Upload app.py

app.py

@@ -846,20 +846,20 @@ with gr.Blocks(theme=theme, title='DeepSEQreen', css=CSS) as demo:
     infer_flag = gr.State(value=False)
 
     with gr.Tabs() as tabs:
-        with gr.TabItem(label='Drug hit screening', id=0):
+        with gr.TabItem(label='Drug Hit Screening', id=0):
             gr.Markdown('''
-# <center>DeepSEQreen Drug Hit Screening</center>
-<center>
-To predict interactions/binding affinities of a single target against a library of compounds.
-</center>
+                # <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:
                 with gr.Column() as screen_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."
+                                "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."
                             )
@@ -883,11 +883,11 @@ To predict interactions/binding affinities of a single target against a library
                             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',
+                        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', visible=True)
-                        target_query_btn = gr.Button(value='Query the sequence', variant='primary',
+                        target_paste_markdown = gr.Button(value='OR Paste Your Sequence Below', visible=True)
+                        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)
@@ -905,27 +905,28 @@ To predict interactions/binding affinities of a single target against a library
                             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)."
+                                "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 Input Protein Family (Optional)', interactive=True)
+                                label='Step 2. Select Target Family (Optional)', interactive=True)
                             # with gr.Column(scale=1, min_width=24):
 
                     with gr.Row():
                         with gr.Column():
-                            target_family_detect_btn = gr.Button(value='Auto-detect', variant='primary')
+                            target_family_detect_btn = gr.Button(value='OR Let Us Auto-Detect for You', variant='primary')
 
                     with gr.Row():
                         with gr.Column():
                             HelpTip(
-                                "Select a preset compound library (e.g., DrugBank)."
+                                "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."
+                                "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 3. Select or Upload a Compound Library',
+                            drug_library = gr.Dropdown(label='Step 3. Select a Preset Compound Library',
                                                        choices=list(DRUG_LIBRARY_MAP.keys()))
                             with gr.Row():
                                 gr.File(label='Example SDF compound library',
@@ -933,42 +934,41 @@ To predict interactions/binding affinities of a single target against a library
                                 gr.File(label='Example CSV compound library',
                                         value='data/examples/compound_library.csv', interactive=False)
                             drug_library_upload_btn = gr.UploadButton(
-                                label='Upload a custom library', variant='primary')
+                                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():
                             HelpTip(
                                 "Interaction prediction provides you binding probability score between the target of "
-                                "interest and each compound in the library,"
+                                "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 4. Select a Prediction Task',
+                                                           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 target was trained."
+                                "on the selected task, family, and whether the target was trained. "
                                 "Please refer to documentation for detailed benchamrk results."
                                 )
                             drug_screen_preset = gr.Dropdown(list(PRESET_MAP.keys()),
                                                              label='Step 5. Select a Preset Model')
-                            screen_preset_recommend_btn = gr.Button(value='Recommend a model', variant='primary')
+                            screen_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(
-                                label='Step 6. Email (Optional)',
-                                info="If an email is provided, a notification email will be sent to you when your job "
-                                     "is completed."
+                                label='Step 6. Input Your Email Address (Optional)',
+                                info="Your email address will be used to notify you about the completion of your job."
                             )
 
                     with gr.Row(visible=True):
                         with gr.Column():
                             # drug_screen_clr_btn = gr.ClearButton(size='lg')
-                            drug_screen_btn = gr.Button(value='SCREEN', variant='primary', 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')
@@ -980,19 +980,19 @@ To predict interactions/binding affinities of a single target against a library
 
         with gr.TabItem(label='Target protein identification', id=1):
             gr.Markdown('''
-# <center>Target Protein Identification</center>
-
-<center>
-To predict interactions/binding affinities of a single compound against a library of protein targets.
-</center>
+                # <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."
+                                "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."
                             )
@@ -1002,7 +1002,7 @@ To predict interactions/binding affinities of a single compound against a librar
                                 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='Upload', variant='primary',
+                            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)
@@ -1011,23 +1011,25 @@ To predict interactions/binding affinities of a single compound against a librar
                     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."
+                                "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 Protein Family ('
+                                                                        label='Step 2. Select Target Family ('
                                                                               'Optional)')
 
                     with gr.Row():
                         with gr.Column():
                             HelpTip(
-                                "Select a preset target library (e.g., ChEMBL33_human_proteins)."
+                                "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."
+                                "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 or Upload a Target Library',
+                            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',
@@ -1035,7 +1037,7 @@ To predict interactions/binding affinities of a single compound against a librar
                                 gr.File(label='Example CSV target library',
                                         value='data/examples/target_library.csv', interactive=False)
                             target_library_upload_btn = gr.UploadButton(
-                                label='Upload a custom library', variant='primary')
+                                label='OR Upload Your Own Library', variant='primary')
                             target_library_upload = gr.File(label='Custom target library file', visible=False)
 
                     with gr.Row():
@@ -1047,7 +1049,7 @@ To predict interactions/binding affinities of a single compound against a librar
                                 "IC50."
                             )
                             target_identify_task = gr.Dropdown(list(TASK_MAP.keys()),
-                                                               label='Step 4. Select a Prediction Task',
+                                                               label='Step 4. Select the Prediction Task You Want to Conduct',
                                                                value='Compound-protein interaction')
 
