change column names

app.py

@@ -1,5 +1,4 @@
 import os
-import datetime as dt
 from typing import List
 
 import gradio as gr
@@ -12,29 +11,26 @@ CONFIGS = {'antibody':'Antibody–Antigen', 'ligand':'Allosteric–Orthosteric',
 # Column schemas per tab (used to create empty frames and to order columns)
 COLUMNS = {
     "antibody": [
-        "timestamp",
-        "user",
-        "model_name",
-        "antibody_id",
-        "antigen_id",
-        "predicted_affinity",
-        "notes",
+        "group_name",
+        "successful",
+        "high",
+        "medium",
+        "acceptable",
     ],
     "ligand": [
-        "timestamp",
-        "user",
-        "model_name",
-        "protein_id",
-        "ligand_type",
-        "predicted_kd",
-        "notes",
+        "group_name",
+        "rmsd_top1_all",
+        "rmsd_top1_allosteric",
+        "rmsd_top1_orthosteric",
+        "rmsd_top5_all",
+        "structures_under_2a",
     ],
     "final": [
-        "timestamp",
-        "team_name",
-        "archive_url",
-        "results_summary",
-        "contact_email",
+        "group_name",
+        "repository_url",
+        "commit_sha",
+        "challenge_type",
+        "description_link",
     ],
 }
 
@@ -83,19 +79,20 @@ def push_df(config: str, df: pd.DataFrame) -> None:
 # --- Tab logic --------------------------------------------------------------
 # Antibody–Antigen
 
-def submit_antibody(user, model_name, antibody_id, antigen_id, predicted_affinity, notes):
+def submit_antibody(group_name, successful, high, medium, acceptable):
     config = CONFIGS["antibody"]
     cols = COLUMNS["antibody"]
     df = load_df(config, cols)
     row = {
-        "timestamp": dt.datetime.now(dt.timezone.utc).isoformat(),
-        "user": user or "",
-        "model_name": model_name or "",
-        "antibody_id": antibody_id or "",
-        "antigen_id": antigen_id or "",
-        "predicted_affinity": float(predicted_affinity) if predicted_affinity is not None else None,
-        "notes": notes or "",
+        "group_name": group_name or "",
+        "successful": int(successful) if successful is not None else 0,
+        "high": int(high) if high is not None else 0,
+        "medium": int(medium) if medium is not None else 0,
+        "acceptable": int(acceptable) if acceptable is not None else 0,
     }
+    # Overwrite if group_name already exists
+    if group_name and not df.empty:
+        df = df[df["group_name"] != group_name]
     df = pd.concat([df, pd.DataFrame([row])], ignore_index=True)
     push_df(config, df)
     # Re-load to ensure what we show is exactly what's on the Hub
@@ -108,19 +105,21 @@ def refresh_antibody():
 
 # Allosteric–Orthosteric
 
-def submit_ligand(user, model_name, protein_id, ligand_type, predicted_kd, notes):
+def submit_ligand(group_name, rmsd_top1_all, rmsd_top1_allosteric, rmsd_top1_orthosteric, rmsd_top5_all, structures_under_2a):
     config = CONFIGS["ligand"]
     cols = COLUMNS["ligand"]
     df = load_df(config, cols)
     row = {
-        "timestamp": dt.datetime.now(dt.timezone.utc).isoformat(),
-        "user": user or "",
-        "model_name": model_name or "",
-        "protein_id": protein_id or "",
-        "ligand_type": ligand_type or "",
-        "predicted_kd": float(predicted_kd) if predicted_kd is not None else None,
-        "notes": notes or "",
+        "group_name": group_name or "",
+        "rmsd_top1_all": float(rmsd_top1_all) if rmsd_top1_all is not None else None,
+        "rmsd_top1_allosteric": float(rmsd_top1_allosteric) if rmsd_top1_allosteric is not None else None,
+        "rmsd_top1_orthosteric": float(rmsd_top1_orthosteric) if rmsd_top1_orthosteric is not None else None,
+        "rmsd_top5_all": float(rmsd_top5_all) if rmsd_top5_all is not None else None,
+        "structures_under_2a": int(structures_under_2a) if structures_under_2a is not None else 0,
     }
+    # Overwrite if group_name already exists
+    if group_name and not df.empty:
+        df = df[df["group_name"] != group_name]
     df = pd.concat([df, pd.DataFrame([row])], ignore_index=True)
     push_df(config, df)
     return load_df(config, cols)
@@ -132,17 +131,20 @@ def refresh_ligand():
 
