Update app.py

app.py

@@ -160,14 +160,14 @@ visibility: hidden
 
 
 class View3DmolCell(py3Dmol.view):
-    def __init__(self, width=640, height=480):
+    def __init__(self, width=400, height=400):
         divid = "3dmolviewer_UNIQUEID"
         self.uniqueid = None
         if isinstance(width, int):
             width = '%dpx' % width
-        if isinstance(width, int):
+        if isinstance(height, int):
             height = '%dpx' % height
-        self.startjs = '''<div id="%s"  style="position: relative; width: %s; height: %s;">
+        self.startjs = '''<div id="%s" style="position: relative; width: %s; height: %s;">
             </div>\n''' % (divid, width, height)
         self.startjs += '<script>\n'
         self.endjs = '</script>'
@@ -1012,8 +1012,7 @@ def submit_predict(predict_filepath, task, preset, target_family, opts, job_info
         # Advanced options for Target Protein Identification
         if "Calculate Max. Tanimoto Similarity between the Input Compound and Compounds in the Training Set" in opts:
             x1 = rdkit_canonicalize(prediction_df['X1'].iloc[0])
-            if 'FP' not in prediction_df.columns:
-                prediction_df['FP'] = prediction_df['X1'].parallel_apply(smiles_to_ecfp)
+            prediction_df['FP'] = prediction_df['X1'].parallel_apply(smiles_to_ecfp)
 
             prediction_df[[
                 'Max. Tanimoto Similarity to Training Compounds',
@@ -1037,12 +1036,11 @@ def submit_predict(predict_filepath, task, preset, target_family, opts, job_info
 
         if "Calculate Max. Tanimoto Similarity between the Input Compound and Known Ligands of the Identified Target" in opts:
             x1 = rdkit_canonicalize(prediction_df['X1'].iloc[0])
-            if 'FP' not in prediction_df.columns:
-                prediction_df['FP'] = prediction_df['X1'].parallel_apply(smiles_to_ecfp)
 
             @cache
             def max_sim(fasta):
                 pos_targets_df = df_training.loc[(df_training['X2'] == fasta) & (df_training['Y'] == 1)].copy()
+                pos_targets_df['FP'] = pos_targets_df['X1'].apply(smiles_to_ecfp)
                 return max_tanimoto_similarity(x1, seen_smiles_with_fp=pos_targets_df)
 
             prediction_df[[
@@ -1051,10 +1049,8 @@ def submit_predict(predict_filepath, task, preset, target_family, opts, job_info
             ]] = prediction_df['X2'].parallel_apply(max_sim).apply(pd.Series)
 
             max_sim.cache_clear()
-        
-        prediction_df.drop(
-            [col for col in prediction_df.columns if col in ['N', 'FP']], axis=1
-        ).to_csv(predictions_file, index=False, na_rep='')
+
+        prediction_df.drop(['N'], axis=1).to_csv(predictions_file, index=False, na_rep='')
         status = "COMPLETED"
 
