Upload externalmod.py

externalmod.py

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+"""This module should not be used directly as its API is subject to change. Instead,
+use the `gr.Blocks.load()` or `gr.load()` functions."""
+
+from __future__ import annotations
+
+import json
+import os
+import re
+import tempfile
+import warnings
+from pathlib import Path
+from typing import TYPE_CHECKING, Callable, Literal
+
+import httpx
+import huggingface_hub
+from gradio_client import Client
+from gradio_client.client import Endpoint
+from gradio_client.documentation import document
+from packaging import version
+
+import gradio
+from gradio import components, external_utils, utils
+from gradio.context import Context
+from gradio.exceptions import (
+    GradioVersionIncompatibleError,
+    ModelNotFoundError,
+    TooManyRequestsError,
+)
+from gradio.processing_utils import save_base64_to_cache, to_binary
+
+if TYPE_CHECKING:
+    from gradio.blocks import Blocks
+    from gradio.interface import Interface
+
+
+HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None # If private or gated models aren't used, ENV setting is unnecessary.
+server_timeout = 600
+
+
+@document()
+def load(
+    name: str,
+    src: str | None = None,
+    hf_token: str | Literal[False] | None = None,
+    alias: str | None = None,
+    **kwargs,
+) -> Blocks:
+    """
+    Constructs a demo from a Hugging Face repo. Can accept model repos (if src is "models") or Space repos (if src is "spaces"). The input
+    and output components are automatically loaded from the repo. Note that if a Space is loaded, certain high-level attributes of the Blocks (e.g.
+    custom `css`, `js`, and `head` attributes) will not be loaded.
+    Parameters:
+        name: the name of the model (e.g. "gpt2" or "facebook/bart-base") or space (e.g. "flax-community/spanish-gpt2"), can include the `src` as prefix (e.g. "models/facebook/bart-base")
+        src: the source of the model: `models` or `spaces` (or leave empty if source is provided as a prefix in `name`)
+        hf_token: optional access token for loading private Hugging Face Hub models or spaces. Will default to the locally saved token if not provided. Pass `token=False` if you don't want to send your token to the server. Find your token here: https://huggingface.co/settings/tokens.  Warning: only provide a token if you are loading a trusted private Space as it can be read by the Space you are loading.
+        alias: optional string used as the name of the loaded model instead of the default name (only applies if loading a Space running Gradio 2.x)
+    Returns:
+        a Gradio Blocks object for the given model
+    Example:
+        import gradio as gr
+        demo = gr.load("gradio/question-answering", src="spaces")
+        demo.launch()
+    """
+    return load_blocks_from_repo(
+        name=name, src=src, hf_token=hf_token, alias=alias, **kwargs
+    )
+
+
+def load_blocks_from_repo(
+    name: str,
+    src: str | None = None,
+    hf_token: str | Literal[False] | None = None,
+    alias: str | None = None,
+    **kwargs,
+) -> Blocks:
+    """Creates and returns a Blocks instance from a Hugging Face model or Space repo."""
+    if src is None:
+        # Separate the repo type (e.g. "model") from repo name (e.g. "google/vit-base-patch16-224")
+        tokens = name.split("/")
+        if len(tokens) <= 1:
+            raise ValueError(
+                "Either `src` parameter must be provided, or `name` must be formatted as {src}/{repo name}"
+            )
+        src = tokens[0]
+        name = "/".join(tokens[1:])
+
+    factory_methods: dict[str, Callable] = {
+        # for each repo type, we have a method that returns the Interface given the model name & optionally an hf_token
+        "huggingface": from_model,
+        "models": from_model,
+        "spaces": from_spaces,
+    }
+    if src.lower() not in factory_methods:
+        raise ValueError(f"parameter: src must be one of {factory_methods.keys()}")
+
+    if hf_token is not None and hf_token is not False:
+        if Context.hf_token is not None and Context.hf_token != hf_token:
+            warnings.warn(
+                """You are loading a model/Space with a different access token than the one you used to load a previous model/Space. This is not recommended, as it may cause unexpected behavior."""
