Update app.py

app.py

@@ -505,9 +505,6 @@ def send_email(job_info):
             gr.Info('Email notification sent successfully.')
         except Exception as e:
             gr.Warning('Failed to send email notification due to error: ' + str(e))
-    else:
-        gr.Info('You won\'t receive an email notification as you haven\'t provided an email address. '
-                'Please make sure you take note of the job ID.')
 
 
 def check_user_running_job(email, request):
@@ -574,6 +571,7 @@ def ts_to_str(ts, timezone_str):
 
 
 def lookup_job(job_id):
+    gr.Info('Start querying the job database...')
     stop = False
     while not stop:
         try:
@@ -605,11 +603,14 @@ using the job id. You will also receive an email notification once the job is do
                     }
                 if job_status == "COMPLETED":
                     stop = True
+                    msg = f"Your {job_type} job (ID: {job_id}) has been **COMPLETED**"
+                    msg += f" at {end_time}" if job.get('end_time') else ""
+                    msg += f" and the results will expire by {expiry_time}." if job.get('expiry_time') else "."
+                    msg += f' Redirecting to the report page...'
+
+                    gr.Info(msg)
                     yield {
-                        pred_lookup_status: f'Your {job_type} job (ID: {job_id}) has been **COMPLETED**' +
-                                            f' at {end_time}' if job.get('end_time') else '' +
-                                            f', and the results will expire by {expiry_time}.'
-                        if job.get('expiry_time') else '.' + f'Redirecting to the report page...',
+                        pred_lookup_status: msg,
                         pred_lookup_btn: gr.Button(visible=True),
                         pred_lookup_stop_btn: gr.Button(visible=False),
                         tabs: gr.Tabs(selected='Chemical Property Report'),
@@ -617,18 +618,22 @@ using the job id. You will also receive an email notification once the job is do
                     }
                 if job_status == "FAILED":
                     stop = True
+                    msg = f'Your {job_type} job (ID: {job_id}) has **FAILED**'
+                    msg += f' at {end_time}' if job.get('end_time') else ''
+                    msg += f' due to error: {error}.' if job.get('expiry_time') else '.'
+                    gr.Info(msg)
                     yield {
-                        pred_lookup_status: f'Your {job_type} job (ID: {job_id}) has **FAILED**' +
-                                            f' at {end_time}' if job.get('end_time') else '' +
-                                            f' due to error: {error}.' if job.get('expiry_time') else '.',
+                        pred_lookup_status: msg,
                         pred_lookup_btn: gr.Button(visible=True),
                         pred_lookup_stop_btn: gr.Button(visible=False),
                         tabs: gr.Tabs(selected='Prediction Status Lookup'),
                     }
             else:
                 stop = True
+                msg = f'Job ID {job_id} not found.'
+                gr.Info(msg)
                 yield {
-                    pred_lookup_status: f'Job ID {job_id} not found.',
+                    pred_lookup_status: msg,
                     pred_lookup_btn: gr.Button(visible=True),
                     pred_lookup_stop_btn: gr.Button(visible=False),
                     tabs: gr.Tabs(selected='Prediction Status Lookup'),
@@ -1186,27 +1191,26 @@ def target_library_from_fasta(fasta_path):
     return df
 
 
-theme = gr.themes.Base(spacing_size="sm", text_size='md').set(
-    background_fill_primary='#dfe6f0',
+theme = gr.themes.Base(spacing_size="sm", text_size='md', font=gr.themes.GoogleFont("Roboto")).set(
+    background_fill_primary='#eef3f9',
     background_fill_secondary='white',
-    checkbox_label_background_fill='#dfe6f0',
+    checkbox_label_background_fill='#eef3f9',
     checkbox_label_background_fill_hover='#dfe6f0',
     checkbox_background_color='white',
     checkbox_border_color='#4372c4',
     border_color_primary='#4372c4',
-    border_color_accent='#4372c4',
-    button_primary_background_fill='#4372c4',
+    border_color_accent='#2e6ab5',
+    button_primary_background_fill='#2e6ab4',
     button_primary_text_color='white',
-    button_secondary_border_color='#4372c4',
-    body_text_color='#4372c4',
-    block_title_text_color='#4372c4',
-    block_label_text_color='#4372c4',
+    body_text_color='#28496F',
+    block_background_fill='#fbfcfd',
+    block_title_text_color='#28496F',
+    block_label_text_color='#28496F',
     block_info_text_color='#505358',
     block_border_color=None,
-    input_border_color='#4372c4',
-    panel_border_color='#4372c4',
-    input_background_fill='white',
-    code_background_fill='white',
+    # input_border_color='#4372c4',
+    # panel_border_color='#4372c4',
+    input_background_fill='#F1F2F4',
 )
 
