Update app.py

app.py

@@ -54,7 +54,7 @@ import sascorer
 
 UNIPROT_ENDPOINT = 'https://rest.uniprot.org/uniprotkb/{query}'
 DATASET_MAX_LEN = 10_000
-SERVER_DATA_DIR = '/data'  # '/data'
+SERVER_DATA_DIR = os.getenv('DATA')  # '/data'
 
 CSS = """
 .help-tip {
@@ -608,9 +608,8 @@ using the job id. You will also receive an email notification once the job is do
                     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...',
+                                            f', and the results will expire by {expiry_time}.'
+                        if job.get('expiry_time') else '.' + f'Redirecting to the report page...',
                         pred_lookup_btn: gr.Button(visible=True),
                         pred_lookup_stop_btn: gr.Button(visible=False),
                         tabs: gr.Tabs(selected='Chemical Property Report'),
@@ -621,8 +620,7 @@ using the job id. You will also receive an email notification once the job is do
                     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 '.',
+                                            f' due to error: {error}.' if job.get('expiry_time') else '.',
                         pred_lookup_btn: gr.Button(visible=True),
                         pred_lookup_stop_btn: gr.Button(visible=False),
                         tabs: gr.Tabs(selected='Prediction Status Lookup'),
@@ -655,41 +653,42 @@ def submit_predict(predict_filepath, task, preset, target_family, state):
         preset = PRESET_MAP[preset]
 
         prediction_df = pd.DataFrame()
-        with hydra.initialize(version_base="1.3", config_path="configs", job_name="webserver_inference"):
-            cfg = hydra.compose(
-                config_name="webserver_inference",
-                overrides=[f"task={task}",
-                           f"preset={preset}",
-                           f"ckpt_path=resources/checkpoints/{preset}-{task}-{target_family}.ckpt",
-                           f"data.data_file='{str(predict_filepath)}'"])
-            # with concurrent.futures.ThreadPoolExecutor() as executor:
-            #     future = executor.submit(predict, cfg)
-            #     try:
-            #         predictions, _ = future.result(timeout=4*60*60)
-            #     except concurrent.futures.TimeoutError:
-            #         raise gr.Error("Prediction timed out.")
-            predictions, _ = predict(cfg)
-            predictions = [pd.DataFrame(prediction) for prediction in predictions]
-            prediction_df = pd.concat([prediction_df, pd.concat(predictions, ignore_index=True)])
-            prediction_df.set_index('N', inplace=True)
-            orig_df = pd.read_csv(
-                predict_filepath,
-                usecols=lambda x: x not in ['X1', 'ID1', 'Compound', 'Scaffold', 'Scaffold SMILES',
-                                            'X2', 'ID2',
-                                            'Y', 'Y^']
-            )
-            prediction_df = pd.merge(prediction_df, orig_df, left_index=True, right_index=True, how='left')
+        cfg = hydra.compose(
+            config_name="webserver_inference",
+            overrides=[f"task={task}",
+                       f"preset={preset}",
+                       f"ckpt_path=resources/checkpoints/{preset}-{task}-{target_family}.ckpt",
+                       f"data.data_file='{str(predict_filepath)}'"])
+        # with concurrent.futures.ThreadPoolExecutor() as executor:
+        #     future = executor.submit(predict, cfg)
+        #     try:
+        #         predictions, _ = future.result(timeout=4*60*60)
+        #     except concurrent.futures.TimeoutError:
+        #         raise gr.Error("Prediction timed out.")
+        predictions, _ = predict(cfg)
+        predictions = [pd.DataFrame(prediction) for prediction in predictions]
+        prediction_df = pd.concat([prediction_df, pd.concat(predictions, ignore_index=True)])
+        prediction_df.set_index('N', inplace=True)
+        orig_df = pd.read_csv(
+            predict_filepath,
+            usecols=lambda x: x not in ['X1', 'ID1', 'Compound', 'Scaffold', 'Scaffold SMILES',
+                                        'X2', 'ID2',
+                                        'Y', 'Y^']
+        )
+        prediction_df = pd.merge(prediction_df, orig_df, left_index=True, right_index=True, how='left')
 
-            prediction_df.to_csv(predictions_file)
-            status = "COMPLETED"
+        prediction_df.to_csv(predictions_file)
+        status = "COMPLETED"
+
+        return {run_state: False}
 