+            )
+        Context.hf_token = hf_token
+
+    blocks: gradio.Blocks = factory_methods[src](name, hf_token, alias, **kwargs)
+    return blocks
+
+
+def from_model(
+    model_name: str, hf_token: str | Literal[False] | None, alias: str | None, **kwargs
+):
+    model_url = f"https://huggingface.co/{model_name}"
+    api_url = f"https://api-inference.huggingface.co/models/{model_name}"
+    print(f"Fetching model from: {model_url}")
+
+    headers = (
+        {} if hf_token in [False, None] else {"Authorization": f"Bearer {hf_token}"}
+    )
+    response = httpx.request("GET", api_url, headers=headers)
+    if response.status_code != 200:
+        raise ModelNotFoundError(
+            f"Could not find model: {model_name}. If it is a private or gated model, please provide your Hugging Face access token (https://huggingface.co/settings/tokens) as the argument for the `hf_token` parameter."
+        )
+    p = response.json().get("pipeline_tag")
+
+    headers["X-Wait-For-Model"] = "true"
+    client = huggingface_hub.InferenceClient(
+        model=model_name, headers=headers, token=hf_token, timeout=server_timeout,
+    )
+
+    # For tasks that are not yet supported by the InferenceClient
+    GRADIO_CACHE = os.environ.get("GRADIO_TEMP_DIR") or str(  # noqa: N806
+        Path(tempfile.gettempdir()) / "gradio"
+    )
+
+    def custom_post_binary(data):
+        data = to_binary({"path": data})
+        response = httpx.request("POST", api_url, headers=headers, content=data)
+        return save_base64_to_cache(
+            external_utils.encode_to_base64(response), cache_dir=GRADIO_CACHE
+        )
+
+    preprocess = None
+    postprocess = None
+    examples = None
+
+    # example model: ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition
+    if p == "audio-classification":
+        inputs = components.Audio(type="filepath", label="Input")
+        outputs = components.Label(label="Class")
+        postprocess = external_utils.postprocess_label
+        examples = [
+            "https://gradio-builds.s3.amazonaws.com/demo-files/audio_sample.wav"
+        ]
+        fn = client.audio_classification
+    # example model: facebook/xm_transformer_sm_all-en
+    elif p == "audio-to-audio":
+        inputs = components.Audio(type="filepath", label="Input")
+        outputs = components.Audio(label="Output")
+        examples = [
+            "https://gradio-builds.s3.amazonaws.com/demo-files/audio_sample.wav"
+        ]
+        fn = custom_post_binary
+    # example model: facebook/wav2vec2-base-960h
+    elif p == "automatic-speech-recognition":
+        inputs = components.Audio(type="filepath", label="Input")
+        outputs = components.Textbox(label="Output")
+        examples = [
+            "https://gradio-builds.s3.amazonaws.com/demo-files/audio_sample.wav"
+        ]
+        fn = client.automatic_speech_recognition
+    # example model: microsoft/DialoGPT-medium
+    elif p == "conversational":
+        inputs = [
+            components.Textbox(render=False),
+            components.State(render=False),
+        ]
+        outputs = [
+            components.Chatbot(render=False),
+            components.State(render=False),
+        ]
+        examples = [["Hello World"]]
+        preprocess = external_utils.chatbot_preprocess
+        postprocess = external_utils.chatbot_postprocess
+        fn = client.conversational
+    # example model: julien-c/distilbert-feature-extraction
+    elif p == "feature-extraction":
+        inputs = components.Textbox(label="Input")
+        outputs = components.Dataframe(label="Output")
+        fn = client.feature_extraction
+        postprocess = utils.resolve_singleton
+    # example model: distilbert/distilbert-base-uncased
+    elif p == "fill-mask":
+        inputs = components.Textbox(label="Input")
+        outputs = components.Label(label="Classification")
+        examples = [
+            "Hugging Face is the AI community, working together, to [MASK] the future."
