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Allow gr.Interface.from_pipeline() and gr.load() to work within gr.Blocks() #5231

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merged 6 commits into from Aug 16, 2023
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Allow gr.Interface.from_pipeline() and gr.load() to work within gr.Blocks() #5231

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04a0457
add test
abidlabs Aug 15, 2023
87d3bda
external
abidlabs Aug 15, 2023
baa4bdd
fixed in external
abidlabs Aug 15, 2023
24dc8e9
add changeset
gradio-pr-bot Aug 15, 2023
f749439
close demo
abidlabs Aug 16, 2023
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Merge branch 'main' into pipe
abidlabs Aug 16, 2023
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5 changes: 5 additions & 0 deletions .changeset/poor-papayas-behave.md
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@@ -0,0 +1,5 @@
---
"gradio": patch
---

fix:Allow `gr.Interface.from_pipeline()` and `gr.load()` to work within `gr.Blocks()`
114 changes: 65 additions & 49 deletions gradio/external.py
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Expand Up @@ -152,52 +152,60 @@ def from_model(model_name: str, hf_token: str | None, alias: str | None, **kwarg
pipelines = {
"audio-classification": {
# example model: ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition
"inputs": components.Audio(source="upload", type="filepath", label="Input"),
"outputs": components.Label(label="Class"),
"inputs": components.Audio(
source="upload", type="filepath", label="Input", render=False
),
"outputs": components.Label(label="Class", render=False),
"preprocess": lambda i: to_binary,
"postprocess": lambda r: postprocess_label(
{i["label"].split(", ")[0]: i["score"] for i in r.json()}
),
},
"audio-to-audio": {
# example model: facebook/xm_transformer_sm_all-en
"inputs": components.Audio(source="upload", type="filepath", label="Input"),
"outputs": components.Audio(label="Output"),
"inputs": components.Audio(
source="upload", type="filepath", label="Input", render=False
),
"outputs": components.Audio(label="Output", render=False),
"preprocess": to_binary,
"postprocess": encode_to_base64,
},
"automatic-speech-recognition": {
# example model: facebook/wav2vec2-base-960h
"inputs": components.Audio(source="upload", type="filepath", label="Input"),
"outputs": components.Textbox(label="Output"),
"inputs": components.Audio(
source="upload", type="filepath", label="Input", render=False
),
"outputs": components.Textbox(label="Output", render=False),
"preprocess": to_binary,
"postprocess": lambda r: r.json()["text"],
},
"conversational": {
"inputs": [components.Textbox(), components.State()], # type: ignore
"outputs": [components.Chatbot(), components.State()], # type: ignore
"inputs": [components.Textbox(render=False), components.State(render=False)], # type: ignore
"outputs": [components.Chatbot(render=False), components.State(render=False)], # type: ignore
"preprocess": chatbot_preprocess,
"postprocess": chatbot_postprocess,
},
"feature-extraction": {
# example model: julien-c/distilbert-feature-extraction
"inputs": components.Textbox(label="Input"),
"outputs": components.Dataframe(label="Output"),
"inputs": components.Textbox(label="Input", render=False),
"outputs": components.Dataframe(label="Output", render=False),
"preprocess": lambda x: {"inputs": x},
"postprocess": lambda r: r.json()[0],
},
"fill-mask": {
"inputs": components.Textbox(label="Input"),
"outputs": components.Label(label="Classification"),
"inputs": components.Textbox(label="Input", render=False),
"outputs": components.Label(label="Classification", render=False),
"preprocess": lambda x: {"inputs": x},
"postprocess": lambda r: postprocess_label(
{i["token_str"]: i["score"] for i in r.json()}
),
},
"image-classification": {
# Example: google/vit-base-patch16-224
"inputs": components.Image(type="filepath", label="Input Image"),
"outputs": components.Label(label="Classification"),
"inputs": components.Image(
type="filepath", label="Input Image", render=False
),
"outputs": components.Label(label="Classification", render=False),
"preprocess": to_binary,
"postprocess": lambda r: postprocess_label(
{i["label"].split(", ")[0]: i["score"] for i in r.json()}
Expand All @@ -206,60 +214,62 @@ def from_model(model_name: str, hf_token: str | None, alias: str | None, **kwarg
"question-answering": {
# Example: deepset/xlm-roberta-base-squad2
"inputs": [
components.Textbox(lines=7, label="Context"),
components.Textbox(label="Question"),
components.Textbox(lines=7, label="Context", render=False),
components.Textbox(label="Question", render=False),
],
"outputs": [
components.Textbox(label="Answer"),
components.Label(label="Score"),
components.Textbox(label="Answer", render=False),
components.Label(label="Score", render=False),
],
"preprocess": lambda c, q: {"inputs": {"context": c, "question": q}},
"postprocess": lambda r: (r.json()["answer"], {"label": r.json()["score"]}),
},
"summarization": {
# Example: facebook/bart-large-cnn
"inputs": components.Textbox(label="Input"),
"outputs": components.Textbox(label="Summary"),
"inputs": components.Textbox(label="Input", render=False),
"outputs": components.Textbox(label="Summary", render=False),
"preprocess": lambda x: {"inputs": x},
"postprocess": lambda r: r.json()[0]["summary_text"],
},
"text-classification": {
# Example: distilbert-base-uncased-finetuned-sst-2-english
"inputs": components.Textbox(label="Input"),
"outputs": components.Label(label="Classification"),
"inputs": components.Textbox(label="Input", render=False),
"outputs": components.Label(label="Classification", render=False),
"preprocess": lambda x: {"inputs": x},
