Update app.py

app.py

@@ -12,7 +12,6 @@ from pathlib import Path
 import sys
 
 import numpy as np
-from Bio import SeqIO
 from Bio.Align import PairwiseAligner
 # from email_validator import validate_email
 import gradio as gr
@@ -60,10 +59,10 @@ SESSION.mount('https://', ADAPTER)
 UNIPROT_ENDPOINT = 'https://rest.uniprot.org/uniprotkb/{query}'
 
 CSS = """
-.help-tip > div {
+.help-tip {
   position: absolute;
-  display: block;
-  top: 0px;
+  display: inline-block;
+  top: 24px;
   right: 0px;
   text-align: center;
   border-radius: 40%;
@@ -74,6 +73,7 @@ CSS = """
   line-height: 26px;
   cursor: default;
   transition: all 0.5s cubic-bezier(0.55, 0, 0.1, 1);
+  z-index: 100 !important;
 }
 
 .help-tip:hover {
@@ -85,7 +85,7 @@ CSS = """
   content: '?';
   font-weight: 700;
   color: #8B0000;
-  z-index: 100;
+  z-index: 100 !important;
 }
 
 .help-tip p {
@@ -161,9 +161,10 @@ visibility: hidden
 
 class HelpTip:
     def __new__(cls, text):
-        return gr.HTML(elem_classes="help-tip",
-                       value=f'<p>{text}</p>'
-                       )
+        return gr.HTML(
+            # elem_classes="absolute",
+            value=f'<div class="help-tip"><p>{text}</p>',
+        )
 
 
 def sa_score(row):
@@ -366,8 +367,8 @@ def submit_predict(predict_filepath, task, preset, target_family, flag, progress
             target_family = TARGET_FAMILY_MAP[target_family]
             # email_hash = hashlib.sha256(email.encode()).hexdigest()
             COLUMN_ALIASES = COLUMN_ALIASES | {
-                'Y': 'Actual interaction' if task == 'binary' else 'Actual affinity',
-                'Y^': 'Predicted interaction' if task == 'binary' else 'Predicted affinity'
+                'Y': 'Actual interaction probability' if task == 'binary' else 'Actual binding affinity',
+                'Y^': 'Predicted interaction probability' if task == 'binary' else 'Predicted binding affinity'
             }
 
             # target_family_list = [target_family]
@@ -387,7 +388,7 @@ def submit_predict(predict_filepath, task, preset, target_family, flag, progress
                 predictions = [pd.DataFrame(prediction) for prediction in predictions]
                 prediction_df = pd.concat([prediction_df, pd.concat(predictions, ignore_index=True)])
 
-                predictions_file = f'{job_id}_predictions.csv'
+                predictions_file = f'temp/{job_id}_predictions.csv'
                 prediction_df.to_csv(predictions_file, index=False)
 
                 return [predictions_file,
@@ -484,12 +485,12 @@ def update_df(file, progress=gr.Progress(track_tqdm=True)):
                                                  includeFingerprints=False)
         DF_FOR_REPORT = df.copy()
 
-        pie_chart = None
-        value = None
-        if 'Y^' in DF_FOR_REPORT.columns:
-            value = 'Y^'
-        elif 'Y' in DF_FOR_REPORT.columns:
-            value = 'Y'
+        # pie_chart = None
+        # value = None
+        # if 'Y^' in DF_FOR_REPORT.columns:
+        #     value = 'Y^'
+        # elif 'Y' in DF_FOR_REPORT.columns:
+        #     value = 'Y'
 
         # if value:
         #     if DF_FOR_REPORT['X1'].nunique() > 1 >= DF_FOR_REPORT['X2'].nunique():
@@ -497,37 +498,47 @@ def update_df(file, progress=gr.Progress(track_tqdm=True)):
         #     elif DF_FOR_REPORT['X2'].nunique() > 1 >= DF_FOR_REPORT['X1'].nunique():
         #         pie_chart = create_pie_chart(DF_FOR_REPORT, category='Target family', value=value, top_k=100)
 
