login-button-attempt

README.md

@@ -4,9 +4,16 @@ emoji: 🐢🔍
 colorFrom: blue
 colorTo: indigo
 sdk: gradio
-sdk_version: 4.7.1
+sdk_version: 4.16.0
 app_file: app.py
 pinned: false
+
+hf_oauth: true
+# optional, default duration is 8 hours/480 minutes. Max duration is 30 days/43200 minutes.
+hf_oauth_expiration_minutes: 480
+# optional, see "Scopes" below. "openid profile" is always included.
+hf_oauth_scopes:
+ - inference-api
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app_debug.py

@@ -63,9 +63,9 @@ def get_queue_status():
         current = pipe.current
         if current is None:
             current = "None"
-        return f'<div style="padding-top: 5%">Current job: {html.escape(current)} <br/> Jobs in queue: <br/> {"".join(get_jobs_info_in_queue())}</div>'
+        return f'<div style="padding-top: 5%">Current job: {html.escape(current)} <br/> Job queue: <br/> {"".join(get_jobs_info_in_queue())}</div>'
     else:
-        return '<div style="padding-top: 5%">No jobs in queue, please submit an evaluation task from another tab.</div>'
+        return '<div style="padding-top: 5%">No jobs waiting, please submit an evaluation task from Text-Classification tab.</div>'
 
 
 def get_demo():

app_leaderboard.py

@@ -96,25 +96,25 @@ def get_demo(leaderboard_tab):
     display_df = get_display_df(default_df)  # the styled dataframe to display
 
     with gr.Row():
-        with gr.Column():
-          issue_columns_select = gr.CheckboxGroup(
-              label="Issue Columns",
-              choices=issue_columns,
-              value=[],
-              interactive=True,
-          )
         with gr.Column():
           info_columns_select = gr.CheckboxGroup(
               label="Info Columns",
               choices=info_columns,
               value=default_columns,
               interactive=True,
+        )
+        with gr.Column():
+          issue_columns_select = gr.CheckboxGroup(
+              label="Issue Columns",
+              choices=issue_columns,
+              value=[],
+              interactive=True,
           )
     
     with gr.Row():
         task_select = gr.Dropdown(
             label="Task",
-            choices=["text_classification", "tabular"],
+            choices=["text_classification"],
             value="text_classification",
             interactive=True,
         )

app_text_classification.py

@@ -8,7 +8,6 @@ from text_classification_ui_helpers import (
     align_columns_and_show_prediction,
     get_dataset_splits,
     check_dataset,
-    show_hf_token_info,
     precheck_model_ds_enable_example_btn,
     try_submit,
     empty_column_mapping,
@@ -20,9 +19,8 @@ import logging
 from wordings import (
   CONFIRM_MAPPING_DETAILS_MD, 
   INTRODUCTION_MD, 
-  USE_INFERENCE_API_TIP, 
+  LOG_IN_TIPS, 
   CHECK_LOG_SECTION_RAW,
-  HF_TOKEN_INVALID_STYLED
 )
 
 MAX_LABELS = 40
@@ -38,6 +36,10 @@ def get_demo():
         uid_label = gr.Textbox(
             label="Evaluation ID:", value=uuid.uuid4, visible=False, interactive=False
         )
+    
+    with gr.Accordion(label="Login to Use This Space", open=True):
+        gr.HTML(LOG_IN_TIPS)
+        gr.LoginButton()
     with gr.Row():
         model_id_input = gr.Textbox(
             label="Hugging Face Model id",
@@ -92,26 +94,7 @@ def get_demo():
                     for _ in range(MAX_LABELS, MAX_LABELS + MAX_FEATURES):
                         column_mappings.append(gr.Dropdown(visible=False))
 
-    with gr.Accordion(label="Model Wrap Advance Config", open=True):
-        gr.HTML(USE_INFERENCE_API_TIP)
-
-        run_inference = gr.Checkbox(value=True, label="Run with Inference API")
-        inference_token = gr.Textbox(
-            placeholder="hf_xxxxxxxxxxxxxxxxxxxx",
-            value="",
-            label="HF Token for Inference API",
-            visible=True,
-            interactive=True,
-        )
-        inference_token_info = gr.HTML(value=HF_TOKEN_INVALID_STYLED, visible=False)
-
-        inference_token.change(
-            fn=show_hf_token_info,
-            inputs=[inference_token],
-            outputs=[inference_token_info],
-        )
-
-    with gr.Accordion(label="Scanner Advance Config (optional)", open=False):
+    with gr.Accordion(label="Scanner Advanced Config (optional)", open=False):
         scanners = gr.CheckboxGroup(visible=True)
 
