Update app.py

app.py

@@ -120,7 +120,7 @@ def draw_mass_spectrum(peak_data_path):
     ms2 = spectrum_processing(ms2)
     Mz = np.array(ms2.mz)
     Intens = np.array(ms2.intensities)
-    plt.figure(figsize=(8.5,5))
+    plt.figure(figsize=(8,5))
     for i in range(len(Mz)):
             plt.axvline(x=Mz[i], ymin=0, ymax=Intens[i],c='red')
     plt.xlabel("m/z")
@@ -398,12 +398,7 @@ def rank_user_lib(candidate_file,spectrum_path,instrument_type,adduct,collision_
 
 with gr.Blocks(theme=seafoam) as demo:
     gr.HTML(custom_css)
-    gr.Markdown('<div style="font-size:40px; font-weight:bold;">🔍 Cross-Modal MS2 Retrieval Demo </div>')
-    gr.Markdown('''
-<div style=" font-weight:bold;">
-    <span style="font-size:40px;"> Based on CSU-MS2 model </span>
-</div>
-''')
+    gr.Markdown('<div style="font-size:40px; font-weight:bold;">🔍 CSU-MS2 web server </div>')
 
     #gr.Markdown('<div style="font-size:20px;">You can select from a list of collected databases or upload your structural file by Clicking the button. The MS/MS spectrum to be identified can be uploaded in MSP format. You also need to set the necessary experimental parameters for the input spectrum, including accurate precursor mass and collision energy. Finally, by clicking the "Cross-Modal Retrieval" button, you can retrieve candidates from the selected database or your own structural file and access identification results that include compound structures, scores, and rankings.</div>')
     with gr.Tab(label="📶 Struture library", elem_id='custom_tab'):