fix log refresh

app.py

@@ -11,7 +11,7 @@ if threading.current_thread() is not threading.main_thread():
 try:
     with gr.Blocks(theme=gr.themes.Soft(primary_hue="green")) as demo:
         with gr.Tab("Text Classification"):
-            get_demo_text_classification()
+            get_demo_text_classification(demo)
         with gr.Tab("Leaderboard"):
             get_demo_leaderboard()
         

app_text_classification.py

@@ -1,6 +1,6 @@
 import gradio as gr
 import uuid
-from io_utils import read_scanners, write_scanners, read_inference_type, write_inference_type
+from io_utils import read_scanners, write_scanners, read_inference_type, write_inference_type, get_logs_file
 from wordings import INTRODUCTION_MD, CONFIRM_MAPPING_DETAILS_MD
 from text_classification_ui_helpers import try_submit, check_dataset_and_get_config, check_dataset_and_get_split, check_model_and_show_prediction, write_column_mapping_to_config, get_logs_file
 
@@ -11,126 +11,125 @@ EXAMPLE_MODEL_ID = 'cardiffnlp/twitter-roberta-base-sentiment-latest'
 EXAMPLE_DATA_ID = 'tweet_eval'
 CONFIG_PATH='./config.yaml'
 
-def get_demo():
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown(INTRODUCTION_MD)
-        with gr.Row():
-            model_id_input = gr.Textbox(
-                label="Hugging Face model id",
-                placeholder=EXAMPLE_MODEL_ID + " (press enter to confirm)",
-            )
+def get_demo(demo):
+    with gr.Row():
+        gr.Markdown(INTRODUCTION_MD)
+    with gr.Row():
+        model_id_input = gr.Textbox(
+            label="Hugging Face model id",
+            placeholder=EXAMPLE_MODEL_ID + " (press enter to confirm)",
+        )
 
-            dataset_id_input = gr.Textbox(
-                label="Hugging Face Dataset id",
-                placeholder=EXAMPLE_DATA_ID + " (press enter to confirm)",
-            )
-        
-        with gr.Row():
-            dataset_config_input = gr.Dropdown(label='Dataset Config', visible=False)
-            dataset_split_input = gr.Dropdown(label='Dataset Split', visible=False)
-        
-        with gr.Row():
-            example_input = gr.Markdown('Example Input', visible=False)
-        with gr.Row():
-            example_prediction = gr.Label(label='Model Prediction Sample', visible=False)
-        
-        with gr.Row():
-            with gr.Accordion(label='Label and Feature Mapping', visible=False, open=False) as column_mapping_accordion:
-                with gr.Row():
-                    gr.Markdown(CONFIRM_MAPPING_DETAILS_MD)
-                column_mappings = []
-                with gr.Row():
-                    with gr.Column():
-                        for _ in range(MAX_LABELS):
-                            column_mappings.append(gr.Dropdown(visible=False))
-                    with gr.Column():    
-                        for _ in range(MAX_LABELS, MAX_LABELS + MAX_FEATURES):
-                            column_mappings.append(gr.Dropdown(visible=False))
-        
-        with gr.Accordion(label='Model Wrap Advance Config (optional)', open=False):
-            run_local = gr.Checkbox(value=True, label="Run in this Space")
-            use_inference = read_inference_type('./config.yaml') == 'hf_inference_api'
-            run_inference = gr.Checkbox(value=use_inference, label="Run with Inference API")
-        
-        with gr.Accordion(label='Scanner Advance Config (optional)', open=False):
-            selected = read_scanners('./config.yaml')
-            # currently we remove data_leakage from the default scanners
-            # Reason: data_leakage barely raises any issues and takes too many requests
-            # when using inference API, causing rate limit error
-            scan_config = selected + ['data_leakage']
-            scanners = gr.CheckboxGroup(choices=scan_config, value=selected, label='Scan Settings', visible=True)
+        dataset_id_input = gr.Textbox(
+            label="Hugging Face Dataset id",
+            placeholder=EXAMPLE_DATA_ID + " (press enter to confirm)",
+        )
+    
+    with gr.Row():
+        dataset_config_input = gr.Dropdown(label='Dataset Config', visible=False)
+        dataset_split_input = gr.Dropdown(label='Dataset Split', visible=False)
+    
+    with gr.Row():
+        example_input = gr.Markdown('Example Input', visible=False)
+    with gr.Row():
+        example_prediction = gr.Label(label='Model Prediction Sample', visible=False)
+    
+    with gr.Row():
+        with gr.Accordion(label='Label and Feature Mapping', visible=False, open=False) as column_mapping_accordion:
+            with gr.Row():
+                gr.Markdown(CONFIRM_MAPPING_DETAILS_MD)
+            column_mappings = []
+            with gr.Row():
+                with gr.Column():
+                    for _ in range(MAX_LABELS):
+                        column_mappings.append(gr.Dropdown(visible=False))
+                with gr.Column():    
+                    for _ in range(MAX_LABELS, MAX_LABELS + MAX_FEATURES):
+                        column_mappings.append(gr.Dropdown(visible=False))
+    
+    with gr.Accordion(label='Model Wrap Advance Config (optional)', open=False):
+        run_local = gr.Checkbox(value=True, label="Run in this Space")
+        use_inference = read_inference_type('./config.yaml') == 'hf_inference_api'
+        run_inference = gr.Checkbox(value=use_inference, label="Run with Inference API")
+    
+    with gr.Accordion(label='Scanner Advance Config (optional)', open=False):
+        selected = read_scanners('./config.yaml')
+        # currently we remove data_leakage from the default scanners
+        # Reason: data_leakage barely raises any issues and takes too many requests
+        # when using inference API, causing rate limit error
+        scan_config = selected + ['data_leakage']
+        scanners = gr.CheckboxGroup(choices=scan_config, value=selected, label='Scan Settings', visible=True)
 