         @gr.on(triggers=[uid_label.change], inputs=[uid_label], outputs=[scanners])
@@ -145,7 +128,7 @@ def get_demo():
     with gr.Row():
         logs = gr.Textbox(
             value=CHECK_LOG_SECTION_RAW,
-            label="Giskard Bot Evaluation Guide:",
+            label="Log",
             visible=False,
             every=0.5,
         )
@@ -156,7 +139,7 @@ def get_demo():
     gr.on(
         triggers=[model_id_input.change],
         fn=get_related_datasets_from_leaderboard,
-        inputs=[model_id_input],
+        inputs=[model_id_input, dataset_id_input],
         outputs=[dataset_id_input],
     ).then(
         fn=check_dataset,
@@ -233,8 +216,6 @@ def get_demo():
             dataset_config_input,
             dataset_split_input,
             uid_label,
-            run_inference,
-            inference_token,
         ],
         outputs=[
             validation_result,
@@ -257,8 +238,6 @@ def get_demo():
             dataset_id_input,
             dataset_config_input,
             dataset_split_input,
-            run_inference,
-            inference_token,
             uid_label,
         ],
         outputs=[
@@ -274,15 +253,11 @@ def get_demo():
 
     gr.on(
         triggers=[
-            run_inference.input,
-            inference_token.input,
             scanners.input,
         ],
         fn=enable_run_btn,
         inputs=[
             uid_label,
-            run_inference, 
-            inference_token, 
             model_id_input, 
             dataset_id_input, 
             dataset_config_input, 
@@ -296,8 +271,6 @@ def get_demo():
         fn=enable_run_btn,
         inputs=[
             uid_label,
-            run_inference, 
-            inference_token, 
             model_id_input, 
             dataset_id_input, 
             dataset_config_input, 

requirements.txt

@@ -1,8 +1,9 @@
-giskard
+-e git+https://github.com/Giskard-AI/giskard.git@b4849c93e1b40f5597d6509c4e9c58b0fff10483#egg=giskard
 huggingface_hub
 hf-transfer
 torch==2.0.1
 transformers
 datasets
 tabulate
+gradio[oauth]
 -e git+https://github.com/Giskard-AI/cicd.git#egg=giskard-cicd

run_jobs.py

@@ -50,7 +50,6 @@ def prepare_env_and_get_command(
     d_id,
     config,
     split,
-    inference,
     inference_token,
     uid,
     label_mapping,
@@ -60,10 +59,6 @@ def prepare_env_and_get_command(
     if os.environ.get("SPACE_ID") == "giskardai/giskard-evaluator":
         leaderboard_dataset = LEADERBOARD
 
-    inference_type = "hf_pipeline"
-    if inference and inference_token:
-        inference_type = "hf_inference_api"
-
     executable = "giskard_scanner"
     try:
         # Copy the current requirements (might be changed)
@@ -100,9 +95,10 @@ def prepare_env_and_get_command(
         "--scan_config",
         get_submitted_yaml_path(uid),
         "--inference_type",
-        inference_type,
+        "hf_inference_api",
         "--inference_api_token",
         inference_token,
+        "--persist_scan",
     ]
     # The token to publish post
     if os.environ.get(HF_WRITE_TOKEN):

text_classification.py

@@ -102,9 +102,8 @@ def hf_inference_api(model_id, hf_token, payload):
     except Exception:
         return {"error": response.content}
     
-def preload_hf_inference_api(model_id):
+def preload_hf_inference_api(model_id, hf_token):
     payload = {"inputs": "This is a test", "options": {"use_cache": True, }}
-    hf_token = os.environ.get(HF_WRITE_TOKEN, default="")
     hf_inference_api(model_id, hf_token, payload)
 
 def check_model_pipeline(model_id):

text_classification_ui_helpers.py

@@ -21,7 +21,6 @@ from text_classification import (
     preload_hf_inference_api,
     get_example_prediction,
     get_labels_and_features_from_dataset,
-    check_hf_token_validity,
     HuggingFaceInferenceAPIResponse,
 )
 from wordings import (
@@ -43,7 +42,7 @@ MAX_FEATURES = 20
 ds_dict = None
 ds_config = None
 
