Fix most flaky Python tests in 5.0-dev branch

.changeset/busy-lizards-heal.md

@@ -0,0 +1,6 @@
+---
+"gradio": minor
+"gradio_client": minor
+---
+
+feat:Fix most flaky Python tests in `5.0-dev` branch

client/python/gradio_client/client.py

@@ -156,7 +156,7 @@ def __init__(
         )
         api_prefix: str = self.config.get("api_prefix", "")
         self.api_prefix = api_prefix.lstrip("/") + "/"
-        self.src_prefixed = urllib.parse.urljoin(self.src, api_prefix) + "/"
+        self.src_prefixed = urllib.parse.urljoin(self.src, api_prefix).rstrip("/") + "/"
 
         self.api_url = urllib.parse.urljoin(self.src_prefixed, utils.API_URL)
         self.sse_url = urllib.parse.urljoin(
@@ -1079,8 +1079,7 @@ def __init__(
             self._get_component_type(id_) for id_ in dependency["outputs"]
         ]
         self.parameters_info = self._get_parameters_info()
-
-        self.root_url = client.src.rstrip("/") + "/" + client.api_prefix
+        self.root_url = self.client.src_prefixed
 
         # Disallow hitting endpoints that the Gradio app has disabled
         self.is_valid = self.api_name is not False

client/python/test/test_client.py

@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 import json
-import os
 import pathlib
 import tempfile
 import time
@@ -16,7 +15,6 @@
 import httpx
 import huggingface_hub
 import pytest
-from huggingface_hub import HfFolder
 from huggingface_hub.utils import RepositoryNotFoundError
 
 from gradio_client import Client, handle_file
@@ -30,7 +28,7 @@
     StatusUpdate,
 )
 
-HF_TOKEN = os.getenv("HF_TOKEN") or HfFolder.get_token()
+HF_TOKEN = huggingface_hub.get_token()
 
 
 @contextmanager
@@ -130,7 +128,7 @@ def test_private_space(self):
         space_id = "gradio-tests/not-actually-private-space"
         api = huggingface_hub.HfApi()
         assert api.space_info(space_id).private
-        client = Client(space_id)
+        client = Client(space_id, hf_token=HF_TOKEN)
         output = client.predict("abc", api_name="/predict")
         assert output == "abc"
 
@@ -141,6 +139,7 @@ def test_private_space_v4(self):
         assert api.space_info(space_id).private
         client = Client(
             space_id,
+            hf_token=HF_TOKEN,
         )
         output = client.predict("abc", api_name="/predict")
         assert output == "abc"
@@ -152,6 +151,7 @@ def test_private_space_v4_sse_v1(self):
         assert api.space_info(space_id).private
         client = Client(
             space_id,
+            hf_token=HF_TOKEN,
         )
         output = client.predict("abc", api_name="/predict")
         assert output == "abc"
@@ -1017,6 +1017,7 @@ def test_state_does_not_appear(self, state_demo):
     def test_private_space(self):
         client = Client(
             "gradio-tests/not-actually-private-space",
+            hf_token=HF_TOKEN,
         )
         assert len(client.endpoints) == 3
         assert len([e for e in client.endpoints.values() if e.is_valid]) == 2
@@ -1252,6 +1253,7 @@ class TestEndpoints:
     def test_upload(self):
         client = Client(
             src="gradio-tests/not-actually-private-file-upload",
+            hf_token=HF_TOKEN,
         )
         response = MagicMock(status_code=200)
         response.json.return_value = [

gradio/external.py

@@ -248,6 +248,7 @@ def custom_post_binary(data):
     elif p == "translation":
         inputs = components.Textbox(label="Input")
         outputs = components.Textbox(label="Translation")
+        postprocess = lambda x: x.translation_text  # noqa: E731
         examples = ["Hello, how are you?"]
         fn = client.translation
     # Example: facebook/bart-large-mnli

gradio/external_utils.py

@@ -9,7 +9,7 @@
 
 import httpx
 import yaml
-from huggingface_hub import HfApi, InferenceClient
+from huggingface_hub import HfApi, ImageClassificationOutputElement, InferenceClient
 
 from gradio import components
 
@@ -89,8 +89,8 @@ def rows_to_cols(incoming_data: dict) -> dict[str, dict[str, dict[str, list[str]
 ##################
 