-            return {run_state: False}
     except Exception as e:
         gr.Warning(f"Prediction job failed due to error: {str(e)}")
         status = "FAILED"
         predictions_file = None
         error = str(e)
         return {run_state: False}
+
     finally:
         Job = Query()
         job_query = (Job.id == job_id)
@@ -1219,229 +1218,227 @@ with gr.Blocks(theme=theme, title='DeepSEQreen', css=CSS, delete_cache=(3600, 48
     with gr.Tabs() as tabs:
         with gr.TabItem(label='Drug Hit Screening', id='Drug Hit Screening'):
             gr.Markdown('''
-                # <center>Drug Hit Screening</center>
-                
-                <center>
-                To predict interactions or binding affinities of a single target against a compound library.
-                </center>
-                    ''')
-            with gr.Blocks() as screen_block:
+            # <center>Drug Hit Screening</center>
+            
+            <center>
+            To predict interactions or binding affinities of a single target against a compound library.
+            </center>
+            ''')
+            with gr.Row():
+                with gr.Column():
+                    HelpTip(
+                        "Enter (paste) a amino acid sequence below manually or upload a FASTA file. "
+                        "If multiple entities are in the FASTA, only the first will be used. "
+                        "Alternatively, enter a Uniprot ID or gene symbol with organism and click Query for "
+                        "the sequence."
+                    )
+                    target_input_type = gr.Dropdown(
+                        label='Step 1. Select Target Input Type and Input',
+                        choices=['Sequence', 'UniProt ID', 'Gene symbol'],
+                        info='Enter (paste) a FASTA string below manually or upload a FASTA file.',
+                        value='Sequence',
+                        scale=4, interactive=True
+                    )
+
+            with gr.Row():
+                target_id = gr.Textbox(show_label=False, visible=False,
+                                       interactive=True, scale=4,
+                                       info='Enter a UniProt ID and query.')
+                target_gene = gr.Textbox(
+                    show_label=False, visible=False,
+                    interactive=True, scale=4,
+                    info='Enter a gene symbol and query.')
+                target_organism = gr.Textbox(
+                    info='Organism scientific name (default: Homo sapiens).',
+                    placeholder='Homo sapiens', show_label=False,
+                    visible=False, interactive=True, scale=4, )
+                target_upload_btn = gr.UploadButton(label='Upload a FASTA File', type='binary',
+                                                    visible=True, variant='primary',
+                                                    size='lg')
+                target_paste_markdown = gr.Button(value='OR Paste Your Sequence Below',
+                                                  variant='secondary')
+                target_query_btn = gr.Button(value='Query the Sequence', variant='primary',
+                                             visible=False, scale=4)
+            # with gr.Row():
+            #     example_uniprot = gr.Button(value='Example: Q16539', elem_classes='example', visible=False)
+            #     example_gene = gr.Button(value='Example: MAPK14', elem_classes='example', visible=False)
+            example_fasta = gr.Button(value='Example: MAPK14 (Q16539)', elem_classes='example')
+            target_fasta = gr.Code(label='Input or Display FASTA', interactive=True, lines=5)
+            # with gr.Row():
+            #     with gr.Column():
+            # with gr.Column():
+            #     gr.File(label='Example FASTA file',
+            #             value='data/examples/MAPK14.fasta', interactive=False)
+
+            with gr.Row():
+                with gr.Column(min_width=200):
+                    HelpTip(
+                        "Click Auto-detect to identify the protein family using sequence alignment. "
+                        "This optional step allows applying a family-specific model instead of a all-family "
+                        "model (general). "
+                        "Manually select general if the alignment results are unsatisfactory."
+                    )
+                    drug_screen_target_family = gr.Dropdown(
+                        choices=list(TARGET_FAMILY_MAP.keys()),
+                        value='General',
+                        label='Step 2. Select Target Family (Optional)', interactive=True)
+                    target_family_detect_btn = gr.Button(value='OR Let Us Auto-Detect for You',
+                                                         variant='primary')
+                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, "
+                        "while affinity prediction directly estimates their binding strength measured using "
+                        "IC50."
+                    )
+                    drug_screen_task = gr.Dropdown(
+                        list(TASK_MAP.keys()),
+                        label='Step 3. Select the Prediction Task',
+                        value='Compound-Protein Interaction')
+                with gr.Column(min_width=200):
+                    HelpTip(
+                        "Select your preferred model, or click Recommend for the best-performing model based "
+                        "on the selected task, family, and whether the target was trained. "
+                        "Please refer to documentation for detailed benchmark results."
+                    )
+                    drug_screen_preset = gr.Dropdown(
+                        list(PRESET_MAP.keys()),
+                        label='Step 4. Select a Preset Model')
+                    screen_preset_recommend_btn = gr.Button(
+                        value='OR Let Us Recommend for You', variant='primary')
+
+            with gr.Row():
+                with gr.Column():
+                    HelpTip(
+                        "Select a preset compound library (e.g., DrugBank). "
+                        "Alternatively, upload a CSV file with a column named X1 containing compound SMILES, "
+                        "or use an SDF file (Max. 10,000 compounds per task). Example CSV and SDF files are "
+                        "provided below and can be downloaded by clicking the lower right corner."
+                    )
+                    drug_library = gr.Dropdown(
+                        label='Step 5. Select a Preset Compound Library',
+                        choices=list(DRUG_LIBRARY_MAP.keys()))
+                    with gr.Row():
+                        gr.File(label='Example SDF compound library',
+                                value='data/examples/compound_library.sdf', interactive=False)
+                        gr.File(label='Example CSV compound library',
+                                value='data/examples/compound_library.csv', interactive=False)
+                    drug_library_upload_btn = gr.UploadButton(
+                        label='OR Upload Your Own Library', variant='primary')
+                    drug_library_upload = gr.File(label='Custom compound library file', visible=False)
+            with gr.Row():
+                with gr.Column():
+                    drug_screen_email = gr.Textbox(
+                        label='Step 6. 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."
+                    )
+
+            with gr.Row(visible=True):
+                with gr.Column():
+                    # drug_screen_clr_btn = gr.ClearButton(size='lg')
+                    drug_screen_btn = gr.Button(value='SUBMIT THE SCREENING JOB', variant='primary', size='lg')
+            # TODO Modify the pd df directly with df['X2'] = target
+
+        screen_data_for_predict = gr.File(visible=False, file_count="single", type='filepath')
+
+        with gr.TabItem(label='Target Protein Identification', id='Target Protein Identification'):
+            gr.Markdown('''
+            # <center>Target Protein Identification</center>
+            
+            <center>
+            To predict interactions or binding affinities of a single compound against a protein library.
+            </center>
+            ''')
+            with gr.Column() as identify_page:
                 with gr.Row():
                     with gr.Column():
                         HelpTip(
-                            "Enter (paste) a amino acid sequence below manually or upload a FASTA file. "
-                            "If multiple entities are in the FASTA, only the first will be used. "
-                            "Alternatively, enter a Uniprot ID or gene symbol with organism and click Query for "
-                            "the sequence."
-                        )
-                        target_input_type = gr.Dropdown(
-                            label='Step 1. Select Target Input Type and Input',
-                            choices=['Sequence', 'UniProt ID', 'Gene symbol'],
-                            info='Enter (paste) a FASTA string below manually or upload a FASTA file.',
-                            value='Sequence',
-                            scale=4, interactive=True
+                            "Enter (paste) a compound SMILES below manually or upload a SDF file. "
+                            "If multiple entities are in the SDF, only the first will be used. "
+                            "SMILES can be obtained by searching for the compound of interest in databases such "
+                            "as NCBI, PubChem and and ChEMBL."
                         )
+                        compound_type = gr.Dropdown(
+                            label='Step 1. Select Compound Input Type and Input',
+                            choices=['SMILES', 'SDF'],
+                            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_smiles = gr.Code(label='Input or Display Compound SMILES', interactive=True, lines=5)
+                example_drug = gr.Button(value='Example: Aspirin', elem_classes='example')
 