 # Final Submission
 
-def submit_final(team_name, archive_url, results_summary, contact_email):
+def submit_final(group_name, repository_url, commit_sha, challenge_type, description_link):
     config = CONFIGS["final"]
     cols = COLUMNS["final"]
     df = load_df(config, cols)
     row = {
-        "timestamp": dt.datetime.now(dt.timezone.utc).isoformat(),
-        "team_name": team_name or "",
-        "archive_url": archive_url or "",
-        "results_summary": results_summary or "",
-        "contact_email": contact_email or "",
+        "group_name": group_name or "",
+        "repository_url": repository_url or "",
+        "commit_sha": commit_sha or "",
+        "challenge_type": challenge_type or "",
+        "description_link": description_link or "",
     }
+    # Overwrite if group_name already exists
+    if group_name and not df.empty:
+        df = df[df["group_name"] != group_name]
     df = pd.concat([df, pd.DataFrame([row])], ignore_index=True)
     push_df(config, df)
     return load_df(config, cols)
@@ -161,18 +163,16 @@ with gr.Blocks(title="Binding Challenges") as app:
             # Antibody-Antigen Binding Challenge
             The goal of this challenge is to improve Boltz-2 accuracy for predicting the correct poses of a VHH binding to an antigen.\n
             Accuracy will be measured through the Capri-Q docking assessment classification scores and the final winner will be determined based on the number of successful top-1 predictions on our *internal* test set. However, you are encouraged to submit results on the training set during the hack to see where you stack up.\n
-            A prediction is deemed successful if the Capri-Q classification is either “high”, “medium”, or “acceptable”.
-            If multiple entries reach the same number of successful predictions, ties are broken by looking at the number of predictions with “High” classification, then with “Medium” classification and finally with “Acceptable” classification.
+            A prediction is deemed successful if the Capri-Q classification is either "high", "medium", or "acceptable".
+            If multiple entries reach the same number of successful predictions, ties are broken by looking at the number of predictions with "High" classification, then with "Medium" classification and finally with "Acceptable" classification.
             If there is still a tie then, we will look at the mean RMSD across all successful predictions.
         """)
+        aa_group = gr.Textbox(label="Group Name", placeholder="Your group name")
         with gr.Row():
-            aa_user = gr.Textbox(label="User / Team", placeholder="Your name or team")
-            aa_model = gr.Textbox(label="Model Name")
-        with gr.Row():
-            aa_antibody = gr.Textbox(label="Antibody ID")
-            aa_antigen = gr.Textbox(label="Antigen ID")
-            aa_aff = gr.Number(label="Predicted affinity (nM)")
-        aa_notes = gr.Textbox(label="Notes", lines=3)
+            aa_successful = gr.Number(label="#Successful", value=0, precision=0)
+            aa_high = gr.Number(label="#High", value=0, precision=0)
+            aa_medium = gr.Number(label="#Medium", value=0, precision=0)
+            aa_acceptable = gr.Number(label="#Acceptable", value=0, precision=0)
         with gr.Row():
             aa_submit = gr.Button("Submit")
             aa_refresh = gr.Button("Refresh table")
@@ -184,7 +184,7 @@ with gr.Blocks(title="Binding Challenges") as app:
         )
         aa_submit.click(
             submit_antibody,
-            inputs=[aa_user, aa_model, aa_antibody, aa_antigen, aa_aff, aa_notes],
+            inputs=[aa_group, aa_successful, aa_high, aa_medium, aa_acceptable],
             outputs=aa_df,
         )
         aa_refresh.click(refresh_antibody, outputs=aa_df)
@@ -192,17 +192,17 @@ with gr.Blocks(title="Binding Challenges") as app:
     with gr.Tab("Allosteric–Orthosteric Ligand Binding Challenge"):
         gr.Markdown("""
             # Allosteric-Orthosteric Ligand Binding Challenge
-            The goal of this challenge is to improve Boltz-2 accuracy for predicting the binding poses of either allosteric or orthosteric ligands.\n 
+            The goal of this challenge is to improve Boltz-2 accuracy for predicting the binding poses of either allosteric or orthosteric ligands.\n
             The winner will be determined by accuracy measured on our *internal* test set by calculating the RMSD between the top-1 prediction and the experimental pose. However, submit your intermediate results here to see where you stack up!
                     """)
+        li_group = gr.Textbox(label="Group Name", placeholder="Your group name")
         with gr.Row():
-            li_user = gr.Textbox(label="User / Team")
-            li_model = gr.Textbox(label="Model Name")
+            li_rmsd_top1_all = gr.Number(label="RMSD top-1 (all structures)")
+            li_rmsd_top1_allosteric = gr.Number(label="RMSD top-1 (allosteric)")
+            li_rmsd_top1_orthosteric = gr.Number(label="RMSD top-1 (orthosteric)")
         with gr.Row():
-            li_protein = gr.Textbox(label="Protein ID")
-            li_type = gr.Radio(["allosteric", "orthosteric"], label="Ligand type")
-            li_kd = gr.Number(label="Predicted Kd (nM)")
-        li_notes = gr.Textbox(label="Notes", lines=3)
+            li_rmsd_top5_all = gr.Number(label="RMSD top-5 (all structures)")
+            li_structures_under_2a = gr.Number(label="#structures with RMSD < 2A", value=0, precision=0)
         with gr.Row():
             li_submit = gr.Button("Submit")
             li_refresh = gr.Button("Refresh table")
@@ -214,16 +214,20 @@ with gr.Blocks(title="Binding Challenges") as app:
         )
         li_submit.click(
             submit_ligand,
-            inputs=[li_user, li_model, li_protein, li_type, li_kd, li_notes],
+            inputs=[li_group, li_rmsd_top1_all, li_rmsd_top1_allosteric, li_rmsd_top1_orthosteric, li_rmsd_top5_all, li_structures_under_2a],
             outputs=li_df,
         )
         li_refresh.click(refresh_ligand, outputs=li_df)
 