-        return create_html_report(DF_FOR_REPORT), pie_chart
+        return create_html_report(DF_FOR_REPORT), df  # pie_chart
     else:
-        return gr.HTML(''), gr.Plot()
+        return gr.HTML(), gr.Dataframe()
 
 
 def create_html_report(df, file=None, progress=gr.Progress(track_tqdm=True)):
-    cols_left = ['ID2', 'Y', 'Y^', 'ID1', 'Compound', 'Scaffold', 'Scaffold SMILES', ]
+    df_html = df.copy()
+    cols_left = ['ID1', 'ID2', 'Y', 'Y^', 'Compound', 'Scaffold', 'Scaffold SMILES', ]
     cols_right = ['X1', 'X2']
-    cols_left = [col for col in cols_left if col in df.columns]
-    cols_right = [col for col in cols_right if col in df.columns]
-    df = df[cols_left + (df.columns.drop(cols_left + cols_right).tolist()) + cols_right]
-    df['X2'] = df['X2'].apply(wrap_text)
-    df.rename(COLUMN_ALIASES, inplace=True)
-
-    styled_df = df.style
-    # styled_df = df.style.format("{:.2f}")
-    colors = sns.color_palette('husl', len(df.columns))
-    for i, col in enumerate(df.columns):
-        if pd.api.types.is_numeric_dtype(df[col]):
-            styled_df = styled_df.background_gradient(subset=col, cmap=sns.light_palette(colors[i], as_cmap=True))
-
+    cols_left = [col for col in cols_left if col in df_html.columns]
+    cols_right = [col for col in cols_right if col in df_html.columns]
+    df_html = df_html[cols_left + (df_html.columns.drop(cols_left + cols_right).tolist()) + cols_right]
+    df_html['X2'] = df_html['X2'].swifter.apply(wrap_text)
+    df_html = df_html.sort_values(
+        [col for col in ['Y', 'Y^', 'ID1', 'ID2', 'X1', 'X2'] if col in df.columns], ascending=False
+    ).rename(columns=COLUMN_ALIASES)
+    # PandasTools.RenderImagesInAllDataFrames(images=True)
+    PandasTools.ChangeMoleculeRendering(df_html, renderer='image')
     # Return the DataFrame as HTML
     PandasTools.RenderImagesInAllDataFrames(images=True)
 
     if not file:
-        html = df.to_html()
-        return f'<div style="overflow:auto; height: 500px;">{html}</div>'
+        styled_df = df_html.iloc[:51].style
+        # styled_df = df.style.format("{:.2f}")
+        colors = sns.color_palette('husl', len(df_html.columns))
+        for i, col in enumerate(df_html.columns):
+            if pd.api.types.is_numeric_dtype(df_html[col]):
+                styled_df = styled_df.background_gradient(subset=col, cmap=sns.light_palette(colors[i], as_cmap=True))
+        html = styled_df.to_html()
+        return f'Report preview<div style="overflow:auto; height: 300px; font-family: Courier !important;">{html}</div>'
     else:
-        html = df.to_html(file)
-        return html
-    # return gr.HTML(pn.widgets.Tabulator(df).embed())
+        import panel as pn
+        from bokeh.resources import INLINE
+        from bokeh.models import NumberFormatter, BooleanFormatter
+        bokeh_formatters = {
+            'float': {'type': 'progress', 'legend': True},
+            'bool': BooleanFormatter(),
+        }
+        # html = df.to_html(file)
+        # return html
+        pn.widgets.Tabulator(df_html, formatters=bokeh_formatters).save(file, resources=INLINE)
 