 with gr.Blocks(theme=theme, title='DeepSEQreen', css=CSS, delete_cache=(3600, 48 * 3600)) as demo:
@@ -1247,7 +1251,7 @@ with gr.Blocks(theme=theme, title='DeepSEQreen', css=CSS, delete_cache=(3600, 48
                 target_gene = gr.Textbox(
                     show_label=False, visible=False,
                     interactive=True, scale=4,
-                    info='Enter a gene symbol and query.')
+                    info='Enter a gene symbol and query. The first record is used by default.')
                 target_organism = gr.Textbox(
                     info='Organism scientific name (default: Homo sapiens).',
                     placeholder='Homo sapiens', show_label=False,
@@ -1293,7 +1297,7 @@ with gr.Blocks(theme=theme, title='DeepSEQreen', css=CSS, delete_cache=(3600, 48
                     )
                     drug_screen_task = gr.Dropdown(
                         list(TASK_MAP.keys()),
-                        label='Step 3. Select the Prediction Task',
+                        label='Step 3. Select a Prediction Task',
                         value='Compound-Protein Interaction')
                 with gr.Column(min_width=200):
                     HelpTip(
@@ -1365,8 +1369,8 @@ with gr.Blocks(theme=theme, title='DeepSEQreen', css=CSS, delete_cache=(3600, 48
                             info='Enter (paste) an SMILES string or upload an SDF file to convert to SMILES.',
                             value='SMILES',
                             interactive=True)
-                        compound_upload_btn = gr.UploadButton(label='OR Upload a SDF File', variant='primary',
-                                                              type='binary', visible=False)
+                        compound_upload_btn = gr.UploadButton(
+                            label='OR Upload a SDF File', variant='primary', type='binary', visible=False)
 
                 compound_smiles = gr.Code(label='Input or Display Compound SMILES', interactive=True, lines=5)
                 example_drug = gr.Button(value='Example: Aspirin', elem_classes='example')
@@ -1380,8 +1384,32 @@ with gr.Blocks(theme=theme, title='DeepSEQreen', css=CSS, delete_cache=(3600, 48
                         )
                         target_identify_target_family = gr.Dropdown(
                             choices=['General'], value='General',
-                            label='Step 2. Select Target Family (Optional)')
+                            label='Target Family')
 
+                with gr.Row():
+                    with gr.Column():
+                        HelpTip(
+                            "Interaction prediction provides you binding probability score between the target of "
+                            "interest and each compound in the library, while affinity prediction directly "
+                            "estimates their binding strength measured using IC50."
+                        )
+                        target_identify_task = gr.Dropdown(
+                            list(TASK_MAP.keys()),
+                            label='Step 2. Select a Prediction Task',
+                            value='Compound-Protein Interaction')
+
+                    with gr.Column():
+                        HelpTip(
+                            "Select your preferred model, or click Recommend for the best-performing model based "
+                            "on the selected task and whether the compound was trained. By default, General-trained "
+                            "model is used for Target Protein Identification. "
+                            "Please refer to the documentation for detailed benchmark results."
+                        )
+                        target_identify_preset = gr.Dropdown(
+                            list(PRESET_MAP.keys()),
+                            label='Step 3. Select a Preset Model')
+                        identify_preset_recommend_btn = gr.Button(value='OR Let Us Recommend for You',
+                                                                  variant='primary')
                 with gr.Row():
                     with gr.Column():
                         HelpTip(
@@ -1391,8 +1419,9 @@ with gr.Blocks(theme=theme, title='DeepSEQreen', css=CSS, delete_cache=(3600, 48
                             "Example CSV and SDF files are provided below "
                             "and can be downloaded by clicking the lower right corner."
                         )
-                        target_library = gr.Dropdown(label='Step 3. Select a Preset Target Library',
-                                                     choices=list(TARGET_LIBRARY_MAP.keys()))
+                        target_library = gr.Dropdown(
+                            label='Step 4. Select a Preset Target Library',
+                            choices=list(TARGET_LIBRARY_MAP.keys()))
                         with gr.Row():
                             gr.File(label='Example FASTA target library',
                                     value='data/examples/target_library.fasta', interactive=False)
@@ -1402,35 +1431,10 @@ with gr.Blocks(theme=theme, title='DeepSEQreen', css=CSS, delete_cache=(3600, 48
                             label='OR Upload Your Own Library', variant='primary')
                         target_library_upload = gr.File(label='Custom target library file', visible=False)
 
