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"""
Defines internal helper methods for handling transformers and diffusers pipelines.
These are used by load_from_pipeline method in pipelines.py.
"""
from typing import Any, Dict, Optional
import numpy as np
from PIL import Image
from gradio import components
def handle_transformers_pipeline(pipeline: Any) -> Optional[Dict[str, Any]]:
try:
import transformers
except ImportError as ie:
raise ImportError(
"transformers not installed. Please try `pip install transformers`"
) from ie
def is_transformers_pipeline_type(pipeline, class_name: str):
cls = getattr(transformers, class_name, None)
return cls and isinstance(pipeline, cls)
# 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 is_transformers_pipeline_type(pipeline, "AudioClassificationPipeline"):
return {
"inputs": components.Audio(type="filepath", label="Input", render=False),
"outputs": components.Label(label="Class", render=False),
"preprocess": lambda i: {"inputs": i},
"postprocess": lambda r: {i["label"]: i["score"] for i in r},
}
if is_transformers_pipeline_type(pipeline, "AutomaticSpeechRecognitionPipeline"):
return {
"inputs": components.Audio(type="filepath", label="Input", render=False),
"outputs": components.Textbox(label="Output", render=False),
"preprocess": lambda i: {"inputs": i},
"postprocess": lambda r: r["text"],
}
if is_transformers_pipeline_type(pipeline, "FeatureExtractionPipeline"):
return {
"inputs": components.Textbox(label="Input", render=False),
"outputs": components.Dataframe(label="Output", render=False),
"preprocess": lambda x: {"inputs": x},
"postprocess": lambda r: r[0],
}
if is_transformers_pipeline_type(pipeline, "FillMaskPipeline"):
return {
"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},
}
if is_transformers_pipeline_type(pipeline, "ImageClassificationPipeline"):
return {
"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"]: i["score"] for i in r},
}
if is_transformers_pipeline_type(pipeline, "QuestionAnsweringPipeline"):
return {
"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"]),
}
if is_transformers_pipeline_type(pipeline, "SummarizationPipeline"):
return {
"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"],
}
if is_transformers_pipeline_type(pipeline, "TextClassificationPipeline"):
return {
"inputs": components.Textbox(label="Input", render=False),
"outputs": components.Label(label="Classification", render=False),
"preprocess": lambda x: [x],
"postprocess": lambda r: {i["label"]: i["score"] for i in r},
}
if is_transformers_pipeline_type(pipeline, "TextGenerationPipeline"):
return {
"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"],
}
if is_transformers_pipeline_type(pipeline, "TranslationPipeline"):
return {
"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"],
}
if is_transformers_pipeline_type(pipeline, "Text2TextGenerationPipeline"):
return {
"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"],
}
if is_transformers_pipeline_type(pipeline, "ZeroShotClassificationPipeline"):
return {
"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"]))
},
}
if is_transformers_pipeline_type(pipeline, "DocumentQuestionAnsweringPipeline"):
return {
"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},
}
if is_transformers_pipeline_type(pipeline, "VisualQuestionAnsweringPipeline"):
return {
"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},
}
if is_transformers_pipeline_type(pipeline, "ImageToTextPipeline"):
return {
"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"],
}
if is_transformers_pipeline_type(pipeline, "ObjectDetectionPipeline"):
return {
"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
],
),
}
raise ValueError(f"Unsupported transformers pipeline type: {type(pipeline)}")
def handle_diffusers_pipeline(pipeline: Any) -> Optional[Dict[str, Any]]:
try:
import diffusers
except ImportError as ie:
raise ImportError(
"diffusers not installed. Please try `pip install diffusers`"
) from ie
def is_diffusers_pipeline_type(pipeline, class_name: str):
cls = getattr(diffusers, class_name, None)
return cls and isinstance(pipeline, cls)
if is_diffusers_pipeline_type(pipeline, "StableDiffusionPipeline"):
return {
"inputs": [
components.Textbox(label="Prompt", render=False),
components.Textbox(label="Negative prompt", render=False),
components.Slider(
label="Number of inference steps",
minimum=1,
maximum=500,
value=50,
step=1,
),
components.Slider(
label="Guidance scale",
minimum=1,
maximum=20,
value=7.5,
step=0.5,
),
],
"outputs": components.Image(
label="Generated Image", render=False, type="pil"
),
"preprocess": lambda prompt, n_prompt, num_inf_steps, g_scale: {
"prompt": prompt,
"negative_prompt": n_prompt,
"num_inference_steps": num_inf_steps,
"guidance_scale": g_scale,
},
"postprocess": lambda r: r["images"][0],
}
if is_diffusers_pipeline_type(pipeline, "StableDiffusionImg2ImgPipeline"):
return {
"inputs": [
components.Textbox(label="Prompt", render=False),
components.Textbox(label="Negative prompt", render=False),
components.Image(type="filepath", label="Image", render=False),
components.Slider(
label="Strength", minimum=0, maximum=1, value=0.8, step=0.1
),
components.Slider(
label="Number of inference steps",
minimum=1,
maximum=500,
value=50,
step=1,
),
components.Slider(
label="Guidance scale",
minimum=1,
maximum=20,
value=7.5,
step=0.5,
),
],
"outputs": components.Image(
label="Generated Image", render=False, type="pil"
),
"preprocess": lambda prompt,
n_prompt,
image,
strength,
num_inf_steps,
g_scale: {
"prompt": prompt,
"image": Image.open(image).resize((768, 768)),
"negative_prompt": n_prompt,
"num_inference_steps": num_inf_steps,
"guidance_scale": g_scale,
"strength": strength,
},
"postprocess": lambda r: r["images"][0],
}
if is_diffusers_pipeline_type(pipeline, "StableDiffusionInpaintPipeline"):
return {
"inputs": [
components.Textbox(label="Prompt", render=False),
components.Textbox(label="Negative prompt", render=False),
components.Image(type="filepath", label="Image", render=False),
components.Image(type="filepath", label="Mask Image", render=False),
components.Slider(
label="Strength", minimum=0, maximum=1, value=0.8, step=0.1
),
components.Slider(
label="Number of inference steps",
minimum=1,
maximum=500,
value=50,
step=1,
),
components.Slider(
label="Guidance scale",
minimum=1,
maximum=20,
value=7.5,
step=0.5,
),
],
"outputs": components.Image(
label="Generated Image", render=False, type="pil"
),
"preprocess": lambda prompt,
n_prompt,
image,
mask_image,
strength,
num_inf_steps,
g_scale: {
"prompt": prompt,
"image": Image.open(image).resize((768, 768)),
"mask_image": Image.open(mask_image).resize((768, 768)),
"negative_prompt": n_prompt,
"num_inference_steps": num_inf_steps,
"guidance_scale": g_scale,
"strength": strength,
},
"postprocess": lambda r: r["images"][0],
}
if is_diffusers_pipeline_type(pipeline, "StableDiffusionDepth2ImgPipeline"):
return {
"inputs": [
components.Textbox(label="Prompt", render=False),
components.Textbox(label="Negative prompt", render=False),
components.Image(type="filepath", label="Image", render=False),
components.Slider(
label="Strength", minimum=0, maximum=1, value=0.8, step=0.1
),
components.Slider(
label="Number of inference steps",
minimum=1,
maximum=500,
value=50,
step=1,
),
components.Slider(
label="Guidance scale",
minimum=1,
maximum=20,
value=7.5,
step=0.5,
),
],
"outputs": components.Image(
label="Generated Image", render=False, type="pil"
),
"preprocess": lambda prompt,
n_prompt,
image,
strength,
num_inf_steps,
g_scale: {
"prompt": prompt,
"image": Image.open(image).resize((768, 768)),
"negative_prompt": n_prompt,