 
-def postprocess_label(scores: list[dict[str, str | float]]) -> dict:
-    return {c["label"]: c["score"] for c in scores}
+def postprocess_label(scores: list[ImageClassificationOutputElement]) -> dict:
+    return {c.label: c.score for c in scores}
 
 
 def postprocess_mask_tokens(scores: list[dict[str, str | float]]) -> dict:

gradio/themes/base.py

@@ -188,7 +188,7 @@ def from_hub(cls, repo_name: str, hf_token: str | None = None):
 
         try:
             space_info = api.space_info(name)
-        except huggingface_hub.utils._errors.RepositoryNotFoundError as e:
+        except huggingface_hub.utils.RepositoryNotFoundError as e:
             raise ValueError(f"The space {name} does not exist") from e
 
         assets = get_theme_assets(space_info)

requirements.txt

@@ -4,7 +4,7 @@ fastapi<1.0
 ffmpy
 gradio_client==1.4.0-beta.3
 httpx>=0.24.1
-huggingface_hub>=0.22.0
+huggingface_hub>=0.25.1
 Jinja2<4.0
 markupsafe~=2.0
 numpy>=1.0,<3.0

test/requirements.txt

@@ -98,14 +98,6 @@ httpx==0.23.0
     #   gradio
     #   gradio-client
     #   respx
-huggingface-hub==0.21.4
-    # via
-    #   -r requirements.in
-    #   diffusers
-    #   gradio
-    #   gradio-client
-    #   tokenizers
-    #   transformers
 hypothesis==6.108.9
 idna==3.3
     # via

test/test_external.py

@@ -1,14 +1,13 @@
 import os
 import tempfile
 import textwrap
-import warnings
 from pathlib import Path
 from unittest.mock import MagicMock, patch
 
+import huggingface_hub
 import pytest
 from fastapi.testclient import TestClient
 from gradio_client import media_data
-from huggingface_hub import HfFolder
 
 import gradio as gr
 from gradio.context import Context
@@ -31,7 +30,7 @@
 
 os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
 
-HF_TOKEN = os.getenv("HF_TOKEN") or HfFolder.get_token()
+HF_TOKEN = huggingface_hub.get_token()
 
 
 class TestLoadInterface:
@@ -76,7 +75,7 @@ def test_text_generation(self):
     def test_summarization(self):
         model_type = "summarization"
         interface = gr.load(
-            "models/facebook/bart-large-cnn", hf_token=None, alias=model_type
+            "models/facebook/bart-large-cnn", hf_token=HF_TOKEN, alias=model_type
         )
         assert interface.__name__ == model_type
         assert interface.input_components and interface.output_components
@@ -86,7 +85,7 @@ def test_summarization(self):
     def test_translation(self):
         model_type = "translation"
         interface = gr.load(
-            "models/facebook/bart-large-cnn", hf_token=None, alias=model_type
+            "models/facebook/bart-large-cnn", hf_token=HF_TOKEN, alias=model_type
         )
         assert interface.__name__ == model_type
         assert interface.input_components and interface.output_components
@@ -96,7 +95,7 @@ def test_translation(self):
     def test_text2text_generation(self):
         model_type = "text2text-generation"
         interface = gr.load(
-            "models/sshleifer/tiny-mbart", hf_token=None, alias=model_type
+            "models/sshleifer/tiny-mbart", hf_token=HF_TOKEN, alias=model_type
         )
         assert interface.__name__ == model_type
         assert interface.input_components and interface.output_components
@@ -107,7 +106,7 @@ def test_text_classification(self):
         model_type = "text-classification"
         interface = gr.load(
             "models/distilbert-base-uncased-finetuned-sst-2-english",
-            hf_token=None,
+            hf_token=HF_TOKEN,
             alias=model_type,
         )
         assert interface.__name__ == model_type
@@ -117,7 +116,9 @@ def test_text_classification(self):
 
     def test_fill_mask(self):
         model_type = "fill-mask"
-        interface = gr.load("models/bert-base-uncased", hf_token=None, alias=model_type)
+        interface = gr.load(
+            "models/bert-base-uncased", hf_token=HF_TOKEN, alias=model_type
+        )
         assert interface.__name__ == model_type
         assert interface.input_components and interface.output_components
         assert isinstance(interface.input_components[0], gr.Textbox)
@@ -126,7 +127,7 @@ def test_fill_mask(self):
     def test_zero_shot_classification(self):
         model_type = "zero-shot-classification"
         interface = gr.load(
-            "models/facebook/bart-large-mnli", hf_token=None, alias=model_type
+            "models/facebook/bart-large-mnli", hf_token=HF_TOKEN, alias=model_type
         )
         assert interface.__name__ == model_type
         assert interface.input_components and interface.output_components
@@ -138,7 +139,7 @@ def test_zero_shot_classification(self):
     def test_automatic_speech_recognition(self):
         model_type = "automatic-speech-recognition"
         interface = gr.load(
-            "models/facebook/wav2vec2-base-960h", hf_token=None, alias=model_type
+            "models/facebook/wav2vec2-base-960h", hf_token=HF_TOKEN, alias=model_type
         )
         assert interface.__name__ == model_type
         assert interface.input_components and interface.output_components
@@ -148,7 +149,7 @@ def test_automatic_speech_recognition(self):
     def test_image_classification(self):
         model_type = "image-classification"
         interface = gr.load(
-            "models/google/vit-base-patch16-224", hf_token=None, alias=model_type
+            "models/google/vit-base-patch16-224", hf_token=HF_TOKEN, alias=model_type
         )
         assert interface.__name__ == model_type
         assert interface.input_components and interface.output_components
@@ -159,7 +160,7 @@ def test_feature_extraction(self):
         model_type = "feature-extraction"
         interface = gr.load(
             "models/sentence-transformers/distilbert-base-nli-mean-tokens",
-            hf_token=None,
+            hf_token=HF_TOKEN,
             alias=model_type,
         )
         assert interface.__name__ == model_type
@@ -171,7 +172,7 @@ def test_sentence_similarity(self):
         model_type = "text-to-speech"
         interface = gr.load(
             "models/julien-c/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train",
-            hf_token=None,
+            hf_token=HF_TOKEN,
             alias=model_type,
         )
         assert interface.__name__ == model_type
@@ -183,7 +184,7 @@ def test_text_to_speech(self):
         model_type = "text-to-speech"
         interface = gr.load(
             "models/julien-c/ljspeech_tts_train_tacotron2_raw_phn_tacotron_g2p_en_no_space_train",
-            hf_token=None,
+            hf_token=HF_TOKEN,
             alias=model_type,
         )
         assert interface.__name__ == model_type
@@ -204,22 +205,22 @@ def test_english_to_spanish_v4(self):
 