                 with gr.Row():
-                    target_id = gr.Textbox(show_label=False, visible=False,
-                                           interactive=True, scale=4,
-                                           info='Enter a UniProt ID and query.')
-                    target_gene = gr.Textbox(
-                        show_label=False, visible=False,
-                        interactive=True, scale=4,
-                        info='Enter a gene symbol and query.')
-                    target_organism = gr.Textbox(
-                        info='Organism scientific name (default: Homo sapiens).',
-                        placeholder='Homo sapiens', show_label=False,
-                        visible=False, interactive=True, scale=4, )
-                    target_upload_btn = gr.UploadButton(label='Upload a FASTA File', type='binary',
-                                                        visible=True, variant='primary',
-                                                        size='lg')
-                    target_paste_markdown = gr.Button(value='OR Paste Your Sequence Below',
-                                                      variant='secondary')
-                    target_query_btn = gr.Button(value='Query the Sequence', variant='primary',
-                                                 visible=False, scale=4)
-                # with gr.Row():
-                #     example_uniprot = gr.Button(value='Example: Q16539', elem_classes='example', visible=False)
-                #     example_gene = gr.Button(value='Example: MAPK14', elem_classes='example', visible=False)
-                example_fasta = gr.Button(value='Example: MAPK14 (Q16539)', elem_classes='example')
-                target_fasta = gr.Code(label='Input or Display FASTA', interactive=True, lines=5)
-                # with gr.Row():
-                #     with gr.Column():
-                # with gr.Column():
-                #     gr.File(label='Example FASTA file',
-                #             value='data/examples/MAPK14.fasta', interactive=False)
+                    with gr.Column(visible=False):
+                        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."
+                        )
+                        target_identify_target_family = gr.Dropdown(
+                            choices=['General'], value='General',
+                            label='Step 2. Select Target Family (Optional)')
 