+        ]
+        postprocess = external_utils.postprocess_mask_tokens
+        fn = client.fill_mask
+    # Example: google/vit-base-patch16-224
+    elif p == "image-classification":
+        inputs = components.Image(type="filepath", label="Input Image")
+        outputs = components.Label(label="Classification")
+        postprocess = external_utils.postprocess_label
+        examples = ["https://gradio-builds.s3.amazonaws.com/demo-files/cheetah-002.jpg"]
+        fn = client.image_classification
+    # Example: deepset/xlm-roberta-base-squad2
+    elif p == "question-answering":
+        inputs = [
+            components.Textbox(label="Question"),
+            components.Textbox(lines=7, label="Context"),
+        ]
+        outputs = [
+            components.Textbox(label="Answer"),
+            components.Label(label="Score"),
+        ]
+        examples = [
+            [
+                "What entity was responsible for the Apollo program?",
+                "The Apollo program, also known as Project Apollo, was the third United States human spaceflight"
+                " program carried out by the National Aeronautics and Space Administration (NASA), which accomplished"
+                " landing the first humans on the Moon from 1969 to 1972.",
+            ]
+        ]
+        postprocess = external_utils.postprocess_question_answering
+        fn = client.question_answering
+    # Example: facebook/bart-large-cnn
+    elif p == "summarization":
+        inputs = components.Textbox(label="Input")
+        outputs = components.Textbox(label="Summary")
+        examples = [
+            [
+                "The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct."
+            ]
+        ]
+        fn = client.summarization
+    # Example: distilbert-base-uncased-finetuned-sst-2-english
+    elif p == "text-classification":
+        inputs = components.Textbox(label="Input")
+        outputs = components.Label(label="Classification")
+        examples = ["I feel great"]
+        postprocess = external_utils.postprocess_label
+        fn = client.text_classification
+    # Example: gpt2
+    elif p == "text-generation":
+        inputs = components.Textbox(label="Text")
+        outputs = inputs
+        examples = ["Once upon a time"]
+        fn = external_utils.text_generation_wrapper(client)
+    # Example: valhalla/t5-small-qa-qg-hl
+    elif p == "text2text-generation":
+        inputs = components.Textbox(label="Input")
+        outputs = components.Textbox(label="Generated Text")
+        examples = ["Translate English to Arabic: How are you?"]
+        fn = client.text_generation
+    # Example: Helsinki-NLP/opus-mt-en-ar
+    elif p == "translation":
+        inputs = components.Textbox(label="Input")
+        outputs = components.Textbox(label="Translation")
+        examples = ["Hello, how are you?"]
+        fn = client.translation
+    # Example: facebook/bart-large-mnli
+    elif p == "zero-shot-classification":
+        inputs = [
+            components.Textbox(label="Input"),
+            components.Textbox(label="Possible class names (" "comma-separated)"),
+            components.Checkbox(label="Allow multiple true classes"),
+        ]
+        outputs = components.Label(label="Classification")
+        postprocess = external_utils.postprocess_label
+        examples = [["I feel great", "happy, sad", False]]
+        fn = external_utils.zero_shot_classification_wrapper(client)
+    # Example: sentence-transformers/distilbert-base-nli-stsb-mean-tokens
+    elif p == "sentence-similarity":
+        inputs = [
+            components.Textbox(
+                label="Source Sentence",
+                placeholder="Enter an original sentence",
+            ),
+            components.Textbox(
+                lines=7,
+                placeholder="Sentences to compare to -- separate each sentence by a newline",
+                label="Sentences to compare to",
+            ),
+        ]
+        outputs = components.JSON(label="Similarity scores")
+        examples = [["That is a happy person", "That person is very happy"]]
+        fn = external_utils.sentence_similarity_wrapper(client)
+    # Example: julien-c/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train
+    elif p == "text-to-speech":
+        inputs = components.Textbox(label="Input")
+        outputs = components.Audio(label="Audio")
+        examples = ["Hello, how are you?"]