-        with gr.Row():
-            run_btn = gr.Button(
-                "Get Evaluation Result",
-                variant="primary",
-                interactive=True,
-                size="lg",
-            )
+    with gr.Row():
+        run_btn = gr.Button(
+            "Get Evaluation Result",
+            variant="primary",
+            interactive=True,
+            size="lg",
+        )
+    
+    with gr.Row():
+        uid = uuid.uuid4()
+        uid_label = gr.Textbox(label="Evaluation ID:", value=uid, visible=False, interactive=False)
+        logs = gr.Textbox(label="Giskard Bot Evaluation Log:", visible=False)
+        demo.load(get_logs_file, uid_label, logs, every=0.5)
         
-        with gr.Row():
-            uid = uuid.uuid4()
-            uid_label = gr.Textbox(label="Evaluation ID:", value=uid, visible=False)
-            logs = gr.Textbox(label="Giskard Bot Evaluation Log:", visible=False)
-            demo.load(get_logs_file, uid_label, logs, every=0.5)
-            
-        gr.on(triggers=[label.change for label in column_mappings], 
-            fn=write_column_mapping_to_config,
-            inputs=[dataset_id_input, dataset_config_input, dataset_split_input, *column_mappings])
+    gr.on(triggers=[label.change for label in column_mappings], 
+        fn=write_column_mapping_to_config,
+        inputs=[dataset_id_input, dataset_config_input, dataset_split_input, *column_mappings])
 
-        gr.on(triggers=[model_id_input.change, dataset_config_input.change, dataset_split_input.change],
-            fn=check_model_and_show_prediction,
-            inputs=[model_id_input, dataset_id_input, dataset_config_input, dataset_split_input], 
-            outputs=[example_input, example_prediction, column_mapping_accordion, *column_mappings])
+    gr.on(triggers=[model_id_input.change, dataset_config_input.change, dataset_split_input.change],
+        fn=check_model_and_show_prediction,
+        inputs=[model_id_input, dataset_id_input, dataset_config_input, dataset_split_input], 
+        outputs=[example_input, example_prediction, column_mapping_accordion, *column_mappings])
 
-        dataset_id_input.blur(check_dataset_and_get_config, dataset_id_input, dataset_config_input)
+    dataset_id_input.blur(check_dataset_and_get_config, dataset_id_input, dataset_config_input)
 
-        dataset_config_input.change(
-            check_dataset_and_get_split, 
-            inputs=[dataset_id_input, dataset_config_input], 
-            outputs=[dataset_split_input])
+    dataset_config_input.change(
+        check_dataset_and_get_split, 
+        inputs=[dataset_id_input, dataset_config_input], 
+        outputs=[dataset_split_input])
 
-        scanners.change(
-            write_scanners,
-            inputs=scanners
-        )
+    scanners.change(
+        write_scanners,
+        inputs=scanners
+    )
 
-        run_inference.change(
-            write_inference_type,
-            inputs=[run_inference]
-        )
+    run_inference.change(
+        write_inference_type,
+        inputs=[run_inference]
+    )
 
-        gr.on(
-            triggers=[
-                run_btn.click,
-                ],
-            fn=try_submit,
-            inputs=[
-                model_id_input, 
-                dataset_id_input, 
-                dataset_config_input, 
-                dataset_split_input, 
-                run_local, 
-                uid_label],
-            outputs=[run_btn, logs])
-        
-        def enable_run_btn():
-            return gr.update(interactive=True)
-        gr.on(
-            triggers=[
-                    model_id_input.change, 
-                    dataset_config_input.change, 
-                    dataset_split_input.change, 
-                    run_inference.change, 
-                    run_local.change, 
-                    scanners.change],
-            fn=enable_run_btn,
-            inputs=None,
-            outputs=[run_btn])
-        
-        gr.on(
-            triggers=[label.change for label in column_mappings],
-            fn=enable_run_btn,
-            inputs=None,
-            outputs=[run_btn])
+    gr.on(
+        triggers=[
+            run_btn.click,
+            ],
+        fn=try_submit,
+        inputs=[
+            model_id_input, 
+            dataset_id_input, 
+            dataset_config_input, 
+            dataset_split_input, 
+            run_local,
+            uid_label],
+        outputs=[run_btn, logs])
+    
+    def enable_run_btn():
+        return (gr.update(interactive=True))
+    gr.on(
+        triggers=[
+                model_id_input.change, 
+                dataset_config_input.change, 
+                dataset_split_input.change, 
+                run_inference.change, 
+                run_local.change, 
+                scanners.change],
+        fn=enable_run_btn,
+        inputs=None,
+        outputs=[run_btn])
+    
+    gr.on(
+        triggers=[label.change for label in column_mappings],
+        fn=enable_run_btn,
+        inputs=None,
+        outputs=[run_btn])