                 with gr.Row():
-                    with gr.Column(min_width=200):
+                    with gr.Column():
                         HelpTip(
-                            "Click Auto-detect to identify the protein family using sequence alignment. "
-                            "This optional step allows applying a family-specific model instead of a all-family "
-                            "model (general). "
-                            "Manually select general if the alignment results are unsatisfactory."
+                            "Select a preset target library (e.g., ChEMBL33_human_proteins). "
+                            "Alternatively, upload a CSV file with a column named X2 containing target protein "
+                            "sequences, or use an FASTA file (Max. 10,000 targets per task). "
+                            "Example CSV and SDF files are provided below "
+                            "and can be downloaded by clicking the lower right corner."
                         )
-                        drug_screen_target_family = gr.Dropdown(
-                            choices=list(TARGET_FAMILY_MAP.keys()),
-                            value='General',
-                            label='Step 2. Select Target Family (Optional)', interactive=True)
-                        target_family_detect_btn = gr.Button(value='OR Let Us Auto-Detect for You',
-                                                             variant='primary')
-                    with gr.Column(min_width=200):
+                        target_library = gr.Dropdown(label='Step 3. 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)
+                            gr.File(label='Example CSV target library',
+                                    value='data/examples/target_library.csv', interactive=False)
+                        target_library_upload_btn = gr.UploadButton(
+                            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."
                         )
-                        drug_screen_task = gr.Dropdown(
+                        target_identify_task = gr.Dropdown(
                             list(TASK_MAP.keys()),
-                            label='Step 3. Select the Prediction Task',
+                            label='Step 4. Select the Prediction Task You Want to Conduct',
                             value='Compound-Protein Interaction')
-                    with gr.Column(min_width=200):
-                        HelpTip(
-                            "Select your preferred model, or click Recommend for the best-performing model based "
-                            "on the selected task, family, and whether the target was trained. "
-                            "Please refer to documentation for detailed benchmark results."
-                        )
-                        drug_screen_preset = gr.Dropdown(
-                            list(PRESET_MAP.keys()),
-                            label='Step 4. Select a Preset Model')
-                        screen_preset_recommend_btn = gr.Button(
-                            value='OR Let Us Recommend for You', variant='primary')
 
                 with gr.Row():
                     with gr.Column():
                         HelpTip(
-                            "Select a preset compound library (e.g., DrugBank). "
-                            "Alternatively, upload a CSV file with a column named X1 containing compound SMILES, "
-                            "or use an SDF file (Max. 10,000 compounds per task). Example CSV and SDF files are "
-                            "provided below and can be downloaded by clicking the lower right corner."
+                            "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."
                         )
-                        drug_library = gr.Dropdown(
-                            label='Step 5. Select a Preset Compound Library',
-                            choices=list(DRUG_LIBRARY_MAP.keys()))
-                        with gr.Row():
-                            gr.File(label='Example SDF compound library',
-                                    value='data/examples/compound_library.sdf', interactive=False)
-                            gr.File(label='Example CSV compound library',
-                                    value='data/examples/compound_library.csv', interactive=False)
-                        drug_library_upload_btn = gr.UploadButton(
-                            label='OR Upload Your Own Library', variant='primary')
-                        drug_library_upload = gr.File(label='Custom compound library file', visible=False)
+                        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():
-                        drug_screen_email = gr.Textbox(
+                        target_identify_email = gr.Textbox(
                             label='Step 6. 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."
                         )
 
