File size: 2,927 Bytes
9b9c66d 4a4b152 9b9c66d 4a4b152 9b9c66d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | import os
import gradio as gr
from utils import calculate_md5, run_community_analysis, run_usalign, save_pdb_files
os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
# Create Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# This is a Temp Title")
with gr.Row():
file_input = gr.File(
label="Upload PDB Files",
file_count="multiple",
file_types=[".pdb"],
)
output = gr.Textbox(
label="Upload Results", lines=5, max_lines=5, container=True # 默认显示行数 # 最大可见行数（超过后自动滚动）
)
threshold = gr.Slider(minimum=0, maximum=1, value=0.75, label="Threshold")
with gr.Row():
submit_btn = gr.Button("Upload Files")
run_usalign_btn = gr.Button("Run USalign")
community_btn = gr.Button("Run Community")
md5_hash = gr.State("")
with gr.Tab("USalign Results"):
results_df = gr.DataFrame(
label="USalign Results",
wrap=True,
)
with gr.Tab("TM Matrix"):
# Add new output components for community analysis with height limits
tm_matrix_output = gr.DataFrame(label="TM Matrix", wrap=True, show_label=True)
with gr.Tab("Newick Tree"):
newick_output = gr.Textbox(
label="Newick Tree", lines=5, max_lines=10, container=True # 默认显示行数 # 最大可见行数（超过后自动滚动）
)
# with gr.Tab("Structure Similarity Network"):
# network_plot = gr.Plot(label="Structure Similarity Network")
# Combine download buttons into a single row
with gr.Row():
with gr.Column():
gr.Markdown("### Download Results")
download_tm = gr.File(label="Download Files")
submit_btn.click(fn=save_pdb_files, inputs=[file_input], outputs=output)
def update_md5_hash(files):
if files:
return calculate_md5(files)
return ""
file_input.change(fn=update_md5_hash, inputs=[file_input], outputs=[md5_hash])
run_usalign_btn.click(fn=run_usalign, inputs=[md5_hash], outputs=[results_df])
def process_community_analysis(results_df, md5_hash, threshold):
if results_df.empty:
return None, None, None
results = run_community_analysis(results_df, "./data", md5_hash, threshold)
if "Error" in results:
return None, None, None
# Prepare download files
return (
results["tm_matrix"],
results["newick_str"],
# results["network_fig"],
results["files"],
)
community_btn.click(
fn=process_community_analysis,
inputs=[results_df, md5_hash, threshold],
outputs=[
tm_matrix_output,
newick_output,
# network_plot,
download_tm,
],
)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0")
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