Update app.py

app.py

@@ -21,7 +21,7 @@ from src.vis_utils import *
 from src.bin.PROBE import run_probe
 
 # ------------------------------------------------------------------
-# Helper functions moved / added here so that UI callbacks can see them
+# Helper functions --------------------------------------------------
 # ------------------------------------------------------------------
 
 def add_new_eval(
@@ -40,7 +40,6 @@ def add_new_eval(
     if any(task in benchmark_types for task in ['similarity', 'family', 'function']) and human_file is None:
         gr.Warning("Human representations are required for similarity, family, or function benchmarks!")
         return -1
-    
     if 'affinity' in benchmark_types and skempi_file is None:
         gr.Warning("SKEMPI representations are required for affinity benchmark!")
         return -1
@@ -77,20 +76,17 @@ def refresh_data():
     """Re‑start the space and pull fresh leaderboard CSVs from the HF Hub."""
     api.restart_space(repo_id=repo_id)
     benchmark_types = ["similarity", "function", "family", "affinity", "leaderboard"]
-
     for benchmark_type in benchmark_types:
         path = f"/tmp/{benchmark_type}_results.csv"
         if os.path.exists(path):
             os.remove(path)
-
     benchmark_types.remove("leaderboard")
     download_from_hub(benchmark_types)
 
 
-# ------- Leaderboard helpers -------------------------------------------------
+# ------- Leaderboard helpers -----------------------------------------------
 
 def update_metrics(selected_benchmarks):
-    """Populate metric selector according to chosen benchmark types."""
     updated_metrics = set()
     for benchmark in selected_benchmarks:
         updated_metrics.update(benchmark_metric_mapping.get(benchmark, []))
@@ -98,50 +94,33 @@ def update_metrics(selected_benchmarks):
 
 
 def update_leaderboard(selected_methods, selected_metrics):
-    updated_df = get_baseline_df(selected_methods, selected_metrics)
-    return updated_df
+    return get_baseline_df(selected_methods, selected_metrics)
 
-# ------- Visualisation helpers ----------------------------------------------
+# ------- Visualisation helpers ---------------------------------------------
 
 def get_plot_explanation(benchmark_type, x_metric, y_metric, aspect, dataset, single_metric):
-    """Return a short natural‑language explanation for the produced plot."""
     if benchmark_type == "similarity":
         return (
-            f"The scatter plot compares models on **{x_metric}** (x‑axis) and "
-            f"**{y_metric}** (y‑axis). Points further to the upper‑right indicate better "
-            "performance on both metrics."
+            f"Scatter plot compares models on **{x_metric}** (x‑axis) and **{y_metric}** (y‑axis). "
+            "Upper‑right points indicate jointly strong performance."
         )
-    elif benchmark_type == "function":
+    if benchmark_type == "function":
         return (
-            f"The heat‑map shows performance of each model (columns) across GO terms "
-            f"for the **{aspect.upper()}** aspect using the **{single_metric}** metric. "
-            "Darker squares correspond to stronger performance; hierarchical clustering "
-            "groups similar models and tasks together."
+            f"Heat‑map shows model scores for **{aspect.upper()}** terms with **{single_metric}**. "
+            "Darker squares → better predictions."
         )
-    elif benchmark_type == "family":
+    if benchmark_type == "family":
         return (
-            f"The horizontal box‑plots summarise cross‑validation performance on the "
-            f"**{dataset}** dataset. Higher median MCC values indicate better family‑"
-            "classification accuracy."
+            f"Box‑plots summarise cross‑fold MCC on **{dataset}**; higher medians are better."
         )
-    elif benchmark_type == "affinity":
+    if benchmark_type == "affinity":
         return (
-            f"Each box‑plot shows the distribution of **{single_metric}** scores for every "
-            "model when predicting binding affinity changes. Higher values are better."
+            f"Box‑plots display distribution of **{single_metric}** scores for affinity prediction; higher values are better."
         )
     return ""
 