                 with gr.Row(visible=True):
-                    with gr.Column():
-                        # drug_screen_clr_btn = gr.ClearButton(size='lg')
-                        drug_screen_btn = gr.Button(value='SUBMIT THE SCREENING JOB', variant='primary', size='lg')
-                # TODO Modify the pd df directly with df['X2'] = target
-
-        screen_data_for_predict = gr.File(visible=False, file_count="single", type='filepath')
-
-        with gr.TabItem(label='Target Protein Identification', id='Target Protein Identification'):
-            gr.Markdown('''
-                # <center>Target Protein Identification</center>
-                
-                <center>
-                To predict interactions or binding affinities of a single compound against a protein library.
-                </center>
-                ''')
-            with gr.Blocks() as identify_block:
-                with gr.Column() as identify_page:
-                    with gr.Row():
-                        with gr.Column():
-                            HelpTip(
-                                "Enter (paste) a compound SMILES below manually or upload a SDF file. "
-                                "If multiple entities are in the SDF, only the first will be used. "
-                                "SMILES can be obtained by searching for the compound of interest in databases such "
-                                "as NCBI, PubChem and and ChEMBL."
-                            )
-                            compound_type = gr.Dropdown(
-                                label='Step 1. Select Compound Input Type and Input',
-                                choices=['SMILES', 'SDF'],
-                                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_smiles = gr.Code(label='Input or Display Compound SMILES', interactive=True, lines=5)
-                    example_drug = gr.Button(value='Example: Aspirin', elem_classes='example')
-
-                    with gr.Row():
-                        with gr.Column(visible=False):
-                            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."
-                            )
-                            target_identify_target_family = gr.Dropdown(
-                                choices=['General'], value='General',
-                                label='Step 2. Select Target Family (Optional)')
-
-                    with gr.Row():
-                        with gr.Column():
-                            HelpTip(
-                                "Select a preset target library (e.g., ChEMBL33_human_proteins). "
-                                "Alternatively, upload a CSV file with a column named X2 containing target protein "
-                                "sequences, or use an FASTA file (Max. 10,000 targets per task). "
-                                "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()))
-                            with gr.Row():
-                                gr.File(label='Example FASTA target library',
-                                        value='data/examples/target_library.fasta', interactive=False)
-                                gr.File(label='Example CSV target library',
-                                        value='data/examples/target_library.csv', interactive=False)
-                            target_library_upload_btn = gr.UploadButton(
-                                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)',
-                                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."
-                            )
-
-                    with gr.Row(visible=True):
-                        # target_identify_clr_btn = gr.ClearButton(size='lg')
-                        target_identify_btn = gr.Button(value='SUBMIT THE IDENTIFICATION JOB', variant='primary',
-                                                        size='lg')
+                    # target_identify_clr_btn = gr.ClearButton(size='lg')
+                    target_identify_btn = gr.Button(value='SUBMIT THE IDENTIFICATION JOB', variant='primary',
+                                                    size='lg')
 
             identify_data_for_predict = gr.File(visible=False, file_count="single", type='filepath')
 
@@ -1452,100 +1449,99 @@ with gr.Blocks(theme=theme, title='DeepSEQreen', css=CSS, delete_cache=(3600, 48
             <center>To predict interactions or binding affinities between up to 
             10,000 paired compound-protein data.</center>
             ''')
-            with gr.Blocks() as infer_block:
-                HelpTip(
-                    "A custom interation pair dataset can be a CSV file with 2 required columns "
-                    "(X1 for smiles and X2 for sequences) "
-                    "and optionally 2 ID columns (ID1 for compound ID and ID2 for target ID), "
-                    "or generated from a FASTA file containing multiple "
-                    "sequences and a SDF file containing multiple compounds. "
-                    "Currently, a maximum of 10,000 pairs is supported, "
-                    "which means that the size of CSV file or "
-                    "the product of the two library sizes should not exceed 10,000."
-                )
-                infer_type = gr.Dropdown(
-                    choices=['Upload a CSV file containing paired compound-protein data',
-                             'Upload a compound library and a target library'],
-                    label='Step 1. Select Pair Input Type and Input',
-                    value='Upload a CSV file containing paired compound-protein data')
-                with gr.Column() as pair_upload:
-                    gr.File(label="Example CSV dataset",
-                            value="data/examples/interaction_pair_inference.csv",
