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| """This module should not be used directly as its API is subject to change. Instead, | |
| please use the `gr.Interface.from_pipeline()` function.""" | |
| from __future__ import annotations | |
| from typing import TYPE_CHECKING | |
| from gradio import components | |
| if TYPE_CHECKING: # Only import for type checking (is False at runtime). | |
| from transformers import pipelines | |
| 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( | |
| source="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( | |
| source="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"], | |
| } | |
| 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) | |
| 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 | |