 
-def generate_plot_and_explanation(
-    benchmark_type,
-    methods_selected,
-    x_metric,
-    y_metric,
-    aspect,
-    dataset,
-    single_metric,
-):
-    """Callback wrapper that returns both the image path and a textual explanation."""
+def generate_plot_and_explanation(benchmark_type, methods_selected, x_metric, y_metric, aspect, dataset, single_metric):
     plot_path = benchmark_plot(
         benchmark_type,
         methods_selected,
@@ -154,10 +133,34 @@ def generate_plot_and_explanation(
     explanation = get_plot_explanation(benchmark_type, x_metric, y_metric, aspect, dataset, single_metric)
     return plot_path, explanation
 
-# ------------------------------------------------------------------
+# ---------------------------------------------------------------------------
+# Custom CSS for frozen first column and clearer table styles
+# ---------------------------------------------------------------------------
+CUSTOM_CSS = """
+/* freeze first column */
+#leaderboard-table thead th:first-child,
+#leaderboard-table tbody td:first-child {
+  position: sticky;
+  left: 0;
+  background: white;
+  z-index: 2;
+}
+
+/* striped rows for readability */
+#leaderboard-table tbody tr:nth-child(odd) {
+  background: #fafafa;
+}
+
+/* centre numeric cells */
+#leaderboard-table td:not(:first-child) {
+  text-align: center;
+}
+"""
+
+# ---------------------------------------------------------------------------
 # UI definition
-# ------------------------------------------------------------------
-block = gr.Blocks()
+# ---------------------------------------------------------------------------
+block = gr.Blocks(css=CUSTOM_CSS)
 
 with block:
     gr.Markdown(LEADERBOARD_INTRODUCTION)
@@ -167,23 +170,28 @@ with block:
         # 1️⃣  Leaderboard tab
         # ------------------------------------------------------------------
         with gr.TabItem("🏅 PROBE Leaderboard", elem_id="probe-benchmark-tab-table", id=1):
-            leaderboard = get_baseline_df(None, None)  # baseline leaderboard without filtering
+            # small workflow figure at top
+            gr.Image(
+                value="./src/data/PROBE_workflow_figure.jpg",
+                show_label=False,
+                height=150,
+                container=False,
+            )
 
+            leaderboard = get_baseline_df(None, None)
             method_names = leaderboard['Method'].unique().tolist()
-            metric_names = leaderboard.columns.tolist()
-            metric_names.remove('Method')  # remove non‑metric column
+            metric_names = leaderboard.columns.tolist(); metric_names.remove('Method')
 
             benchmark_metric_mapping = {
                 "similarity": [m for m in metric_names if m.startswith('sim_')],
-                "function": [m for m in metric_names if m.startswith('func')],
-                "family": [m for m in metric_names if m.startswith('fam_')],
-                "affinity": [m for m in metric_names if m.startswith('aff_')],
+                "function":  [m for m in metric_names if m.startswith('func')],
+                "family":    [m for m in metric_names if m.startswith('fam_')],
+                "affinity":  [m for m in metric_names if m.startswith('aff_')],
             }
 
-            # selectors -----------------------------------------------------
             leaderboard_method_selector = gr.CheckboxGroup(
                 choices=method_names,
-                label="Select Methods for the Leaderboard",
+                label="Select Methods",
                 value=method_names,
                 interactive=True,
             )
@@ -197,15 +205,14 @@ with block:
 
             leaderboard_metric_selector = gr.CheckboxGroup(
                 choices=metric_names,
-                label="Select Metrics for the Leaderboard",
+                label="Select Metrics",
                 value=None,
                 interactive=True,
             )
 