 
 # def create_pie_chart(df, category, value, top_k):
@@ -694,6 +705,12 @@ with (gr.Blocks(theme=theme, title='DeepScreen', css=CSS) as demo):
                 with gr.Column() as screen_page:
                     with gr.Row():
                         with gr.Column():
+                            HelpTip(
+                                "Target amino acid sequence in the FASTA format. Alternatively, you may use a "
+                                "UniProt ID/accession to query UniProt database for the sequence of your "
+                                "target of interest. If the input FASTA contains multiple entities, "
+                                "only the first one will be used."
+                            )
                             with gr.Row():
                                 target_input_type = gr.Dropdown(
                                     label='Target Input Type',
@@ -713,22 +730,17 @@ with (gr.Blocks(theme=theme, title='DeepScreen', css=CSS) as demo):
                                     info='Organism common name or scientific name (default: Human).',
                                     placeholder='Human', show_label=False,
                                     visible=False, interactive=True, scale=4, )
+                        with gr.Column():
                             HelpTip(
-                                "Target amino acid sequence in the FASTA format. Alternatively, you may use a "
-                                "UniProt ID/accession to query UniProt database for the sequence of your "
-                                "target of interest. If the input FASTA contains multiple entities, "
-                                "only the first one will be used."
+                                "Identify the protein family by conducting sequence alignment. "
+                                "You may select General if you find the alignment score unsatisfactory."
                             )
-                        with gr.Column():
                             drug_screen_target_family = gr.Dropdown(
                                 choices=list(TARGET_FAMILY_MAP.keys()),
                                 value='General',
                                 label='Select Input Protein Family (Optional)', interactive=True)
                             # with gr.Column(scale=1, min_width=24):
-                            HelpTip(
-                                "Identify the protein family by conducting sequence alignment. "
-                                "You may select General if you find the alignment score unsatisfactory."
-                            )
+
                     with gr.Row():
                         with gr.Column():
                             target_upload_btn = gr.UploadButton(label='Upload a FASTA file', type='binary',
@@ -751,12 +763,13 @@ with (gr.Blocks(theme=theme, title='DeepScreen', css=CSS) as demo):
                             drug_screen_task = gr.Dropdown(list(TASK_MAP.keys()), label='Select a Prediction Task',
                                                            value='Drug-target interaction')
                         with gr.Column():
-                            drug_screen_preset = gr.Dropdown(list(PRESET_MAP.keys()), label='Select a Preset Model')
-                            screen_preset_recommend_btn = gr.Button(value='Recommend a model', variant='primary')
                             HelpTip("We recommend the appropriate model for your use case based on model performance "
                                     "in drug-target interaction or binding affinity prediction. "
                                     "The models were benchmarked on different target families "
                                     "and real-world data scenarios.")
+                            drug_screen_preset = gr.Dropdown(list(PRESET_MAP.keys()), label='Select a Preset Model')
+                            screen_preset_recommend_btn = gr.Button(value='Recommend a model', variant='primary')
+
 
                     # drug_screen_email = gr.Textbox(
                     #     label='Email (optional)',
@@ -787,13 +800,6 @@ with (gr.Blocks(theme=theme, title='DeepScreen', css=CSS) as demo):
                 with gr.Column() as identify_page:
                     with gr.Row():
                         with gr.Column():
-                            compound_type = gr.Dropdown(
-                                label='Compound Input Type',
-                                choices=['SMILES', 'SDF'],
-                                info='Enter (paste) an SMILES string or upload an SMI file.',
-                                value='SMILES',
-                                interactive=True)
-                            compound_upload_btn = gr.UploadButton(label='Upload', variant='primary', type='binary')
                             HelpTip(
                                 """Compound molecule in the SMILES format. You may input the SMILES string directly, 
                                 upload an SMI file, or upload an SDF file to convert to SMILES. Alternatively, 
@@ -801,6 +807,14 @@ with (gr.Blocks(theme=theme, title='DeepScreen', css=CSS) as demo):
                                 representing your drug of interest.
                                 """
                             )
+                            compound_type = gr.Dropdown(
+                                label='Compound Input Type',
+                                choices=['SMILES', 'SDF'],
+                                info='Enter (paste) an SMILES string or upload an SMI file.',
+                                value='SMILES',
+                                interactive=True)
+                            compound_upload_btn = gr.UploadButton(label='Upload', variant='primary', type='binary')
+
                         with gr.Column():
                             target_identify_target_family = gr.Dropdown(choices=['General'], value='General',
                                                                         label='Target Protein Family')
@@ -819,12 +833,13 @@ with (gr.Blocks(theme=theme, title='DeepScreen', css=CSS) as demo):
                                                                value='Drug-target interaction')
 