-                            interactive=False)
-                    with gr.Row():
-                        infer_csv_prompt = gr.Button(
-                            value="Upload Your Own Dataset Below",
-                            variant='secondary')
-                    with gr.Column():
-                        infer_pair = gr.File(
-                            label='Upload CSV File Containing Paired Records',
-                            file_count="single", type='filepath', visible=True)
-                with gr.Column(visible=False) as pair_generate:
-                    with gr.Row():
-                        gr.File(label='Example SDF compound library',
-                                value='data/examples/compound_library.sdf', interactive=False)
-                        gr.File(label='Example FASTA target library',
-                                value='data/examples/target_library.fasta', interactive=False)
-                    with gr.Row():
-                        gr.File(label='Example CSV compound library',
-                                value='data/examples/compound_library.csv', interactive=False)
-                        gr.File(label='Example CSV target library',
-                                value='data/examples/target_library.csv', interactive=False)
-                    with gr.Row():
-                        infer_library_prompt = gr.Button(
-                            value="Upload Your Own Libraries Below",
-                            visible=False, variant='secondary')
-                    with gr.Row():
-                        infer_drug = gr.File(label='Upload SDF/CSV File Containing Multiple Compounds',
-                                             file_count="single", type='filepath')
-                        infer_target = gr.File(label='Upload FASTA/CSV File Containing Multiple Targets',
-                                               file_count="single", type='filepath')
-
+            HelpTip(
+                "A custom interation pair dataset can be a CSV file with 2 required columns "
+                "(X1 for smiles and X2 for sequences) "
+                "and optionally 2 ID columns (ID1 for compound ID and ID2 for target ID), "
+                "or generated from a FASTA file containing multiple "
+                "sequences and a SDF file containing multiple compounds. "
+                "Currently, a maximum of 10,000 pairs is supported, "
+                "which means that the size of CSV file or "
+                "the product of the two library sizes should not exceed 10,000."
+            )
+            infer_type = gr.Dropdown(
+                choices=['Upload a CSV file containing paired compound-protein data',
+                         'Upload a compound library and a target library'],
+                label='Step 1. Select Pair Input Type and Input',
+                value='Upload a CSV file containing paired compound-protein data')
+            with gr.Column() as pair_upload:
+                gr.File(label="Example CSV dataset",
+                        value="data/examples/interaction_pair_inference.csv",
+                        interactive=False)
                 with gr.Row():
-                    with gr.Column():
-                        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)')
-
+                    infer_csv_prompt = gr.Button(
+                        value="Upload Your Own Dataset Below",
+                        variant='secondary')
+                with gr.Column():
+                    infer_pair = gr.File(
+                        label='Upload CSV File Containing Paired Records',
+                        file_count="single", type='filepath', visible=True)
+            with gr.Column(visible=False) as pair_generate:
                 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."
-                        )
-                        pair_infer_task = gr.Dropdown(
-                            list(TASK_MAP.keys()),
-                            label='Step 3. Select the Prediction Task You Want to Conduct',
-                            value='Compound-Protein Interaction')
-
+                    gr.File(label='Example SDF compound library',
+                            value='data/examples/compound_library.sdf', interactive=False)
+                    gr.File(label='Example FASTA target library',
+                            value='data/examples/target_library.fasta', interactive=False)
                 with gr.Row():
-                    with gr.Column():
-                        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')
-                        # infer_preset_recommend_btn = gr.Button(value='OR Let Us Recommend for You',
-                        #                                        variant='primary')
-
+                    gr.File(label='Example CSV compound library',
+                            value='data/examples/compound_library.csv', interactive=False)
+                    gr.File(label='Example CSV target library',
+                            value='data/examples/target_library.csv', interactive=False)
                 with gr.Row():
-                    pair_infer_email = gr.Textbox(
-                        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.")
+                    infer_library_prompt = gr.Button(
+                        value="Upload Your Own Libraries Below",
+                        visible=False, variant='secondary')
+                with gr.Row():
+                    infer_drug = gr.File(label='Upload SDF/CSV File Containing Multiple Compounds',
+                                         file_count="single", type='filepath')
+                    infer_target = gr.File(label='Upload FASTA/CSV File Containing Multiple Targets',
+                                           file_count="single", type='filepath')
 