+        fn = client.text_to_speech
+    # example model: osanseviero/BigGAN-deep-128
+    elif p == "text-to-image":
+        inputs = components.Textbox(label="Input")
+        outputs = components.Image(label="Output")
+        examples = ["A beautiful sunset"]
+        fn = client.text_to_image
+    # example model: huggingface-course/bert-finetuned-ner
+    elif p == "token-classification":
+        inputs = components.Textbox(label="Input")
+        outputs = components.HighlightedText(label="Output")
+        examples = [
+            "Hugging Face is a company based in Paris and New York City that acquired Gradio in 2021."
+        ]
+        fn = external_utils.token_classification_wrapper(client)
+    # example model: impira/layoutlm-document-qa
+    elif p == "document-question-answering":
+        inputs = [
+            components.Image(type="filepath", label="Input Document"),
+            components.Textbox(label="Question"),
+        ]
+        postprocess = external_utils.postprocess_label
+        outputs = components.Label(label="Label")
+        fn = client.document_question_answering
+    # example model: dandelin/vilt-b32-finetuned-vqa
+    elif p == "visual-question-answering":
+        inputs = [
+            components.Image(type="filepath", label="Input Image"),
+            components.Textbox(label="Question"),
+        ]
+        outputs = components.Label(label="Label")
+        postprocess = external_utils.postprocess_visual_question_answering
+        examples = [
+            [
+                "https://gradio-builds.s3.amazonaws.com/demo-files/cheetah-002.jpg",
+                "What animal is in the image?",
+            ]
+        ]
+        fn = client.visual_question_answering
+    # example model: Salesforce/blip-image-captioning-base
+    elif p == "image-to-text":
+        inputs = components.Image(type="filepath", label="Input Image")
+        outputs = components.Textbox(label="Generated Text")
+        examples = ["https://gradio-builds.s3.amazonaws.com/demo-files/cheetah-002.jpg"]
+        fn = client.image_to_text
+    # example model: rajistics/autotrain-Adult-934630783
+    elif p in ["tabular-classification", "tabular-regression"]:
+        examples = external_utils.get_tabular_examples(model_name)
+        col_names, examples = external_utils.cols_to_rows(examples)  # type: ignore
+        examples = [[examples]] if examples else None
+        inputs = components.Dataframe(
+            label="Input Rows",
+            type="pandas",
+            headers=col_names,
+            col_count=(len(col_names), "fixed"),
+            render=False,
+        )
+        outputs = components.Dataframe(
+            label="Predictions", type="array", headers=["prediction"]
+        )
+        fn = external_utils.tabular_wrapper
+    # example model: microsoft/table-transformer-detection
+    elif p == "object-detection":
+        inputs = components.Image(type="filepath", label="Input Image")
+        outputs = components.AnnotatedImage(label="Annotations")
+        fn = external_utils.object_detection_wrapper(client)
+    # example model: stabilityai/stable-diffusion-xl-refiner-1.0
+    elif p == "image-to-image":
+        inputs = [
+            components.Image(type="filepath", label="Input Image"),
+            components.Textbox(label="Input"),
+        ]
+        outputs = components.Image(label="Output")
+        examples = [
+            [
+                "https://gradio-builds.s3.amazonaws.com/demo-files/cheetah-002.jpg",
+                "Photo of a cheetah with green eyes",