         return {run_state: False}
@@ -1179,8 +1175,8 @@ def create_html_report(df, file=None, task=None, opts=(), progress=gr.Progress(t
             if any(col in df_html.columns for col in ['Y^', 'Y']):
                 job = 'Target Protein Identification'
                 category = 'Target Family'
-            columns_unique = df_html.columns.isin(
-                ['ID1', 'Pharmacophore', 'Compound', 'Scaffold', 'X1', 'Scaffold SMILES',
+            columns_unique = df_html.columns.isin(  # 'Pharmacophore' excluded until an image solution is available
+                ['ID1', 'Compound', 'Scaffold', 'X1', 'Scaffold SMILES',
                  'Max. Tanimoto Similarity to Training Compounds', 'Max. Sim. Training Compound']
                 + list(FILTER_MAP.keys()) + list(SCORE_MAP.keys())
             )
@@ -1264,8 +1260,10 @@ def create_html_report(df, file=None, task=None, opts=(), progress=gr.Progress(t
         uniprot_id_formatter = HTMLTemplateFormatter(
             template='<% if (value == value) { '  # Check if value is not NaN
                      'if (/^[OPQ][0-9][A-Z0-9]{3}[0-9]|[A-NR-Z][0-9]([A-Z][A-Z0-9]{2}[0-9]){1,2}$/.test(value)) '
-                     '{ %><a href="https://www.uniprot.org/uniprotkb/<%= value %>" target="_blank"><%= value %></a><%'
-                     '} else { %><textarea style="width: 60ch;"><%= value %></textarea><% } %>'
+            # Check if value is a valid UniProt ID
+                     '{ %><a href="https://www.uniprot.org/uniprotkb/<%= value %>" target="_blank"><%= value %></a><% '
+            # Else treat it as a sequence or other plain-text string, line-warping every 60 characters
+                     '} else { %><div style="white-space: pre-wrap;"><%= value.match(/.{1,60}/g).join("<br>") %></div><% } %>'
                      '<% } else { %><% } %>'  # Output empty string if value is NaN
         )
         pubchem_id_formatter = HTMLTemplateFormatter(
@@ -1284,8 +1282,10 @@ def create_html_report(df, file=None, task=None, opts=(), progress=gr.Progress(t
             'Target FASTA': {'type': 'textarea', 'width': 60},
             'Target ID': uniprot_id_formatter,
             'Compound ID': pubchem_id_formatter,
-            'Max. Tanimoto Similarity Target Ligand': pubchem_id_formatter,
-            'Max. Sequence Identity Ligand Target': uniprot_id_formatter,
+            'Max. Sim. Ligand': pubchem_id_formatter,
+            'Max. Id. Target': uniprot_id_formatter,
+            'Max. Sim. Training Compound': pubchem_id_formatter,
+            'Max. Id. Training Target': uniprot_id_formatter,
         }
         formatters = {**bool_formatters, **float_formatters, **other_formatters}
 
@@ -1325,14 +1325,56 @@ def create_html_report(df, file=None, task=None, opts=(), progress=gr.Progress(t
         # Remove keys with empty values
         pie_charts = {k: v for k, v in pie_charts.items() if any(v)}
 
-        pn.extension(
-            css_files=[
-                './static/panel.css',
-            ],
-            js_files={
-                '3Dmol': './static/3Dmol-min.js',
-                'panel_custom': './static/panel.js'
+        panel_css = """
+            .tabulator {
+                font-family: Courier New !important;
+                font-weight: normal !important;
+                font-size: 12px !important;
+            }
+            
+            .tabulator-cell {
+                overflow: visible !important;
+            }
+            
+            
+            .image-zoom-viewer {
+                display: inline-block;
+                overflow: visible;
+                z-index: 1000;
+            }
+            
+            .image-zoom-viewer::after {
+                content: "";
+                top: 0;
+                left: 0;
+                width: 100%;
+                height: 100%;
+                pointer-events: none;
+            }
+            
+            .image-zoom-viewer:hover::after {
+                pointer-events: all;
             }
+            
+            /* When hovering over the container, scale its child (the SVG) */
+            .tabulator-cell:hover .image-zoom-viewer svg {
+                padding: 3px;
+                position: absolute;
+                background-color: rgba(250, 250, 250, 0.854);
+                box-shadow: 0 0 10px rgba(0, 0, 0, 0.618);
+                border-radius: 3px;
+                transform: scale(3); /* Scale up the SVG */
+                transition: transform 0.3s ease;
+                pointer-events: none; /* Prevents the SVG from blocking mouse interactions */
+                z-index: 1000;
+            }
+        """
+
+        pn.extension(
+            raw_css=[panel_css],
+            js_files={'panel_custom': 'static/panel.js', '3Dmol': 'static/3Dmol-min.js'},
+            # js_modules={'3Dmol': 'static/3Dmol-min.js'},
+            inline=True
         )
 
         template = pn.template.VanillaTemplate(
@@ -1370,7 +1412,7 @@ def create_html_report(df, file=None, task=None, opts=(), progress=gr.Progress(t
                     margin=10)
         )
 
-        template.save(file, resources=INLINE, title=f'DeepSEQreen {job} Report')
+        template.save(file, title=f'DeepSEQreen {job} Report', resources=INLINE)
         return file