-def get_related_datasets_from_leaderboard(model_id):
+def get_related_datasets_from_leaderboard(model_id, dataset_id_input):
     records = leaderboard.records
     model_id = strip_model_id_from_url(model_id)
     model_records = records[records["model_id"] == model_id]
@@ -52,7 +51,10 @@ def get_related_datasets_from_leaderboard(model_id):
     if len(datasets_unique) == 0:
         return gr.update(choices=[])
     
-    return gr.update(choices=datasets_unique)
+    if dataset_id_input in datasets_unique:
+        return gr.update(choices=datasets_unique)
+    
+    return gr.update(choices=datasets_unique, value="")
 
 
 logger = logging.getLogger(__file__)
@@ -179,11 +181,11 @@ def list_labels_and_features_from_dataset(ds_labels, ds_features, model_labels,
 
 
 def precheck_model_ds_enable_example_btn(
-    model_id, dataset_id, dataset_config, dataset_split
+    model_id, dataset_id, dataset_config, dataset_split, profile: gr.OAuthProfile | None, oath_token: gr.OAuthToken | None
 ):  
     model_id = strip_model_id_from_url(model_id)
     model_task = check_model_task(model_id)
-    preload_hf_inference_api(model_id)
+    preload_hf_inference_api(model_id, oath_token.token)
 
     if dataset_config is None or dataset_split is None or len(dataset_config) == 0:
         return (
@@ -249,8 +251,8 @@ def align_columns_and_show_prediction(
     dataset_config,
     dataset_split,
     uid,
-    run_inference,
-    inference_token,
+    profile: gr.OAuthProfile | None,
+    oauth_token: gr.OAuthToken | None,
 ):
     model_id = strip_model_id_from_url(model_id)
     model_task = check_model_task(model_id)
@@ -268,11 +270,9 @@ def align_columns_and_show_prediction(
     dropdown_placement = [
         gr.Dropdown(visible=False) for _ in range(MAX_LABELS + MAX_FEATURES)
     ]
-    
-    hf_token = os.environ.get(HF_WRITE_TOKEN, default="")
 
     prediction_input, prediction_response = get_example_prediction(
-        model_id, dataset_id, dataset_config, dataset_split, hf_token
+        model_id, dataset_id, dataset_config, dataset_split, oauth_token.token
     )
 
     if prediction_input is None or prediction_response is None:
@@ -344,7 +344,7 @@ def align_columns_and_show_prediction(
             gr.update(value=prediction_input, lines=min(len(prediction_input)//225 + 1, 5), visible=True),
             gr.update(value=prediction_response, visible=True),
             gr.update(visible=True, open=True),
-            gr.update(interactive=(run_inference and inference_token != "")),
+            gr.update(interactive=(profile is not None and oauth_token is not None)),
             "",
             *column_mappings,
         )
@@ -354,7 +354,7 @@ def align_columns_and_show_prediction(
         gr.update(value=prediction_input, lines=min(len(prediction_input)//225 + 1, 5), visible=True),
         gr.update(value=prediction_response, visible=True),
         gr.update(visible=True, open=False),
-        gr.update(interactive=(run_inference and inference_token != "")),
+        gr.update(interactive=(profile is not None and oauth_token is not None)),
         "",
         *column_mappings,
     )
@@ -372,9 +372,10 @@ def check_column_mapping_keys_validity(all_mappings):
 
     return True
 
-def enable_run_btn(uid, run_inference, inference_token, model_id, dataset_id, dataset_config, dataset_split):
-    if not run_inference or inference_token == "":
-        logger.warn("Inference API is not enabled")
+def enable_run_btn(uid, model_id, dataset_id, dataset_config, dataset_split, profile: gr.OAuthProfile | None, oath_token: gr.OAuthToken | None):
+    if profile is None:
+        return gr.update(interactive=False)
+    if oath_token is None:
         return gr.update(interactive=False)
     if model_id == "" or dataset_id == "" or dataset_config == "" or dataset_split == "":
         logger.warn("Model id or dataset id is not selected")
@@ -385,11 +386,6 @@ def enable_run_btn(uid, run_inference, inference_token, model_id, dataset_id, da
         logger.warn("Column mapping is not valid")
         return gr.update(interactive=False)
 