"num_inference_steps": num_inf_steps,
"guidance_scale": g_scale,
"strength": strength,
},
"postprocess": lambda r: r["images"][0],
}
if is_diffusers_pipeline_type(pipeline, "StableDiffusionImageVariationPipeline"):
return {
"inputs": [
components.Image(type="filepath", label="Image", render=False),
components.Slider(
label="Number of inference steps",
minimum=1,
maximum=500,
value=50,
step=1,
),
components.Slider(
label="Guidance scale",
minimum=1,
maximum=20,
value=7.5,
step=0.5,
),
],
"outputs": components.Image(
label="Generated Image", render=False, type="pil"
),
"preprocess": lambda image, num_inf_steps, g_scale: {
"image": Image.open(image).resize((768, 768)),
"num_inference_steps": num_inf_steps,
"guidance_scale": g_scale,
},
"postprocess": lambda r: r["images"][0],
}
if is_diffusers_pipeline_type(pipeline, "StableDiffusionInstructPix2PixPipeline"):
return {
"inputs": [
components.Textbox(label="Prompt", render=False),
components.Textbox(label="Negative prompt", render=False),
components.Image(type="filepath", label="Image", render=False),
components.Slider(
label="Number of inference steps",
minimum=1,
maximum=500,
value=50,
step=1,
),
components.Slider(
label="Guidance scale",
minimum=1,
maximum=20,
value=7.5,
step=0.5,
),
components.Slider(
label="Image Guidance scale",
minimum=1,
maximum=5,
value=1.5,
step=0.5,
),
],
"outputs": components.Image(
label="Generated Image", render=False, type="pil"
),
"preprocess": lambda prompt,
n_prompt,
image,
num_inf_steps,
g_scale,
img_g_scale: {
"prompt": prompt,
"image": Image.open(image).resize((768, 768)),
"negative_prompt": n_prompt,
"num_inference_steps": num_inf_steps,
"guidance_scale": g_scale,
"image_guidance_scale": img_g_scale,
},
"postprocess": lambda r: r["images"][0],
}
if is_diffusers_pipeline_type(pipeline, "StableDiffusionUpscalePipeline"):
return {
"inputs": [
components.Textbox(label="Prompt", render=False),
components.Textbox(label="Negative prompt", render=False),
components.Image(type="filepath", label="Image", render=False),
components.Slider(
label="Number of inference steps",
minimum=1,
maximum=500,
value=50,
step=1,
),
components.Slider(
label="Guidance scale",
minimum=1,
maximum=20,
value=7.5,
step=0.5,
),
components.Slider(
label="Noise level", minimum=1, maximum=100, value=20, step=1
),
],
"outputs": components.Image(
label="Generated Image", render=False, type="pil"
),
"preprocess": lambda prompt,
n_prompt,
image,
num_inf_steps,
g_scale,
noise_level: {
"prompt": prompt,
"image": Image.open(image).resize((768, 768)),
"negative_prompt": n_prompt,
"num_inference_steps": num_inf_steps,
"guidance_scale": g_scale,
"noise_level": noise_level,
},
"postprocess": lambda r: r["images"][0],
}
raise ValueError(f"Unsupported diffusers pipeline type: {type(pipeline)}")
def handle_transformers_js_pipeline(pipeline: Any) -> Dict[str, Any]:
try:
from transformers_js_py import as_url, read_audio # type: ignore
except ImportError as ie:
raise ImportError(
"transformers_js_py not installed. Please add `transformers_js_py` to the requirements of your Gradio-Lite app"
) from ie
## Natural Language Processing ##
if pipeline.task == "fill-mask":
return {
"inputs": components.Textbox(label="Input"),
"outputs": components.Label(label="Classification"),
"preprocess": None,
"postprocess": lambda r: {i["token_str"]: i["score"] for i in r},
}
if pipeline.task == "question-answering":
return {
"inputs": [
components.Textbox(lines=7, label="Context"),
components.Textbox(label="Question"),
],
"outputs": [
components.Textbox(label="Answer"),
components.Label(label="Score"),
],
"preprocess": lambda c, q: (
q,
c,
), # Placed the context first in the input UI to match `handle_transformers_pipeline`'s order of inputs, but Transformers.js' question-answering pipeline expects the question first.