-                with gr.Row():
-                    with gr.Column():
-                        HelpTip(
-                            "Interaction prediction provides you binding probability score between the target of "
-                            "interest and each compound in the library, "
-                            "while affinity prediction directly estimates their binding strength measured using "
-                            "IC50."
-                        )
-                        target_identify_task = gr.Dropdown(
-                            list(TASK_MAP.keys()),
-                            label='Step 4. Select the Prediction Task You Want to Conduct',
-                            value='Compound-Protein Interaction')
-
-                with gr.Row():
-                    with gr.Column():
-                        HelpTip(
-                            "Select your preferred model, or click Recommend for the best-performing model based "
-                            "on the selected task, family, and whether the compound was trained. "
-                            "Please refer to documentation for detailed benchamrk results."
-                        )
-                        target_identify_preset = gr.Dropdown(list(PRESET_MAP.keys()),
-                                                             label='Step 5. Select a Preset Model')
-                        identify_preset_recommend_btn = gr.Button(value='OR Let Us Recommend for You',
-                                                                  variant='primary')
-
                 with gr.Row():
                     with gr.Column():
                         target_identify_email = gr.Textbox(
-                            label='Step 6. Input Your Email Address (Optional)',
+                            label='Step 5. Input Your Email Address (Optional)',
                             info="Your email address will be used to notify you of the status of your job. "
                                  "If you cannot receive the email, please check your spam/junk folder."
                         )
@@ -1498,18 +1502,18 @@ with gr.Blocks(theme=theme, title='DeepSEQreen', css=CSS, delete_cache=(3600, 48
                                            file_count="single", type='filepath')
 
             with gr.Row():
-                with gr.Column():
+                with gr.Column(min_width=200):
                     HelpTip(
                         "By default, models trained on all protein families (general) will be applied. "
                         "If the proteins in the target library of interest "
                         "all belong to the same protein family, manually selecting the family is supported."
                     )
-                    pair_infer_target_family = gr.Dropdown(choices=list(TARGET_FAMILY_MAP.keys()),
-                                                           value='General',
-                                                           label='Step 2. Select Target Family (Optional)')
+                    pair_infer_target_family = gr.Dropdown(
+                        choices=list(TARGET_FAMILY_MAP.keys()),
+                        value='General',
+                        label='Step 2. Select Target Family (Optional)')
 
-            with gr.Row():
-                with gr.Column():
+                with gr.Column(min_width=200):
                     HelpTip(
                         "Interaction prediction provides you binding probability score "
                         "between the target of interest and each compound in the library, "
@@ -1518,16 +1522,16 @@ with gr.Blocks(theme=theme, title='DeepSEQreen', css=CSS, delete_cache=(3600, 48
                     )
                     pair_infer_task = gr.Dropdown(
                         list(TASK_MAP.keys()),
-                        label='Step 3. Select the Prediction Task You Want to Conduct',
+                        label='Step 3. Select a Prediction Task',
                         value='Compound-Protein Interaction')
 
-            with gr.Row():
-                with gr.Column():
+                with gr.Column(min_width=200):
                     HelpTip("Select your preferred model. "
                             "Please refer to documentation for detailed benchmark results."
                             )
-                    pair_infer_preset = gr.Dropdown(list(PRESET_MAP.keys()),
-                                                    label='Step 4. Select a Preset Model')
+                    pair_infer_preset = gr.Dropdown(
+                        list(PRESET_MAP.keys()),
+                        label='Step 4. Select a Preset Model')
                     # infer_preset_recommend_btn = gr.Button(value='OR Let Us Recommend for You',
                     #                                        variant='primary')
 
@@ -1597,7 +1601,8 @@ with gr.Blocks(theme=theme, title='DeepSEQreen', css=CSS, delete_cache=(3600, 48
             if the job has completed. Note that predictions are only kept for 48 hours upon job completion.
             