-                with gr.Row(visible=True):
-                    # pair_infer_clr_btn = gr.ClearButton(size='lg')
-                    pair_infer_btn = gr.Button(value='SUBMIT THE INFERENCE JOB', variant='primary', size='lg')
+            with gr.Row():
+                with gr.Column():
+                    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)')
+
+            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."
+                    )
+                    pair_infer_task = gr.Dropdown(
+                        list(TASK_MAP.keys()),
+                        label='Step 3. Select the Prediction Task You Want to Conduct',
+                        value='Compound-Protein Interaction')
+
+            with gr.Row():
+                with gr.Column():
+                    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')
+                    # infer_preset_recommend_btn = gr.Button(value='OR Let Us Recommend for You',
+                    #                                        variant='primary')
+
+            with gr.Row():
+                pair_infer_email = gr.Textbox(
+                    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.")
 
-                infer_data_for_predict = gr.File(file_count="single", type='filepath', visible=False)
+            with gr.Row(visible=True):
+                # pair_infer_clr_btn = gr.ClearButton(size='lg')
+                pair_infer_btn = gr.Button(value='SUBMIT THE INFERENCE JOB', variant='primary', size='lg')
+
+            infer_data_for_predict = gr.File(file_count="single", type='filepath', visible=False)
 
         with gr.TabItem(label='Chemical Property Report', id='Chemical Property Report'):
             gr.Markdown('''
@@ -1563,36 +1559,35 @@ with gr.Blocks(theme=theme, title='DeepSEQreen', css=CSS, delete_cache=(3600, 48
             Please first `Preview` the report, then `Generate` and download a CSV report 
             or an interactive HTML report below if you wish to access the full report.
             ''')
-            with gr.Blocks() as report_block:
-                with gr.Row():
-                    with gr.Column():
-                        file_for_report = gr.File(interactive=True, type='filepath')
-                        report_task = gr.Dropdown(list(TASK_MAP.keys()), visible=False, value=None,
-                                                  label='Specify the Task for the Labels in the Upload Dataset')
-                    raw_df = gr.State(value=pd.DataFrame())
-                    report_df = gr.State(value=pd.DataFrame())
-                    scores = gr.CheckboxGroup(list(SCORE_MAP.keys()), label='Scores')
-                    filters = gr.CheckboxGroup(list(FILTER_MAP.keys()), label='Filters')
-
-                with gr.Row():
-                    # clear_btn = gr.ClearButton(size='lg')
-                    analyze_btn = gr.Button('Preview Top 30 Records', variant='primary', size='lg',
-                                            interactive=False)
-
-                with gr.Row():
-                    with gr.Column(scale=3):
-                        html_report = gr.HTML()  # label='Results', visible=True)
-                    ranking_pie_chart = gr.Plot(visible=False)
-
-                with gr.Row():
-                    with gr.Column():
-                        csv_generate = gr.Button(value='Generate CSV Report',
-                                                 interactive=False, variant='primary')
-                        csv_download_file = gr.File(label='Download CSV Report', visible=False)
-                    with gr.Column():
-                        html_generate = gr.Button(value='Generate HTML Report',
-                                                  interactive=False, variant='primary')
-                        html_download_file = gr.File(label='Download HTML Report', visible=False)
+            with gr.Row():
+                with gr.Column():
+                    file_for_report = gr.File(interactive=True, type='filepath')
+                    report_task = gr.Dropdown(list(TASK_MAP.keys()), visible=False, value=None,
+                                              label='Specify the Task for the Labels in the Upload Dataset')
+                raw_df = gr.State(value=pd.DataFrame())
+                report_df = gr.State(value=pd.DataFrame())
+                scores = gr.CheckboxGroup(list(SCORE_MAP.keys()), label='Scores')
+                filters = gr.CheckboxGroup(list(FILTER_MAP.keys()), label='Filters')
+
+            with gr.Row():
+                # clear_btn = gr.ClearButton(size='lg')
+                analyze_btn = gr.Button('Preview Top 30 Records', variant='primary', size='lg',
+                                        interactive=False)
+
+            with gr.Row():
+                with gr.Column(scale=3):
+                    html_report = gr.HTML()  # label='Results', visible=True)
+                ranking_pie_chart = gr.Plot(visible=False)
+
+            with gr.Row():
+                with gr.Column():
+                    csv_generate = gr.Button(value='Generate CSV Report',
+                                             interactive=False, variant='primary')
+                    csv_download_file = gr.File(label='Download CSV Report', visible=False)
+                with gr.Column():
+                    html_generate = gr.Button(value='Generate HTML Report',
+                                              interactive=False, variant='primary')
+                    html_download_file = gr.File(label='Download HTML Report', visible=False)
 
         with gr.TabItem(label='Prediction Status Lookup', id='Prediction Status Lookup'):
             gr.Markdown('''
@@ -1604,15 +1599,14 @@ with gr.Blocks(theme=theme, title='DeepSEQreen', css=CSS, delete_cache=(3600, 48
             You will be redirected to Chemical Property Report for carrying out further analysis and 
             generating the full report if the job is done.
             ''')
-            with gr.Blocks() as lookup_block:
-                with gr.Column():
-                    pred_lookup_id = gr.Textbox(
-                        label='Input Your Job ID', placeholder='e.g., e9dfd149-3f5c-48a6-b797-c27d027611ac',
-                        info="Your job ID is a UUID4 string that you receive after submitting a job on the "
-                             "page or in the email notification.")
-                    pred_lookup_btn = gr.Button(value='Lookup the Job Status', variant='primary', visible=True)
-                    pred_lookup_stop_btn = gr.Button(value='Stop Tracking', variant='stop', visible=False)
-                    pred_lookup_status = gr.Markdown()
+            with gr.Column():
+                pred_lookup_id = gr.Textbox(
+                    label='Input Your Job ID', placeholder='e.g., e9dfd149-3f5c-48a6-b797-c27d027611ac',
+                    info="Your job ID is a UUID4 string that you receive after submitting a job on the "
+                         "page or in the email notification.")
+                pred_lookup_btn = gr.Button(value='Lookup the Job Status', variant='primary', visible=True)
+                pred_lookup_stop_btn = gr.Button(value='Stop Tracking', variant='stop', visible=False)
+                pred_lookup_status = gr.Markdown()
 