+            ]
+        ]
+        fn = client.image_to_image
+    else:
+        raise ValueError(f"Unsupported pipeline type: {p}")
+
+    def query_huggingface_inference_endpoints(*data, **kwargs):
+        if preprocess is not None:
+            data = preprocess(*data)
+        try:
+            data = fn(*data, **kwargs)  # type: ignore
+        except huggingface_hub.utils.HfHubHTTPError as e:
+            if "429" in str(e):
+                raise TooManyRequestsError() from e
+        if postprocess is not None:
+            data = postprocess(data)  # type: ignore
+        return data
+
+    query_huggingface_inference_endpoints.__name__ = alias or model_name
+
+    interface_info = {
+        "fn": query_huggingface_inference_endpoints,
+        "inputs": inputs,
+        "outputs": outputs,
+        "title": model_name,
+        #"examples": examples,
+    }
+
+    kwargs = dict(interface_info, **kwargs)
+    interface = gradio.Interface(**kwargs)
+    return interface
+
+
+def from_spaces(
+    space_name: str, hf_token: str | None, alias: str | None, **kwargs
+) -> Blocks:
+    space_url = f"https://huggingface.co/spaces/{space_name}"
+
+    print(f"Fetching Space from: {space_url}")
+
+    headers = {}
+    if hf_token not in [False, None]:
+        headers["Authorization"] = f"Bearer {hf_token}"
+
+    iframe_url = (
+        httpx.get(
+            f"https://huggingface.co/api/spaces/{space_name}/host", headers=headers
+        )
+        .json()
+        .get("host")
+    )
+
+    if iframe_url is None:
+        raise ValueError(
+            f"Could not find Space: {space_name}. If it is a private or gated Space, please provide your Hugging Face access token (https://huggingface.co/settings/tokens) as the argument for the `hf_token` parameter."
+        )
+
+    r = httpx.get(iframe_url, headers=headers)
+
+    result = re.search(
+        r"window.gradio_config = (.*?);[\s]*</script>", r.text
+    )  # some basic regex to extract the config
+    try:
+        config = json.loads(result.group(1))  # type: ignore
+    except AttributeError as ae:
+        raise ValueError(f"Could not load the Space: {space_name}") from ae
+    if "allow_flagging" in config:  # Create an Interface for Gradio 2.x Spaces
+        return from_spaces_interface(
+            space_name, config, alias, hf_token, iframe_url, **kwargs
+        )
+    else:  # Create a Blocks for Gradio 3.x Spaces
+        if kwargs:
+            warnings.warn(
+                "You cannot override parameters for this Space by passing in kwargs. "
+                "Instead, please load the Space as a function and use it to create a "
+                "Blocks or Interface locally. You may find this Guide helpful: "
+                "https://gradio.app/using_blocks_like_functions/"
+            )
+        return from_spaces_blocks(space=space_name, hf_token=hf_token)
+
+
+def from_spaces_blocks(space: str, hf_token: str | None) -> Blocks:
+    client = Client(
+        space,
+        hf_token=hf_token,
+        download_files=False,
+        _skip_components=False,
+    )
+    # We set deserialize to False to avoid downloading output files from the server.
+    # Instead, we serve them as URLs using the /proxy/ endpoint directly from the server.
+
+    if client.app_version < version.Version("4.0.0b14"):
+        raise GradioVersionIncompatibleError(
+            f"Gradio version 4.x cannot load spaces with versions less than 4.x ({client.app_version})."
+            "Please downgrade to version 3 to load this space."