             You will be redirected to Chemical Property Report for carrying out further analysis and 
-            generating the full report if the job is done.
+            generating the full report if the job is done. If the Lookup fails to respond, please come back 
+            in five minutes, refresh the page, and try again. 
             ''')
             with gr.Column():
                 pred_lookup_id = gr.Textbox(
@@ -1655,7 +1660,7 @@ with gr.Blocks(theme=theme, title='DeepSEQreen', css=CSS, delete_cache=(3600, 48
 
 
     def uniprot_query(input_type, uid, gene, organism='Human'):
-        fasta_seq = ''
+        fasta_rec = ''
 
         match input_type:
             case 'UniProt ID':
@@ -1667,11 +1672,14 @@ with gr.Blocks(theme=theme, title='DeepSEQreen', css=CSS, delete_cache=(3600, 48
         try:
             fasta = session.get(UNIPROT_ENDPOINT.format(query=query))
             fasta.raise_for_status()
-            fasta_seq = fasta.text
+            if fasta.text:
+                fasta_rec = next(SeqIO.parse(io.StringIO(fasta.text), format='fasta'))
+                fasta_rec = f"{fasta_rec.description}\n{fasta_rec.seq}"
+
         except Exception as e:
             raise gr.Warning(f"Failed to query FASTA from UniProt database due to {str(e)}")
         finally:
-            return fasta_seq
+            return fasta_rec
 
 
     def process_fasta_upload(fasta_upload):
@@ -1889,10 +1897,12 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
 
     infer_type.select(fn=infer_type_change, inputs=infer_type,
                       outputs=[pair_upload, pair_generate, infer_pair, infer_drug, infer_target,
-                               infer_csv_prompt, infer_library_prompt])
+                               infer_csv_prompt, infer_library_prompt],
+                      show_progress='hidden')
 
 
     def common_input_validate(state, preset, email, request):
+        gr.Info('Start processing inputs...')
         if not preset:
             raise gr.Error('Please select a model.')
 
@@ -1914,7 +1924,7 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
 
 
     def common_job_initiate(job_id, job_type, email, request, task):
-        gr.Info('Finished input validation. Initiating the prediction job... '
+        gr.Info('Finished processing inputs. Initiating the prediction job... '
                 'You will be redirected to Prediction Status Lookup after the job is submitted.')
         job_info = {'id': job_id,
                     'type': job_type,
@@ -2062,7 +2072,7 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
         inputs=[pred_lookup_id],
         outputs=[pred_lookup_status, pred_lookup_btn, pred_lookup_stop_btn, tabs, file_for_report],
         show_progress='minimal',
-        concurrency_limit=100
+        concurrency_limit=100,
     )
 
     drug_screen_click.success(
@@ -2146,6 +2156,7 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
         inputs=[pred_lookup_id],
         outputs=[pred_lookup_status, pred_lookup_btn, pred_lookup_stop_btn, tabs, file_for_report],
         show_progress='minimal',
+        cancels=[drug_screen_lookup, target_identify_lookup, pair_infer_lookup],
         concurrency_limit=100,
     )
 
@@ -2191,13 +2202,12 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
     )
 
     file_for_report.clear(
-        fn=lambda: [gr.Button(visible=False)] * 2 +
+        fn=lambda: [gr.Button(interactive=False)] * 3 +
                    [gr.File(visible=False, value=None)] * 2 +
                    [gr.Dropdown(visible=False, value=None),
-                    gr.HTML(visible=False),
-                    gr.Button(interactive=False)],
+                    gr.HTML(visible=False)],
         outputs=[
-            csv_generate, html_generate, csv_download_file, html_download_file, report_task, html_report, analyze_btn
+            csv_generate, html_generate, analyze_btn, csv_download_file, html_download_file, report_task, html_report
         ]
     )
 
@@ -2249,4 +2259,4 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
 
 if __name__ == "__main__":
     hydra.initialize(version_base="1.3", config_path="configs", job_name="webserver_inference")
-    demo.queue(default_concurrency_limit=None, max_size=10).launch(show_api=False, share=True)
+    demo.queue(default_concurrency_limit=None, max_size=10).launch(show_api=False)