io_utils.py

@@ -52,7 +52,8 @@ def read_column_mapping(path):
     column_mapping = {}
     with open(path, "r") as f:
         config = yaml.load(f, Loader=yaml.FullLoader)
-        column_mapping = config.get("column_mapping", dict())
+        if config:
+            column_mapping = config.get("column_mapping", dict())
     return column_mapping
 
 # write column mapping to yaml file
@@ -77,6 +78,13 @@ def convert_column_mapping_to_json(df, label=""):
         column_mapping[label].append(row.tolist())
     return column_mapping
 
+def get_logs_file(uid):
+    try:
+        file = open(f"./tmp/{uid}_log", "r")
+        return file.read()
+    except Exception:
+        return "Log file does not exist"
+
 def write_log_to_user_file(id, log):
     with open(f"./tmp/{id}_log", "a") as f:
         f.write(log)

text_classification_ui_helpers.py

@@ -43,6 +43,9 @@ def write_column_mapping_to_config(dataset_id, dataset_config, dataset_split, *l
     labels = [*labels]
     all_mappings = read_column_mapping(CONFIG_PATH)
 
+    if all_mappings is None:
+        all_mappings = dict()
+
     if "labels" not in all_mappings.keys():
         all_mappings["labels"] = dict()
     for i, label in enumerate(labels[:MAX_LABELS]):
@@ -58,7 +61,9 @@ def write_column_mapping_to_config(dataset_id, dataset_config, dataset_split, *l
 
 def list_labels_and_features_from_dataset(ds_labels, ds_features, model_id2label):
     model_labels = list(model_id2label.values())
-    lables = [gr.Dropdown(label=f"{label}", choices=model_labels, value=model_id2label[i], interactive=True, visible=True) for i, label in enumerate(ds_labels[:MAX_LABELS])]
+    len_model_labels = len(model_labels)
+    print(model_labels, model_id2label, 3%len_model_labels)
+    lables = [gr.Dropdown(label=f"{label}", choices=model_labels, value=model_id2label[i%len_model_labels], interactive=True, visible=True) for i, label in enumerate(ds_labels[:MAX_LABELS])]
     lables += [gr.Dropdown(visible=False) for _ in range(MAX_LABELS - len(lables))]
     # TODO: Substitute 'text' with more features for zero-shot
     features = [gr.Dropdown(label=f"{feature}", choices=ds_features, value=ds_features[0], interactive=True, visible=True) for feature in ['text']]
@@ -90,7 +95,7 @@ def check_model_and_show_prediction(model_id, dataset_id, dataset_config, datase
     
     # when dataset does not have labels or features
     if not isinstance(ds_labels, list) or not isinstance(ds_features, list):
-        gr.Warning(CONFIRM_MAPPING_DETAILS_FAIL_RAW)
+        # gr.Warning(CONFIRM_MAPPING_DETAILS_FAIL_RAW)
         return (
             gr.update(visible=False),
             gr.update(visible=False),
@@ -123,29 +128,21 @@ def check_model_and_show_prediction(model_id, dataset_id, dataset_config, datase
         *column_mappings
     )
 
-def get_logs_file(uid):
-    print("read log file")
-    file = open(f"./tmp/{uid}_log", "r")
-    contents = file.readlines()
-    print(contents)
-    file.close()
-    return '\n'.join(contents)
-
 def try_submit(m_id, d_id, config, split, local, uid):
     all_mappings = read_column_mapping(CONFIG_PATH)
 
     if all_mappings is None:
         gr.Warning(CONFIRM_MAPPING_DETAILS_FAIL_RAW)
-        return gr.update(interactive=True)
+        return (gr.update(interactive=True), gr.update(visible=False))
 
     if "labels" not in all_mappings.keys():
         gr.Warning(CONFIRM_MAPPING_DETAILS_FAIL_RAW)
-        return gr.update(interactive=True)
+        return (gr.update(interactive=True), gr.update(visible=False))
     label_mapping = all_mappings["labels"]
     
     if "features" not in all_mappings.keys():
         gr.Warning(CONFIRM_MAPPING_DETAILS_FAIL_RAW)
-        return gr.update(interactive=True)
+        return (gr.update(interactive=True), gr.update(visible=False))
     feature_mapping = all_mappings["features"]
 
     # TODO: Set column mapping for some dataset such as `amazon_polarity`
@@ -175,7 +172,7 @@ def try_submit(m_id, d_id, config, split, local, uid):
 
         return (
             gr.update(interactive=False),
-            gr.update(value=get_logs_file(uid),lines=5, visible=True, interactive=False))
+            gr.update(lines=5, visible=True, interactive=False))
 
     else:
         gr.Info("TODO: Submit task to an endpoint")