                         with gr.Column():
-                            target_identify_preset = gr.Dropdown(list(PRESET_MAP.keys()), label='Preset')
-                            identify_preset_recommend_btn = gr.Button(value='Recommend a model', variant='primary')
                             HelpTip("We recommend the appropriate model for your use case based on model performance "
                                     "in drug-target interaction or binding affinity prediction. "
                                     "The models were benchmarked on different target families "
                                     "and real-world data scenarios.")
+                            target_identify_preset = gr.Dropdown(list(PRESET_MAP.keys()), label='Preset')
+                            identify_preset_recommend_btn = gr.Button(value='Recommend a model', variant='primary')
+
 
                     # with gr.Row():
                     #     target_identify_email = gr.Textbox(
@@ -843,24 +858,47 @@ with (gr.Blocks(theme=theme, title='DeepScreen', css=CSS) as demo):
                                            visible=False)
         with gr.TabItem(label='Interaction pair inference', id=2):
             gr.Markdown('''
-                # <center>DeepSEQreen Interaction Pair Inference</center>
-                <center>
-                To predict interactions/binding affinities between any drug-target pairs.
-                </center>
-                ''')
+# <center>DeepSEQreen Interaction Pair Inference</center>
+<center>To predict interactions/binding affinities between any drug-target pairs.</center>
+''')
             with gr.Blocks() as infer_block:
                 with gr.Column() as infer_page:
-                    HelpTip("Upload a custom drug-target pair dataset. See the documentation for details.")
-                    infer_data_for_predict = gr.File(
-                        label='Prediction dataset file', file_count="single", type='filepath')
-                    # TODO example dataset
-                    # TODO download example dataset
+                    with gr.Column() as custom_upload:
+                        gr.Markdown("""
+Please upload a custom dataset CSV file with 2 required string columns and optionally 2 ID columns:
+
+<b>X1</b>: the SMILES string of a compound\n
+<b>X2</b>: the FASTA sequence of a target\n
+<b>ID1</b>: the ID (PubChem or any arbitrary unique identifier) of a compound\n
+<b>ID22</b>: the ID (UniProt or any arbitrary unique identifier) of a target
+
+Example:
+
+| X1                                      | X2            | ID1          | ID2    |
+|---------------------------------------- |---------------|--------------|--------|
+| CCOC(=O)Nc1ccc(NCc2ccc(F)cc2)cc1N       | MVQKSRNGGV... | CHEMBL41355  | O88943 |
+| CCCCCc1cc(O)c(C/C=C(\C)CCC=C(C)C)c(O)c1 | MTSPSSSPVF... | CHEMBL497318 | Q9Y5S1 |
+                        """)
+                        gr.File(label="Example custom dataset",
+                                value="data/examples/interaction_pair_inference.csv",
+                                interactive=False)
+                        with gr.Column():
+                            infer_data_for_predict = gr.File(
+                                label='Custom dataset file', file_count="single", type='filepath', visible=True)
+                    with gr.Column() as pair_generate:
+                        gr.Markdown("""
+Upload a SDF file which contains multiple compounds of interest and a FASTA file which contains multiple targets of 
+interest. All combinations of drug-target pairs from these two files will be automatically generated and submitted to 
+a prediction job.
+                        """)
+                        pair_sdf = gr.File(label='SDF file containing multiple compounds')
+                        pair_fasta = gr.File(label='FASTA file containing multiple targets')
+
+
 
                     with gr.Row(visible=True):
                         pair_infer_task = gr.Dropdown(list(TASK_MAP.keys()), label='Task')
-                        HelpTip("Choose a preset model for making the predictions.")
                         pair_infer_preset = gr.Dropdown(list(PRESET_MAP.keys()), label='Preset')
-                        HelpTip("Choose the protein family of your target.")
                         pair_infer_target_family = gr.Dropdown(choices=['General'],
                                                                label='Target family',
                                                                value='General')
@@ -887,7 +925,9 @@ with (gr.Blocks(theme=theme, title='DeepScreen', css=CSS) as demo):
                 <center>
                 To compute chemical properties for the predictions of drug hit screening, 
                 target protein identification, and interaction pair inference. You may also upload 
-                your own dataset. 
+                your own dataset. The page shows only a preview report displaying at most 30 records 
+                (with top predicted DTI/DTA if reporting results from a prediction job). For a full report, please 
+                generate and download a raw data CSV or interactive table HTML file below.
                 </center>
                 ''')
                 with gr.Row():
@@ -907,10 +947,10 @@ with (gr.Blocks(theme=theme, title='DeepScreen', css=CSS) as demo):
 