"postprocess": lambda r: (r["answer"], r["score"]),
}
if pipeline.task == "summarization":
return {
"inputs": [
components.Textbox(lines=7, label="Input"),
components.Slider(
label="The maximum numbers of tokens to generate",
minimum=1,
maximum=500,
value=100,
step=1,
),
],
"outputs": components.Textbox(label="Summary"),
"preprocess": lambda text, max_new_tokens: (
text,
{"max_new_tokens": max_new_tokens},
),
"postprocess": lambda r: r[0]["summary_text"],
}
if pipeline.task == "text-classification":
return {
"inputs": [
components.Textbox(label="Input"),
components.Number(label="Top k", value=5),
],
"outputs": components.Label(label="Classification"),
"preprocess": lambda text, topk: (text, {"topk": topk}),
"postprocess": lambda r: {i["label"]: i["score"] for i in r},
}
if pipeline.task == "text-generation":
return {
"inputs": components.Textbox(label="Input"),
"outputs": components.Textbox(label="Output"),
"preprocess": None,
"postprocess": lambda r: r[0]["generated_text"],
}
if pipeline.task == "text2text-generation":
return {
"inputs": [
components.Textbox(label="Input"),
components.Slider(
label="The maximum numbers of tokens to generate",
minimum=1,
maximum=500,
value=100,
step=1,
),
],
"outputs": components.Textbox(label="Generated Text"),
"preprocess": lambda text, max_new_tokens: (
text,
{"max_new_tokens": max_new_tokens},
),
"postprocess": lambda r: r[0]["generated_text"],
}
if pipeline.task == "token-classification":
return {
"inputs": components.Textbox(label="Input"),
"outputs": components.JSON(label="Output"),
"preprocess": None,
"postprocess": None,
"postprocess_takes_inputs": True,
}
if pipeline.task in {"translation", "translation_xx_to_yy"}:
return {
"inputs": [
components.Textbox(label="Input"),
components.Textbox(label="Source Language"),
components.Textbox(label="Target Language"),
],
"outputs": components.Textbox(label="Translation"),
"preprocess": lambda x, s, t: (x, {"src_lang": s, "tgt_lang": t}),
"postprocess": lambda r: r[0]["translation_text"],
}
if pipeline.task == "zero-shot-classification":
return {
"inputs": [
components.Textbox(label="Input"),
components.Textbox(label="Possible class names (comma-separated)"),
],
"outputs": components.Label(label="Classification"),
"preprocess": lambda text, classnames: (
text,
[c.strip() for c in classnames.split(",")],
),
"postprocess": lambda result: dict(zip(result["labels"], result["scores"])),
}
if pipeline.task == "feature-extraction":
return {
"inputs": components.Textbox(label="Input"),
"outputs": components.Dataframe(label="Output"),
"preprocess": None,
"postprocess": lambda tensor: tensor.to_numpy()[0],
}
## Vision ##
if pipeline.task == "depth-estimation":
return {
"inputs": components.Image(type="filepath", label="Input Image"),
"outputs": components.Image(label="Depth"),
"preprocess": lambda image_path: (as_url(image_path),),
"postprocess": lambda result: result["depth"].to_pil(),
}
if pipeline.task == "image-classification":
return {
"inputs": [
components.Image(type="filepath", label="Input Image"),
components.Number(label="Top k", value=5),
],
"outputs": components.Label(label="Classification"),
"preprocess": lambda image_path, topk: (as_url(image_path), {"topk": topk}),
"postprocess": lambda result: {
item["label"]: item["score"] for item in result
},
}
if pipeline.task == "image-segmentation":
return {
"inputs": components.Image(type="filepath", label="Input Image"),
"outputs": components.AnnotatedImage(label="Segmentation"),
"preprocess": lambda image_path: (as_url(image_path),),
"postprocess": lambda result, image_path: (
image_path,
[
(
item["mask"].to_numpy()[:, :, 0]
/ 255.0, # Reshape ([h,w,1] -> [h,w]) and normalize ([0,255] -> [0,1])
f"{item['label']} ({item['score']})",
)
for item in result
],
),
"postprocess_takes_inputs": True,
}
if pipeline.task == "image-to-image":
return {
"inputs": components.Image(type="filepath", label="Input Image"),
"outputs": components.Image(label="Output Image"),
"preprocess": lambda image_path: (as_url(image_path),),
"postprocess": lambda result: result.to_pil(),
}
if pipeline.task == "object-detection":
return {
