Spaces:
Running
Running
zhouxiangxin1998
commited on
Commit
β’
cc5c681
1
Parent(s):
8c31b30
add auto datatype
Browse files
app.py
CHANGED
@@ -58,74 +58,85 @@ with demo:
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with gr.Tabs(elem_classes="tab-buttons") as tabs:
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with gr.TabItem("π Inverse Folding Leaderboard", elem_id='inverse-folding-table', id=0,):
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with gr.Row():
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inverse_folding_table = gr.components.DataFrame(
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-
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height=99999,
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interactive=False,
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datatype=['markdown'
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)
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with gr.TabItem("π Structure Design Leaderboard", elem_id='structure-design-table', id=1,):
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with gr.Row():
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height=99999,
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interactive=False,
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datatype=['markdown'
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)
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with gr.TabItem("π Sequence Design Leaderboard", elem_id='sequence-design-table', id=2,):
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with gr.Row():
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height=99999,
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interactive=False,
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datatype=['markdown'
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)
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with gr.TabItem("π Sequence-Structure Co-Design Leaderboard", elem_id='co-design-table', id=3,):
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with gr.Row():
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height=99999,
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interactive=False,
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datatype=['markdown'
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)
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with gr.TabItem("π Motif Scaffolding Leaderboard", elem_id='motif-scaffolding-table', id=4,):
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with gr.Row():
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height=99999,
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interactive=False,
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datatype=['markdown'
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)
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with gr.TabItem("π Antibody Design Leaderboard", elem_id='antibody-design-table', id=5,):
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with gr.Row():
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height=99999,
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interactive=False,
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)
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with gr.TabItem("π
Protein Folding Leaderboard", elem_id='protein-folding-table', id=6,):
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with gr.Row():
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height=99999,
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interactive=False,
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datatype=['markdown'
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)
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with gr.TabItem("π
Multi-State Prediction Leaderboard", elem_id='multi-state-prediction-table', id=7,):
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with gr.Row():
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height=99999,
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interactive=False,
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datatype=['markdown'
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)
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with gr.TabItem("π
Conformation Prediction Leaderboard", elem_id='conformation-prediction-table', id=8,):
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with gr.Row():
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height=99999,
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interactive=False,
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datatype=['markdown'
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)
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with gr.Tabs(elem_classes="tab-buttons") as tabs:
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with gr.TabItem("π Inverse Folding Leaderboard", elem_id='inverse-folding-table', id=0,):
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with gr.Row():
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inverse_folding_csv = pd.read_csv('data/inverse_folding.csv')
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inverse_folding_table = gr.components.DataFrame(
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inverse_folding_csv,
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height=99999,
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interactive=False,
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datatype=['markdown'] + (len(inverse_folding_csv.columns)-1) * ['number'],
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)
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with gr.TabItem("π Structure Design Leaderboard", elem_id='structure-design-table', id=1,):
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with gr.Row():
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structure_design_csv = pd.read_csv('data/structure_design.csv')
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structure_design_table = gr.components.DataFrame(
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structure_design_csv,
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height=99999,
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interactive=False,
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datatype=['markdown'] + (len(structure_design_csv.columns)-1) * ['number'],
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)
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with gr.TabItem("π Sequence Design Leaderboard", elem_id='sequence-design-table', id=2,):
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with gr.Row():
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sequence_design_csv = pd.read_csv('data/sequence_design.csv'),
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sequence_design_table = gr.components.DataFrame(
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sequence_design_csv,
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height=99999,
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interactive=False,
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datatype=['markdown'] + (len(sequence_design_csv.columns)-1) * ['number'],
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)
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with gr.TabItem("π Sequence-Structure Co-Design Leaderboard", elem_id='co-design-table', id=3,):
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with gr.Row():
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co_design_csv = pd.read_csv('data/co_design.csv')
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co_design_table = gr.components.DataFrame(
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co_design_csv,
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height=99999,
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interactive=False,
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datatype=['markdown'] + (len(co_design_csv.columns)-1) * ['number'],
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)
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with gr.TabItem("π Motif Scaffolding Leaderboard", elem_id='motif-scaffolding-table', id=4,):
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with gr.Row():
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motif_scaffolding_csv = pd.read_csv('data/motif_scaffolding.csv')
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motif_scaffolding_table = gr.components.DataFrame(
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motif_scaffolding_csv,
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height=99999,
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interactive=False,
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datatype=['markdown'] + (len(motif_scaffolding_csv.columns)-1) * ['number'],
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)
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with gr.TabItem("π Antibody Design Leaderboard", elem_id='antibody-design-table', id=5,):
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with gr.Row():
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antibody_design_csv = pd.read_csv('data/antibody_design.csv')
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antibody_design_table = gr.components.DataFrame(
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antibody_design_csv,
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height=99999,
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interactive=False,
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datatype=['markdown'] + (len(antibody_design_csv.columns)-1) * ['number'],
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)
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with gr.TabItem("π
Protein Folding Leaderboard", elem_id='protein-folding-table', id=6,):
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with gr.Row():
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protein_folding_csv = pd.read_csv('data/protein_folding.csv')
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protein_folding_table = gr.components.DataFrame(
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protein_folding_csv,
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height=99999,
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interactive=False,
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datatype=['markdown'] + (len(protein_folding_csv.columns)-1) * ['number'],
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)
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with gr.TabItem("π
Multi-State Prediction Leaderboard", elem_id='multi-state-prediction-table', id=7,):
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with gr.Row():
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multi_state_prediction_csv = pd.read_csv('data/multi_state_prediction.csv')
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multi_state_prediction_table = gr.components.DataFrame(
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multi_state_prediction_csv,
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height=99999,
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interactive=False,
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datatype=['markdown'] + (len(multi_state_prediction_csv.columns)-1) * ['number'],
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)
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with gr.TabItem("π
Conformation Prediction Leaderboard", elem_id='conformation-prediction-table', id=8,):
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with gr.Row():
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conformation_prediction = pd.read_csv('data/conformation_prediction.csv')
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conformation_prediction_table = gr.components.DataFrame(
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conformation_prediction,
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height=99999,
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interactive=False,
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datatype=['markdown'] + (len(conformation_prediction.columns)-1) * ['number'],
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)
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