-            # leaderboard table --------------------------------------------
             baseline_value = get_baseline_df(method_names, metric_names)
             baseline_value = baseline_value.applymap(lambda x: round(x, 4) if isinstance(x, (int, float)) else x)
-            baseline_header = ["Method"] + metric_names
+            baseline_header  = ["Method"] + metric_names
             baseline_datatype = ['markdown'] + ['number'] * len(metric_names)
 
             with gr.Row(show_progress=True, variant='panel'):
@@ -215,22 +222,21 @@ with block:
                     type="pandas",
                     datatype=baseline_datatype,
                     interactive=False,
-                    visible=True,
+                    elem_id="leaderboard-table",
+                    height=600,  # make table longer
                 )
 
-            # callbacks -----------------------------------------------------
+            # callbacks
             leaderboard_method_selector.change(
                 get_baseline_df,
                 inputs=[leaderboard_method_selector, leaderboard_metric_selector],
                 outputs=data_component,
             )
-
             benchmark_type_selector_lb.change(
                 lambda selected: update_metrics(selected),
                 inputs=[benchmark_type_selector_lb],
                 outputs=leaderboard_metric_selector,
             )
-
             leaderboard_metric_selector.change(
                 get_baseline_df,
                 inputs=[leaderboard_method_selector, leaderboard_metric_selector],
@@ -238,58 +244,36 @@ with block:
             )
 