"postprocess": lambda r: postprocess_label(
{i["label"].split(", ")[0]: i["score"] for i in r.json()[0]}
),
},
"text-generation": {
# Example: gpt2
"inputs": components.Textbox(label="Input"),
"outputs": components.Textbox(label="Output"),
"inputs": components.Textbox(label="Input", render=False),
"outputs": components.Textbox(label="Output", render=False),
"preprocess": lambda x: {"inputs": x},
"postprocess": lambda r: r.json()[0]["generated_text"],
},
"text2text-generation": {
# Example: valhalla/t5-small-qa-qg-hl
"inputs": components.Textbox(label="Input"),
"outputs": components.Textbox(label="Generated Text"),
"inputs": components.Textbox(label="Input", render=False),
"outputs": components.Textbox(label="Generated Text", render=False),
"preprocess": lambda x: {"inputs": x},
"postprocess": lambda r: r.json()[0]["generated_text"],
},
"translation": {
"inputs": components.Textbox(label="Input"),
"outputs": components.Textbox(label="Translation"),
"inputs": components.Textbox(label="Input", render=False),
"outputs": components.Textbox(label="Translation", render=False),
"preprocess": lambda x: {"inputs": x},
"postprocess": lambda r: r.json()[0]["translation_text"],
},
"zero-shot-classification": {
# Example: facebook/bart-large-mnli
"inputs": [
components.Textbox(label="Input"),
components.Textbox(label="Possible class names (" "comma-separated)"),
components.Checkbox(label="Allow multiple true classes"),
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"),
"outputs": components.Label(label="Classification", render=False),
"preprocess": lambda i, c, m: {
"inputs": i,
"parameters": {"candidate_labels": c, "multi_class": m},
Expand All @@ -275,15 +285,18 @@ def from_model(model_name: str, hf_token: str | None, alias: str | None, **kwarg
# Example: sentence-transformers/distilbert-base-nli-stsb-mean-tokens
"inputs": [
components.Textbox(
value="That is a happy person", label="Source Sentence"
value="That is a happy person",
label="Source Sentence",
render=False,
),
components.Textbox(
lines=7,
placeholder="Separate each sentence by a newline",
label="Sentences to compare to",
render=False,
),
],
"outputs": components.Label(label="Classification"),
"outputs": components.Label(label="Classification", render=False),
"preprocess": lambda src, sentences: {
"inputs": {
"source_sentence": src,
Expand All @@ -296,32 +309,32 @@ def from_model(model_name: str, hf_token: str | None, alias: str | None, **kwarg
},
"text-to-speech": {
# Example: julien-c/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train
"inputs": components.Textbox(label="Input"),
"outputs": components.Audio(label="Audio"),
"inputs": components.Textbox(label="Input", render=False),
"outputs": components.Audio(label="Audio", render=False),
"preprocess": lambda x: {"inputs": x},
"postprocess": encode_to_base64,
},
"text-to-image": {
# example model: osanseviero/BigGAN-deep-128
"inputs": components.Textbox(label="Input"),
"outputs": components.Image(label="Output"),
"inputs": components.Textbox(label="Input", render=False),
"outputs": components.Image(label="Output", render=False),
"preprocess": lambda x: {"inputs": x},
"postprocess": encode_to_base64,
},
"token-classification": {
# example model: huggingface-course/bert-finetuned-ner
"inputs": components.Textbox(label="Input"),
"outputs": components.HighlightedText(label="Output"),
"inputs": components.Textbox(label="Input", render=False),
"outputs": components.HighlightedText(label="Output", render=False),
"preprocess": lambda x: {"inputs": x},
"postprocess": lambda r: r, # Handled as a special case in query_huggingface_api()
},
"document-question-answering": {
# example model: impira/layoutlm-document-qa
"inputs": [
components.Image(type="filepath", label="Input Document"),
components.Textbox(label="Question"),
components.Image(type="filepath", label="Input Document", render=False),
components.Textbox(label="Question", render=False),
],
"outputs": components.Label(label="Label"),
"outputs": components.Label(label="Label", render=False),
"preprocess": lambda img, q: {
"inputs": {
"image": extract_base64_data(img), # Extract base64 data
Expand All @@ -335,10 +348,10 @@ def from_model(model_name: str, hf_token: str | None, alias: str | None, **kwarg
"visual-question-answering": {
# example model: dandelin/vilt-b32-finetuned-vqa
"inputs": [
components.Image(type="filepath", label="Input Image"),
components.Textbox(label="Question"),
components.Image(type="filepath", label="Input Image", render=False),
components.Textbox(label="Question", render=False),
],
"outputs": components.Label(label="Label"),
"outputs": components.Label(label="Label", render=False),
"preprocess": lambda img, q: {
"inputs": {
"image": extract_base64_data(img),
Expand All @@ -351,8 +364,10 @@ def from_model(model_name: str, hf_token: str | None, alias: str | None, **kwarg
},
"image-to-text": {
# example model: Salesforce/blip-image-captioning-base
"inputs": components.Image(type="filepath", label="Input Image"),
"outputs": components.Textbox(label="Generated Text"),
"inputs": components.Image(
type="filepath", label="Input Image", render=False
),
"outputs": components.Textbox(label="Generated Text", render=False),
"preprocess": to_binary,
"postprocess": lambda r: r.json()[0]["generated_text"],
},
Expand All @@ -369,9 +384,10 @@ def from_model(model_name: str, hf_token: str | None, alias: str | None, **kwarg
type="pandas",
headers=col_names,
col_count=(len(col_names), "fixed"),
render=False,
),
"outputs": components.Dataframe(
label="Predictions", type="array", headers=["prediction"]
label="Predictions", type="array", headers=["prediction"], render=False
),
"preprocess": rows_to_cols,
"postprocess": lambda r: {
Expand Down