     def test_sentiment_model(self):
         io = gr.load(
-            "models/distilbert-base-uncased-finetuned-sst-2-english", hf_token=None
+            "models/distilbert-base-uncased-finetuned-sst-2-english", hf_token=HF_TOKEN
         )
         try:
             assert io("I am happy, I love you")["label"] == "POSITIVE"
         except TooManyRequestsError:
             pass
 
     def test_image_classification_model(self):
-        io = gr.load(name="models/google/vit-base-patch16-224", hf_token=None)
+        io = gr.load(name="models/google/vit-base-patch16-224", hf_token=HF_TOKEN)
         try:
             assert io("gradio/test_data/lion.jpg")["label"].startswith("lion")
         except TooManyRequestsError:
             pass
 
     def test_translation_model(self):
-        io = gr.load(name="models/t5-base", hf_token=None)
+        io = gr.load(name="models/t5-base", hf_token=HF_TOKEN)
         try:
             output = io("My name is Sarah and I live in London")
             assert output == "Mein Name ist Sarah und ich lebe in London"
@@ -239,47 +240,29 @@ def test_numerical_to_label_space(self):
             pass
 
     def test_visual_question_answering(self):
-        io = gr.load("models/dandelin/vilt-b32-finetuned-vqa", hf_token=None)
+        io = gr.load("models/dandelin/vilt-b32-finetuned-vqa", hf_token=HF_TOKEN)
         try:
             output = io("gradio/test_data/lion.jpg", "What is in the image?")
             assert isinstance(output, dict) and "label" in output
         except TooManyRequestsError:
             pass
 
     def test_image_to_text(self):
-        io = gr.load("models/nlpconnect/vit-gpt2-image-captioning", hf_token=None)
+        io = gr.load("models/nlpconnect/vit-gpt2-image-captioning", hf_token=HF_TOKEN)
         try:
             output = io("gradio/test_data/lion.jpg")
             assert isinstance(output, str)
         except TooManyRequestsError:
             pass
 
     def test_speech_recognition_model(self):
-        io = gr.load("models/facebook/wav2vec2-base-960h", hf_token=None)
+        io = gr.load("models/facebook/wav2vec2-base-960h", hf_token=HF_TOKEN)
         try:
             output = io("gradio/test_data/test_audio.wav")
             assert output is not None
         except TooManyRequestsError:
             pass
 
-        app, _, _ = io.launch(prevent_thread_lock=True, show_error=True)
-        client = TestClient(app)
-        resp = client.post(
-            "api/predict",
-            json={"fn_index": 0, "data": [media_data.BASE64_AUDIO], "name": "sample"},
-        )
-        try:
-            if resp.status_code != 200:
-                warnings.warn("Request for speech recognition model failed!")
-                assert (
-                    "Could not complete request to HuggingFace API"
-                    not in resp.json()["error"]
-                )
-            else:
-                assert resp.json()["data"] is not None
-        finally:
-            io.close()
-
     def test_private_space(self):
         io = gr.load(
             "spaces/gradio-tests/not-actually-private-spacev4-sse", hf_token=HF_TOKEN
@@ -329,7 +312,7 @@ def test_loading_files_via_proxy_works(self):
     def test_private_space_v4_sse_v1(self):
         io = gr.load(
             "spaces/gradio-tests/not-actually-private-spacev4-sse-v1",
-            hf_token=HfFolder.get_token(),
+            hf_token=HF_TOKEN,
         )
         try:
             output = io("abc")
@@ -359,7 +342,7 @@ def test_interface_load_cache_examples(self, tmp_path):
                     name="models/google/vit-base-patch16-224",
                     examples=[Path(test_file_dir, "cheetah1.jpg")],
                     cache_examples=True,
-                    hf_token=None,
+                    hf_token=HF_TOKEN,
                 )
             except TooManyRequestsError:
                 pass