remove height (might be gradio version issue)

app.py

@@ -93,7 +93,7 @@ with demo:
                 inverse_folding_csv = assign_rank_and_get_sorted_csv('data_link/inverse_folding.csv', 'data_rank/inverse_folding.csv')
                 inverse_folding_table = gr.components.DataFrame(
                     value=convert_to_float(inverse_folding_csv).values,
-                    height=99999,
+                    # height=99999,
                     interactive=False,
                     headers=inverse_folding_csv.columns.to_list(),
                     datatype=['number', 'markdown'] + (len(inverse_folding_csv.columns)-1) * ['number'],
@@ -104,7 +104,7 @@ with demo:
                 structure_design_csv = assign_rank_and_get_sorted_csv('data_link/structure_design.csv','data_rank/structure_design.csv', ignore_num=1)
                 structure_design_table = gr.components.DataFrame(
                     value=convert_to_float(structure_design_csv).values,
-                    height=99999,
+                    # height=99999,
                     interactive=False,
                     headers=structure_design_csv.columns.to_list(),
                     datatype=['number', 'markdown'] + (len(structure_design_csv.columns)-1) * ['number'],
@@ -114,7 +114,7 @@ with demo:
                 sequence_design_csv = assign_rank_and_get_sorted_csv('data_link/sequence_design.csv', 'data_rank/sequence_design.csv', ignore_num=1)
                 sequence_design_table = gr.components.DataFrame(
                     value=convert_to_float(sequence_design_csv).values,
-                    height=99999,
+                    # height=99999,
                     interactive=False,
                     headers=sequence_design_csv.columns.to_list(),
                     datatype=['number', 'markdown'] + (len(sequence_design_csv.columns)-1) * ['number'],
@@ -124,7 +124,7 @@ with demo:
                 co_design_csv = assign_rank_and_get_sorted_csv('data_link/co_design.csv', 'data_rank/co_design.csv', ignore_num=1)
                 co_design_table = gr.components.DataFrame(
                     value=convert_to_float(co_design_csv).values,
-                    height=99999,
+                    # height=99999,
                     interactive=False,
                     headers=co_design_csv.columns.to_list(),
                     datatype=['number', 'markdown'] + (len(co_design_csv.columns)-1) * ['number'],
@@ -134,7 +134,7 @@ with demo:
                 motif_scaffolding_csv = assign_rank_and_get_sorted_csv('data_link/motif_scaffolding.csv', 'data_rank/motif_scaffolding.csv')
                 motif_scaffolding_table = gr.components.DataFrame(
                     value=convert_to_float(motif_scaffolding_csv).values,
-                    height=99999,
+                    # height=99999,
                     interactive=False,
                     headers=motif_scaffolding_csv.columns.to_list(),
                     datatype=['number', 'markdown'] + (len(motif_scaffolding_csv.columns)-1) * ['number'],
@@ -144,7 +144,7 @@ with demo:
                 antibody_design_csv = assign_rank_and_get_sorted_csv('data_link/antibody_design.csv', 'data_rank/antibody_design.csv', ignore_num=1)
                 antibody_design_table = gr.components.DataFrame(
                     value=convert_to_float(antibody_design_csv).values,
-                    height=99999,
+                    # height=99999,
                     interactive=False,
                     headers=antibody_design_csv.columns.to_list(),
                     datatype=['number', 'markdown'] + (len(antibody_design_csv.columns)-1) * ['number'],
@@ -154,7 +154,7 @@ with demo:
                 protein_folding_csv = assign_rank_and_get_sorted_csv('data_link/protein_folding.csv', 'data_rank/protein_folding.csv')
                 protein_folding_table = gr.components.DataFrame(
                     value=convert_to_float(protein_folding_csv).values,
-                    height=99999,
+                    # height=99999,
                     interactive=False,
                     headers=protein_folding_csv.columns.to_list(),
                     datatype=['number', 'markdown'] + (len(protein_folding_csv.columns)-1) * ['number'],
@@ -164,7 +164,7 @@ with demo:
                 multi_state_prediction_csv = assign_rank_and_get_sorted_csv('data_link/multi_state_prediction_bpti.csv', 'data_rank/multi_state_prediction_bpti.csv')
                 multi_state_prediction_table = gr.components.DataFrame(
                     value=convert_to_float(multi_state_prediction_csv).values,
-                    height=99999,
+                    # height=99999,
                     interactive=False,
                     headers=multi_state_prediction_csv.columns.to_list(),
                     datatype=['number', 'markdown'] + (len(multi_state_prediction_csv.columns)-1) * ['number'],
@@ -174,7 +174,7 @@ with demo:
                 conformation_prediction_csv = assign_rank_and_get_sorted_csv('data_link/multi_state_prediction_apo.csv', 'data_rank/multi_state_prediction_apo.csv', ignore_num=1)
                 conformation_prediction_table = gr.components.DataFrame(
                     value=convert_to_float(conformation_prediction_csv).values,
-                    height=99999,
+                    # height=99999,
                     interactive=False,
                     headers=conformation_prediction_csv.columns.to_list(),
                     datatype=['number', 'markdown'] + (len(conformation_prediction_csv.columns)-1) * ['number'],
@@ -184,7 +184,7 @@ with demo:
                 distribution_prediction_csv = assign_rank_and_get_sorted_csv('data_link/distribution_prediction.csv', 'data_rank/distribution_prediction.csv', ignore_num=2)
                 distribution_prediction_table = gr.components.DataFrame(
                     value=convert_to_float(distribution_prediction_csv).values,
-                    height=99999,
+                    # height=99999,
                     interactive=False,
                     headers=distribution_prediction_csv.columns.to_list(),
                     datatype=['number', 'markdown'] + (len(distribution_prediction_csv.columns)-1) * ['number'],