     with gr.Tab("3) Final Submission"):
-        fs_team = gr.Textbox(label="Team name")
-        fs_archive = gr.Textbox(label="Archive URL (e.g., model artifacts)")
-        fs_summary = gr.Textbox(label="Results summary", lines=4)
-        fs_email = gr.Textbox(label="Contact email")
+        fs_group = gr.Textbox(label="Group Name", placeholder="Your group name")
+        fs_repo_url = gr.Textbox(label="Repository URL")
+        fs_commit_sha = gr.Textbox(label="Commit SHA")
+        fs_challenge_type = gr.Radio(
+            ["allosteric-orthosteric binding", "antibody-antigen binding"],
+            label="Challenge Type"
+        )
+        fs_description_link = gr.Textbox(label="Link to Markdown Description", placeholder="Link to a markdown page in your repo")
         with gr.Row():
             fs_submit = gr.Button("Submit")
             fs_refresh = gr.Button("Refresh table")
@@ -235,7 +239,7 @@ with gr.Blocks(title="Binding Challenges") as app:
         )
         fs_submit.click(
             submit_final,
-            inputs=[fs_team, fs_archive, fs_summary, fs_email],
+            inputs=[fs_group, fs_repo_url, fs_commit_sha, fs_challenge_type, fs_description_link],
             outputs=fs_df,
         )
         fs_refresh.click(refresh_final, outputs=fs_df)