Interface
      def fn(*params):
          data = pipeline_info["preprocess"](*params)
          # special cases that needs to be handled differently
          if isinstance(
              pipeline,
              (
                  pipelines.text_classification.TextClassificationPipeline,
                  pipelines.text2text_generation.Text2TextGenerationPipeline,
                  pipelines.text2text_generation.TranslationPipeline,
              ),
          ):
              data = pipeline(*data)
          else:
              data = pipeline(**data)
          # special case for object-detection
          # original input image sent to postprocess function
          if isinstance(
              pipeline,
              pipelines.object_detection.ObjectDetectionPipeline,
          ):
              output = pipeline_info["postprocess"](data, params[0])
          else:
              output = pipeline_info["postprocess"](data)
          return output
  
      interface_info = pipeline_info.copy()
      interface_info["fn"] = fn
      del interface_info["preprocess"]
      del interface_info["postprocess"]
  
      # define the title/description of the Interface
      interface_info["title"] = pipeline.model.__class__.__name__
  
      return interface_info
  
label_desc: Use image node_id 1 for calling a global function with example usage: load_from_pipeline(pipeline) and returns: interface_info, output