                 with gr.Row():
                     with gr.Column():
-                        csv_generate = gr.Button(value='Generate raw data (CSV)')
+                        csv_generate = gr.Button(value='Generate raw data (CSV)', interactive=True)
                         csv_download_file = gr.File(label='Download raw data (CSV)', visible=False)
                     with gr.Column():
-                        html_generate = gr.Button(value='Generate report (HTML)')
+                        html_generate = gr.Button(value='Generate report (HTML)', interactive=True)
                         html_download_file = gr.File(label='Download report (HTML)', visible=False)
 
 
@@ -1133,22 +1173,16 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
                     if library_upload.endswith('.csv'):
                         screen_df = pd.read_csv(library_upload)
                     elif library_upload.endswith('.sdf'):
-                        suppl = Chem.ForwardSDMolSupplier(library_upload)
-                        screen_df = pd.DataFrame([Chem.MolToSmiles(mol) for mol in suppl], columns=['X1'])
+                        screen_df = PandasTools.LoadSDF(library_upload,
+                        smilesName='X1', molColName='Compound', includeFingerprints=True)
                     else:
-                        raise 'Currently only csv and sdf files are supported.'
+                        raise gr.Error('Currently only CSV and SDF files are supported.')
                     validate_columns(screen_df, ['X1'])
 
-                if not np.isin('ID1', screen_df.columns):
-                    screen_df['ID1'] = list(range(screen_df.shape[0]))
-                if not np.isin('ID2', screen_df.columns):
-                    screen_df['ID2'] = 'Input'
                 screen_df['X2'] = fasta
-                screen_df['Y'] = 0
-                screen_df = screen_df.loc[:, ['ID1', 'X1', 'ID2', 'X2', 'Y']]
 
                 job_id = uuid4()
-                temp_file = Path(f'{job_id}_input.csv').resolve()
+                temp_file = Path(f'temp/{job_id}_input.csv').resolve()
                 screen_df.to_csv(temp_file, index=False)
                 if temp_file.is_file():
                     return {screen_data_for_predict: str(temp_file),
@@ -1191,15 +1225,9 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
                     validate_columns(identify_df, ['X2'])
 
                 identify_df['X1'] = smiles
-                if not np.isin('ID1', identify_df.columns):
-                    identify_df['ID1'] = 'Input'
-                if not np.isin('ID2', identify_df.columns):
-                    identify_df['ID2'] = list(range(identify_df.shape[0]))
-                identify_df = identify_df.loc[:, ['ID1', 'X1', 'ID2', 'X2']]
-                identify_df['Y'] = 0
-                
+
                 job_id = uuid4()
-                temp_file = Path(f'{job_id}_input.csv').resolve()
+                temp_file = Path(f'temp/{job_id}_input.csv').resolve()
                 identify_df.to_csv(temp_file, index=False)
                 if temp_file.is_file():
                     return {identify_data_for_predict: str(temp_file),
@@ -1321,16 +1349,16 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
 
     def create_csv_raw_file(df, file_report):
         from datetime import datetime
-        now = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
+        now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
         filename = f"reports/{Path(file_report.name).stem}_DeepSEQreen_report_{now}.csv"
-        df.to_csv(filename, index=False)
+        df.drop(['Compound', 'Scaffold']).to_csv(filename, index=False)
         return gr.File(filename, visible=True)
 
 
     def create_html_report_file(df, file_report):
         from datetime import datetime
-        now = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
-        filename = f"reports/{Path(file_report.name).stem}_DeepSEQreen_report_{now}.csv"
+        now = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+        filename = f"reports/{Path(file_report.name).stem}_DeepSEQreen_report_{now}.html"
         create_html_report(df, filename)
         return gr.File(filename, visible=True)