-    if not check_hf_token_validity(inference_token):
-        logger.warn("HF token is not valid")
-        return gr.update(interactive=False)
-    return gr.update(interactive=True)
-
 def construct_label_and_feature_mapping(all_mappings, ds_labels, ds_features, label_keys=None):
     label_mapping = {}
     if len(all_mappings["labels"].keys()) != len(ds_labels):
@@ -413,13 +409,7 @@ def construct_label_and_feature_mapping(all_mappings, ds_labels, ds_features, la
       feature_mapping.update({"label": label_keys[0]})
     return label_mapping, feature_mapping
 
-def show_hf_token_info(token):
-    valid = check_hf_token_validity(token)
-    if not valid:
-        return gr.update(visible=True)
-    return gr.update(visible=False)
-
-def try_submit(m_id, d_id, config, split, inference, inference_token, uid):
+def try_submit(m_id, d_id, config, split, uid, profile: gr.OAuthProfile | None, oath_token: gr.OAuthToken | None):
     all_mappings = read_column_mapping(uid)
     if not check_column_mapping_keys_validity(all_mappings):
         return (gr.update(interactive=True), gr.update(visible=False))
@@ -437,8 +427,7 @@ def try_submit(m_id, d_id, config, split, inference, inference_token, uid):
             d_id,
             config,
             split,
-            inference,
-            inference_token,
+            oath_token.token,
             uid,
             label_mapping,
             feature_mapping,

wordings.py

@@ -1,14 +1,16 @@
 INTRODUCTION_MD = """
-                <h1 style="text-align: center;">
+                <div style="display: flex; justify-content: center;"><h1>
                 🐢Giskard Evaluator - Text Classification
-                </h1>
+                </h1></div>
                 Welcome to the Giskard Evaluator Space! Get a model vulnerability report immediately by simply sharing your model and dataset id below. 
                 You can also checkout our library documentation <a href="https://docs.giskard.ai/en/latest/getting_started/quickstart/index.html">here</a>.
                 """
 CONFIRM_MAPPING_DETAILS_MD = """
+                            <div style="display: flex; justify-content: center;"><h1>
                             <h1 style="text-align: center;">
                             Confirm Pre-processing Details
                             </h1>
+                            </div>
                             Make sure the output variable's labels and the input variable's name are accurately mapped across both the dataset and the model. You can select the output variable's labels from the dropdowns below.
                             """
 CONFIRM_MAPPING_DETAILS_FAIL_MD = """
@@ -26,9 +28,7 @@ CHECK_CONFIG_OR_SPLIT_RAW = """
                             Please check your dataset config or split.
                             """
 
-CHECK_LOG_SECTION_RAW = """
-                          Your have successfully submitted a Giskard evaluation. Further details are available in the Logs tab. You can find your report will be posted to your model's community discussion.
-                        """
+CHECK_LOG_SECTION_RAW = """Your have successfully submitted a Giskard evaluation job. Further details are available in the Logs tab. You can find your report posted in your model's community discussion section."""
 
 PREDICTION_SAMPLE_MD = """
                         <h1 style="text-align: center;">
@@ -58,7 +58,11 @@ USE_INFERENCE_API_TIP = """
                 <a href="https://huggingface.co/docs/api-inference/detailed_parameters#text-classification-task">
                     Hugging Face Inference API
                 </a>
-                . Please input your <a href="https://huggingface.co/settings/tokens">Hugging Face token</a> to do so. You can find it <a href="https://huggingface.co/settings/tokens">here</a>.
+                . Please input your <a href="https://huggingface.co/docs/hub/security-tokens#user-access-tokens">Hugging Face token</a> to do so. You can find it <a href="https://huggingface.co/settings/tokens">here</a>.
+            """
+
+LOG_IN_TIPS = """
+            To use the Hugging Face Inference API, you need to log in to your Hugging Face account.
             """
 
 HF_TOKEN_INVALID_STYLED= """