+        )
+
+    # Use end_to_end_fn here to properly upload/download all files
+    predict_fns = []
+    for fn_index, endpoint in client.endpoints.items():
+        if not isinstance(endpoint, Endpoint):
+            raise TypeError(
+                f"Expected endpoint to be an Endpoint, but got {type(endpoint)}"
+            )
+        helper = client.new_helper(fn_index)
+        if endpoint.backend_fn:
+            predict_fns.append(endpoint.make_end_to_end_fn(helper))
+        else:
+            predict_fns.append(None)
+    return gradio.Blocks.from_config(client.config, predict_fns, client.src)  # type: ignore
+
+
+def from_spaces_interface(
+    model_name: str,
+    config: dict,
+    alias: str | None,
+    hf_token: str | None,
+    iframe_url: str,
+    **kwargs,
+) -> Interface:
+    config = external_utils.streamline_spaces_interface(config)
+    api_url = f"{iframe_url}/api/predict/"
+    headers = {"Content-Type": "application/json"}
+    if hf_token not in [False, None]:
+        headers["Authorization"] = f"Bearer {hf_token}"
+
+    # The function should call the API with preprocessed data
+    def fn(*data):
+        data = json.dumps({"data": data})
+        response = httpx.post(api_url, headers=headers, data=data)  # type: ignore
+        result = json.loads(response.content.decode("utf-8"))
+        if "error" in result and "429" in result["error"]:
+            raise TooManyRequestsError("Too many requests to the Hugging Face API")
+        try:
+            output = result["data"]
+        except KeyError as ke:
+            raise KeyError(
+                f"Could not find 'data' key in response from external Space. Response received: {result}"
+            ) from ke
+        if (
+            len(config["outputs"]) == 1
+        ):  # if the fn is supposed to return a single value, pop it
+            output = output[0]
+        if (
+            len(config["outputs"]) == 1 and isinstance(output, list)
+        ):  # Needed to support Output.Image() returning bounding boxes as well (TODO: handle different versions of gradio since they have slightly different APIs)
+            output = output[0]
+        return output
+
+    fn.__name__ = alias if (alias is not None) else model_name
+    config["fn"] = fn
+
+    kwargs = dict(config, **kwargs)
+    kwargs["_api_mode"] = True
+    interface = gradio.Interface(**kwargs)
+    return interface
+
+
+def gr_Interface_load(
+    name: str,
+    src: str | None = None,
+    hf_token: str | None = None,
+    alias: str | None = None,
+    **kwargs, # ignore
+) -> Blocks:
+    try:
+        return load_blocks_from_repo(name, src, hf_token, alias)
+    except Exception as e:
+        print(e)
+        return gradio.Interface(lambda: None, ['text'], ['image'])
+
+
+def list_uniq(l):
+    return sorted(set(l), key=l.index)
+
+
+def get_status(model_name: str):
+    from huggingface_hub import AsyncInferenceClient
+    client = AsyncInferenceClient(token=HF_TOKEN, timeout=10)
+    return client.get_model_status(model_name)
+
+
+def is_loadable(model_name: str, force_gpu: bool = False):
+    try:
+        status = get_status(model_name)
+    except Exception as e:
+        print(e)
+        print(f"Couldn't load {model_name}.")
+        return False
+    gpu_state = isinstance(status.compute_type, dict) and "gpu" in status.compute_type.keys()
+    if status is None or status.state not in ["Loadable", "Loaded"] or (force_gpu and not gpu_state):
+        print(f"Couldn't load {model_name}. Model state:'{status.state}', GPU:{gpu_state}")
+    return status is not None and status.state in ["Loadable", "Loaded"] and (not force_gpu or gpu_state)
+
+
+def find_model_list(author: str="", tags: list[str]=[], not_tag="", sort: str="last_modified", limit: int=30, force_gpu=False, check_status=False):
+    from huggingface_hub import HfApi
+    api = HfApi(token=HF_TOKEN)
+    default_tags = ["diffusers"]
+    if not sort: sort = "last_modified"
+    limit = limit * 20 if check_status and force_gpu else limit * 5
+    models = []
+    try:
+        model_infos = api.list_models(author=author, #task="text-to-image",
+                                       tags=list_uniq(default_tags + tags), cardData=True, sort=sort, limit=limit)
+    except Exception as e:
+        print(f"Error: Failed to list models.")
+        print(e)
+        return models
+    for model in model_infos:
+        if not model.private and not model.gated or HF_TOKEN is not None:
+           loadable = is_loadable(model.id, force_gpu) if check_status else True
+           if not_tag and not_tag in model.tags or not loadable: continue
+           models.append(model.id)
+           if len(models) == limit: break
+    return models