                     with gr.Row():
@@ -1057,21 +1059,21 @@ To predict interactions/binding affinities of a single compound against a librar
                                 "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='Recommend a model', variant='primary')
+                            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. Email (Optional)',
-                                info="If an email is provided, a notification email will be sent to you when your job "
-                                     "is completed."
+                                label='Step 6. Input Your Email Address (Optional)',
+                                info="Your email address will be used to notify you about the completion of your job."
                             )
 
                     with gr.Row(visible=True):
                         # target_identify_clr_btn = gr.ClearButton(size='lg')
-                        target_identify_btn = gr.Button(value='IDENTIFY', variant='primary', 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')
             identify_waiting = gr.Markdown(f"Your job is running... It might take a few minutes."
@@ -1081,7 +1083,7 @@ To predict interactions/binding affinities of a single compound against a librar
         with gr.TabItem(label='Interaction pair inference', id=2):
             gr.Markdown('''
 # <center>Interaction Pair Inference</center>
-<center>To predict interactions/binding affinities between any compound-protein pairs.</center>
+<center>To predict interactions or binding affinities between up to 10,000 paired compound-protein data.</center>
 ''')
             with gr.Blocks() as infer_block:
                 with gr.Column() as infer_page:
@@ -1089,22 +1091,29 @@ To predict interactions/binding affinities of a single compound against a librar
                         "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."
+                        "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 interaction pair dataset',
+                        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 interaction pair dataset')
+                        value='Upload a CSV file containing paired compound-protein data')
                     with gr.Column() as pair_upload:
-                        gr.File(label="Example custom dataset",
+                        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",
+                                                         visible=True)
                         with gr.Column():
                             infer_data_for_predict = gr.File(
-                                label='Upload a custom dataset', file_count="single", type='filepath', visible=True)
-                    with gr.Column() as pair_generate:
+                                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)
@@ -1116,48 +1125,56 @@ To predict interactions/binding affinities of a single compound against a librar
                             gr.File(label='Example CSV target library',
                                     value='data/examples/target_library.csv', interactive=False)
                         with gr.Row():
-                            infer_drug = gr.File(label='SDF/CSV file containing multiple compounds',
+                            infer_library_prompt = gr.Button(value="Upload Your Own Libraries Below",
+                                                             visible=False)
+                        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='FASTA/CSV file containing multiple targets',
+                            infer_target = gr.File(label='Upload FASTA/CSV File Containing Multiple Targets',
                                                    file_count="single", type='filepath')
 
                     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."
+                                "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 Protein Family (Optional)')
+                                                                   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."
+                                "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 a Prediction Task',
+                                                          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 benchamrk results."
+                                    "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='Recommend a model', variant='primary')
+                            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. Email (Optional)',
-                            info="If an email is provided, a notification email will be sent to you when your job is completed."
+                            label='Step 5. Input Your Email Address (Optional)',
+                            info="Your email address will be used to notify you about the completion of your job."
                         )
 
                     with gr.Row(visible=True):
                         # pair_infer_clr_btn = gr.ClearButton(size='lg')
-                        pair_infer_btn = gr.Button(value='INFER', variant='primary', size='lg')
+                        pair_infer_btn = gr.Button(value='SUBMIT THE INFERENCE JOB', variant='primary', size='lg')
 
             infer_waiting = gr.Markdown(f"Your job is running... It might take a few minutes."
                                         f"When it's done, you will be redirected to the report page. "
@@ -1400,7 +1417,7 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
         elif task == 'DTA':
             train = pd.read_csv('data/benchmarks/all_families_reduced_dta_train.csv')
             score = 'CI'
-        if smiles not in train['X1']:
+        if not np.isin(smiles, train['X1']):
             scenario = "Unseen drug"
         else:
             scenario = "Seen drug"
@@ -1429,21 +1446,26 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
                     pair_generate: gr.Column(visible=True),
                     infer_data_for_predict: None,
                     infer_drug: None,
-                    infer_target: None
+                    infer_target: None,
+                    infer_csv_prompt: gr.Button(visible=False),
+                    infer_library_prompt: gr.Button(visible=True),
                 }
         match upload_type:
-            case "Upload a CSV interaction pair dataset":
+            case "Upload a CSV file containing paired compound-protein data":
                 return {
                     pair_upload: gr.Column(visible=True),
                     pair_generate: gr.Column(visible=False),
                     infer_data_for_predict: None,
                     infer_drug: None,
-                    infer_target: None
+                    infer_target: None,
+                    infer_csv_prompt: gr.Button(visible=True),
+                    infer_library_prompt: gr.Button(visible=False),
                 }
 
 
     infer_type.select(fn=infer_type_change, inputs=infer_type,
-                      outputs=[pair_upload, pair_generate, infer_data_for_predict, infer_drug, infer_target])
+                      outputs=[pair_upload, pair_generate, infer_data_for_predict, infer_drug, infer_target,
+                               infer_csv_prompt, infer_library_prompt])
 
 
     def drug_screen_validate(fasta, library, library_upload, state, progress=gr.Progress(track_tqdm=True)):