                 # retrieve_email = gr.Textbox(label='Step 2. Input Your Email Address', placeholder='e.g.,
 
@@ -2055,7 +2049,8 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
         fn=drug_screen_validate,
         inputs=[target_fasta, drug_library, drug_library_upload, drug_screen_preset, drug_screen_task,
                 drug_screen_email, run_state],
-        outputs=[screen_data_for_predict, run_state]
+        outputs=[screen_data_for_predict, run_state],
+        concurrency_limit=2,
     )
 
     drug_screen_lookup = drug_screen_click.success(
@@ -2066,7 +2061,8 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
         fn=lookup_job,
         inputs=[pred_lookup_id],
         outputs=[pred_lookup_status, pred_lookup_btn, pred_lookup_stop_btn, tabs, file_for_report],
-        show_progress='hidden'
+        show_progress='minimal',
+        concurrency_limit=100
     )
 
     drug_screen_click.success(
@@ -2085,7 +2081,8 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
         fn=target_identify_validate,
         inputs=[compound_smiles, target_library, target_library_upload, target_identify_preset, target_identify_task,
                 target_identify_email, run_state],
-        outputs=[identify_data_for_predict, run_state]
+        outputs=[identify_data_for_predict, run_state],
+        concurrency_limit=2,
     )
 
     target_identify_lookup = target_identify_click.success(
@@ -2096,7 +2093,8 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
         fn=lookup_job,
         inputs=[pred_lookup_id],
         outputs=[pred_lookup_status, pred_lookup_btn, pred_lookup_stop_btn, tabs, file_for_report],
-        show_progress='hidden'
+        show_progress='minimal',
+        concurrency_limit=100
     )
 
     target_identify_click.success(
@@ -2116,7 +2114,7 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
         inputs=[infer_pair, infer_drug, infer_target, pair_infer_preset, pair_infer_task,
                 pair_infer_email, run_state],
         outputs=[infer_data_for_predict, run_state],
-        queue=False
+        concurrency_limit=2,
     )
 
     pair_infer_lookup = pair_infer_click.success(
@@ -2127,7 +2125,8 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
         fn=lookup_job,
         inputs=[pred_lookup_id],
         outputs=[pred_lookup_status, pred_lookup_btn, pred_lookup_stop_btn, tabs, file_for_report],
-        show_progress='hidden'
+        show_progress='minimal',
+        concurrency_limit=100
     )
 
     pair_infer_click.success(
@@ -2146,14 +2145,15 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
         fn=lookup_job,
         inputs=[pred_lookup_id],
         outputs=[pred_lookup_status, pred_lookup_btn, pred_lookup_stop_btn, tabs, file_for_report],
-        show_progress='hidden'
+        show_progress='minimal',
+        concurrency_limit=100,
     )
 
     pred_lookup_stop_btn.click(
         fn=lambda: [gr.Button(visible=True), gr.Button(visible=False)],
         outputs=[pred_lookup_btn, pred_lookup_stop_btn],
         cancels=[pred_lookup_click, drug_screen_lookup, target_identify_lookup, pair_infer_lookup],
-        queue=False
+        concurrency_limit=None,
     )
 
 
@@ -2177,12 +2177,14 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
 
 
     report_df_change = file_for_report.change(
-        fn=update_df, inputs=file_for_report, outputs=[html_report, raw_df, report_df, analyze_btn, report_task]
+        fn=update_df, inputs=file_for_report, outputs=[html_report, raw_df, report_df, analyze_btn, report_task],
+        concurrency_limit=100,
     )
 
     file_for_report.upload(
         fn=update_df, inputs=file_for_report, outputs=[html_report, raw_df, report_df, analyze_btn, report_task],
-        cancels=[report_df_change]
+        cancels=[report_df_change],
+        concurrency_limit=100,
     ).then(
         fn=inquire_task, inputs=[raw_df],
         outputs=[report_task, html_report, analyze_btn, csv_generate, html_generate],
@@ -2196,12 +2198,17 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
                     gr.Button(interactive=False)],
         outputs=[
             csv_generate, html_generate, csv_download_file, html_download_file, report_task, html_report, analyze_btn
-        ])
+        ]
+    )
 
-    analyze_btn.click(fn=submit_report, inputs=[raw_df, scores, filters, report_task], outputs=[
-        html_report, report_df, csv_download_file, html_download_file
-    ]).success(fn=lambda: [gr.Button(interactive=True)] * 2,
-               outputs=[csv_generate, html_generate])
+    analyze_btn.click(
+        fn=submit_report, inputs=[raw_df, scores, filters, report_task], outputs=[
+            html_report, report_df, csv_download_file, html_download_file]
+    ).success(
+        fn=lambda: [gr.Button(interactive=True)] * 2,
+        outputs=[csv_generate, html_generate],
+        concurrency_limit=100,
+    )
 
     report_task.select(fn=lambda: gr.Button(interactive=True),
                        outputs=analyze_btn)
@@ -2241,9 +2248,5 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
            outputs=html_download_file, show_progress='full')
 
 if __name__ == "__main__":
-    screen_block.queue(default_concurrency_limit=2, max_size=10)
-    identify_block.queue(default_concurrency_limit=2, max_size=10)
-    infer_block.queue(default_concurrency_limit=2, max_size=10)
-    report_block.queue(default_concurrency_limit=10, max_size=10)
-
-    demo.launch(show_api=False)
+    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)