         # ------------------------------------------------------------------
-        # 2️⃣ Visualisation tab
+        # 2️⃣  Visualisation tab
         # ------------------------------------------------------------------
-        with gr.TabItem("📊 Visualization", elem_id="probe-benchmark-tab-visualization", id=2):
-            # Intro / instructions
+        with gr.TabItem("📊 Visualizations", elem_id="probe-benchmark-tab-visualization", id=2):
             gr.Markdown(
-                """
-                ## **Interactive Visualizations**
-                Select a benchmark type first; context‑specific options will appear automatically.  
-                Once your parameters are set, click **Plot** to generate the figure.
-
-                **How to read the plots**
-                * **Similarity (scatter)** – Each point is a model. Points nearer the top‑right perform well on both chosen similarity metrics.
-                * **Function prediction (heat‑map)** – Darker squares denote better scores. Rows/columns are clustered to reveal shared structure.
-                * **Family / Affinity (boxplots)** – Boxes summarise distribution across folds/targets. Higher medians indicate stronger performance.
-                """,
+                """## **Interactive Visualizations**  
+                Choose a benchmark type; context‑specific options will appear. Click **Plot** and an explanation will follow the figure.""",
                 elem_classes="markdown-text",
             )
-
-            # ------------------------------------------------------------------
-            # selectors specific to visualisation
-            # ------------------------------------------------------------------
             vis_benchmark_type_selector = gr.Dropdown(
                 choices=list(benchmark_specific_metrics.keys()),
-                label="Select Benchmark Type",
+                label="Benchmark Type",
                 value=None,
             )
-
             with gr.Row():
-                vis_x_metric_selector = gr.Dropdown(choices=[], label="Select X‑axis Metric", visible=False)
-                vis_y_metric_selector = gr.Dropdown(choices=[], label="Select Y‑axis Metric", visible=False)
-                vis_aspect_type_selector = gr.Dropdown(choices=[], label="Select Aspect Type", visible=False)
-                vis_dataset_selector = gr.Dropdown(choices=[], label="Select Dataset", visible=False)
-                vis_single_metric_selector = gr.Dropdown(choices=[], label="Select Metric", visible=False)
-
+                vis_x_metric_selector = gr.Dropdown(choices=[], label="X‑axis Metric", visible=False)
+                vis_y_metric_selector = gr.Dropdown(choices=[], label="Y‑axis Metric", visible=False)
+                vis_aspect_type_selector = gr.Dropdown(choices=[], label="Aspect", visible=False)
+                vis_dataset_selector = gr.Dropdown(choices=[], label="Dataset", visible=False)
+                vis_single_metric_selector = gr.Dropdown(choices=[], label="Metric", visible=False)
             vis_method_selector = gr.CheckboxGroup(
                 choices=method_names,
-                label="Select methods to visualize",
-                interactive=True,
+                label="Methods",
                 value=method_names,
+                interactive=True,
             )
-
             plot_button = gr.Button("Plot")
-
             with gr.Row(show_progress=True, variant='panel'):
                 plot_output = gr.Image(label="Plot")
-
-            # textual explanation below the image
             plot_explanation = gr.Markdown(visible=False)
-
-            # ------------------------------------------------------------------
-            # callbacks for visualisation tab
-            # ------------------------------------------------------------------
+            # callbacks
             vis_benchmark_type_selector.change(
                 update_metric_choices,
                 inputs=[vis_benchmark_type_selector],
@@ -301,7 +285,6 @@ with block:
                     vis_single_metric_selector,
                 ],
             )
-
             plot_button.click(
                 generate_plot_and_explanation,
                 inputs=[
@@ -335,53 +318,21 @@ with block:
         with gr.TabItem("🚀 Submit here! ", elem_id="probe-benchmark-tab-table", id=4):
             with gr.Row():
                 gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
-
             with gr.Row():
                 gr.Markdown("# ✉️✨ Submit your model's representation files here!", elem_classes="markdown-text")
-
             with gr.Row():
                 with gr.Column():
                     model_name_textbox = gr.Textbox(label="Method name")
                     revision_name_textbox = gr.Textbox(label="Revision Method Name")
-                    
-                    benchmark_types = gr.CheckboxGroup(
-                        choices=TASK_INFO,
-                        label="Benchmark Types",
-                        interactive=True,
-                    )
-                    similarity_tasks = gr.CheckboxGroup(
-                        choices=similarity_tasks_options,
-                        label="Similarity Tasks",
-                        interactive=True,
-                    )
-                    
-                    function_prediction_aspect = gr.Radio(
-                        choices=function_prediction_aspect_options,
-                        label="Function Prediction Aspects",
-                        interactive=True,
-                    )
-                    
-                    family_prediction_dataset = gr.CheckboxGroup(
-                        choices=family_prediction_dataset_options,
-                        label="Family Prediction Datasets",
-                        interactive=True,
-                    )
-                    
-                    function_dataset = gr.Textbox(
-                        label="Function Prediction Datasets",
-                        visible=False,
-                        value="All_Data_Sets",
-                    )
-
-                    save_checkbox = gr.Checkbox(
-                        label="Save results for leaderboard and visualization",
-                        value=True,
-                    )
-
+                    benchmark_types = gr.CheckboxGroup(choices=TASK_INFO, label="Benchmark Types", interactive=True)
+                    similarity_tasks = gr.CheckboxGroup(choices=similarity_tasks_options, label="Similarity Tasks", interactive=True)
+                    function_prediction_aspect = gr.Radio(choices=function_prediction_aspect_options, label="Function Prediction Aspects", interactive=True)
+                    family_prediction_dataset = gr.CheckboxGroup(choices=family_prediction_dataset_options, label="Family Prediction Datasets", interactive=True)
+                    function_dataset = gr.Textbox(label="Function Prediction Datasets", visible=False, value="All_Data_Sets")
+                    save_checkbox = gr.Checkbox(label="Save results for leaderboard and visualization", value=True)
             with gr.Row():
                 human_file = gr.File(label="Representation file (CSV) for Human dataset", file_count="single", type='filepath')
                 skempi_file = gr.File(label="Representation file (CSV) for SKEMPI dataset", file_count="single", type='filepath')
-
             submit_button = gr.Button("Submit Eval")
             submission_result = gr.Markdown()
             submit_button.click(
@@ -400,9 +351,7 @@ with block:
                 ],
             )
 
-    # ----------------------------------------------------------------------
-    # global refresh button & citation accordion
-    # ----------------------------------------------------------------------
+    # global refresh + citation ---------------------------------------------
     with gr.Row():
         data_run = gr.Button("Refresh")
         data_run.click(refresh_data, outputs=[data_component])
@@ -415,5 +364,5 @@ with block:
             show_copy_button=True,
         )
 
-# -----------------------------------------------------------------------------
+# ---------------------------------------------------------------------------
 block.launch()