"inputs": components.Image(type="filepath", label="Input Image"),
"outputs": components.AnnotatedImage(label="Objects Detected"),
"preprocess": lambda image_path: (as_url(image_path),),
"postprocess": lambda result, image_path: (
image_path,
[
(
(
int(item["box"]["xmin"]),
int(item["box"]["ymin"]),
int(item["box"]["xmax"]),
int(item["box"]["ymax"]),
),
f"{item['label']} ({item['score']})",
)
for item in result
],
),
"postprocess_takes_inputs": True,
}
if pipeline.task == "image-feature-extraction":
return {
"inputs": components.Image(type="filepath", label="Input Image"),
"outputs": components.Dataframe(label="Output"),
"preprocess": lambda image_path: (as_url(image_path),),
"postprocess": lambda tensor: tensor.to_numpy(),
}
## Audio ##
if pipeline.task == "audio-classification":
return {
"inputs": components.Audio(type="filepath", label="Input"),
"outputs": components.Label(label="Class"),
"preprocess": lambda i: (
read_audio(
i, pipeline.processor.feature_extractor.config["sampling_rate"]
),
),
"postprocess": lambda r: {i["label"]: i["score"] for i in r},
}
if pipeline.task == "automatic-speech-recognition":
return {
"inputs": components.Audio(type="filepath", label="Input"),
"outputs": components.Textbox(label="Output"),
"preprocess": lambda i: (
read_audio(
i, pipeline.processor.feature_extractor.config["sampling_rate"]
),
),
"postprocess": lambda r: r["text"],
}
if pipeline.task == "text-to-audio":
return {
"inputs": [
components.Textbox(label="Input"),
components.Textbox(label="Speaker Embeddings"),
],
"outputs": components.Audio(label="Output"),
"preprocess": lambda text, speaker_embeddings: (
text,
{"speaker_embeddings": speaker_embeddings},
),
"postprocess": lambda r: (r["sampling_rate"], np.asarray(r["audio"])),
}
## Multimodal ##
if pipeline.task == "document-question-answering":
return {
"inputs": [
components.Image(type="filepath", label="Input Document"),
components.Textbox(label="Question"),
],
"outputs": components.Textbox(label="Label"),
"preprocess": lambda img, q: (as_url(img), q),
"postprocess": lambda r: r[0][
"answer"
], # This data structure is different from the original Transformers.
}
if pipeline.task == "image-to-text":
return {
"inputs": components.Image(type="filepath", label="Input Image"),
"outputs": components.Textbox(label="Output"),
"preprocess": lambda image_path: (as_url(image_path),),
"postprocess": lambda r: r[0]["generated_text"],
}
if pipeline.task == "zero-shot-audio-classification":
return {
"inputs": [
components.Audio(type="filepath", label="Input"),
components.Textbox(label="Possible class names (comma-separated)"),
],
"outputs": components.Label(label="Classification"),
"preprocess": lambda audio_path, classnames: (
read_audio(
audio_path,
pipeline.processor.feature_extractor.config["sampling_rate"],
),
[c.strip() for c in classnames.split(",")],
),
"postprocess": lambda result: {i["label"]: i["score"] for i in result},
}
if pipeline.task == "zero-shot-image-classification":
return {
"inputs": [
components.Image(type="filepath", label="Input Image"),
components.Textbox(label="Possible class names (comma-separated)"),
],
"outputs": components.Label(label="Classification"),
"preprocess": lambda image_path, classnames: (
as_url(image_path),
[c.strip() for c in classnames.split(",")],
),
"postprocess": lambda result: {i["label"]: i["score"] for i in result},
}
if pipeline.task == "zero-shot-object-detection":
return {
"inputs": [
components.Image(type="filepath", label="Input Image"),
components.Textbox(label="Possible class names (comma-separated)"),
],
"outputs": components.AnnotatedImage(label="Objects Detected"),
"preprocess": lambda image_path, classnames: (
as_url(image_path),
[c.strip() for c in classnames.split(",")],
),
"postprocess": lambda result, image_path, _: (
image_path,
[
(
(
int(item["box"]["xmin"]),
int(item["box"]["ymin"]),
int(item["box"]["xmax"]),
int(item["box"]["ymax"]),
),
f"{item['label']} ({item['score']})",
)
for item in result
],
),
"postprocess_takes_inputs": True,
}
raise ValueError(f"Unsupported transformers_js_py pipeline type: {pipeline.task}")
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