Update app.py

app.py

@@ -60,7 +60,7 @@ SESSION.mount('https://', ADAPTER)
 
 UNIPROT_ENDPOINT = 'https://rest.uniprot.org/uniprotkb/{query}'
 
-CUSTOM_DATASET_MAX_LEN = 10000
+CUSTOM_DATASET_MAX_LEN = 10_000
 
 CSS = """
 .help-tip {
@@ -403,12 +403,12 @@ def validate_columns(df, mandatory_cols):
 
 
 def process_target_fasta(sequence):
-    lines = sequence.strip().split("\n")
-    if lines[0].startswith(">"):
-        lines = lines[1:]
-    return ''.join(lines).split(">")[0]
-    # record = SeqIO.parse(io.StringIO(sequence), "fasta")[0]
-    # return str(record.seq)
+    # lines = sequence.strip().split("\n")
+    # if lines[0].startswith(">"):
+    #     lines = lines[1:]
+    # return ''.join(lines).split(">")[0]
+    record = list(SeqIO.parse(io.StringIO(sequence), "fasta"))[0]
+    return str(record.seq)
 
 
 def send_email(receiver, msg):
@@ -732,24 +732,24 @@ def drug_library_from_sdf(sdf_path):
 
 def process_target_library_upload(library_upload):
     if library_upload.endswith('.csv'):
-        identify_df = pd.read_csv(library_upload)
+        df = pd.read_csv(library_upload)
     elif library_upload.endswith('.fasta'):
-        identify_df = target_library_from_fasta(library_upload)
+        df = target_library_from_fasta(library_upload)
     else:
         raise gr.Error('Currently only CSV and FASTA files are supported as target libraries.')
-    validate_columns(identify_df, ['X2'])
-    return library_upload
+    validate_columns(df, ['X2'])
+    return df
 
 
 def process_drug_library_upload(library_upload):
     if library_upload.endswith('.csv'):
-        screen_df = pd.read_csv(library_upload)
+        df = pd.read_csv(library_upload)
     elif library_upload.endswith('.sdf'):
-        screen_df = drug_library_from_sdf(library_upload)
+        df = drug_library_from_sdf(library_upload)
     else:
-        raise gr.Error('Currently only CSV and SDF files are supported as compound libraries.')
-    validate_columns(screen_df, ['X1'])
-    return screen_df
+        raise gr.Error('Currently only CSV and SDF files are supported as drug libraries.')
+    validate_columns(df, ['X1'])
+    return df
 
 
 def target_library_from_fasta(fasta_path):
@@ -863,14 +863,15 @@ To predict interactions/binding affinities of a single target against a library
                         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."
+                                "Alternatively, upload a CSV file with a column named X1 containing compound SMILES, "
+                                "or use an SDF file."
                             )
                             drug_library = gr.Dropdown(label='Step 3. Select or Upload a Compound Library',
                                                        choices=list(DRUG_LIBRARY_MAP.keys()))
                             with gr.Row():
-                                gr.File(label='Example SDF Compound Library',
+                                gr.File(label='Example SDF compound library',
                                         value='data/examples/compound_library.sdf', interactive=False)
-                                gr.File(label='Example CSV Compound 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')
@@ -958,9 +959,9 @@ To predict interactions/binding affinities of a single compound against a librar
                             target_library = gr.Dropdown(label='Step 3. Select or Upload a Target Library',
                                                          choices=list(TARGET_LIBRARY_MAP.keys()))
                             with gr.Row():
-                                gr.File(label='Example FASTA Target Library',
+                                gr.File(label='Example FASTA target library',
                                         value='data/examples/target_library.fasta', interactive=False)
-                                gr.File(label='Example CSV Target Library',
+                                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')
@@ -1006,69 +1007,45 @@ To predict interactions/binding affinities of a single compound against a librar
 ''')
             with gr.Blocks() as infer_block:
                 with gr.Column() as infer_page:
-                    infer_type = gr.Dropdown(choices=['Upload a CSV interaction pair dataset',
-                                                      'Upload a compound library and a target library'],
-                                             label='Step 1. Select Pair Input Type and Input',
-                                             value='Upload a CSV interaction pair dataset')
+                    infer_type = gr.Dropdown(choices=['Upload a compound library and a target library',
+                                                      'Upload a CSV interaction pair dataset'],
+                                             value='Upload a compound library and a target library')
                     with gr.Column() as pair_upload:
-                        with gr.Row():
-                            gr.File(label="Example custom dataset",
-                                    value="data/examples/interaction_pair_inference.csv",
-                                    interactive=False)
-                        with gr.Row():
+                        gr.File(label="Example custom dataset",
+                                value="data/examples/interaction_pair_inference.csv",
+                                interactive=False)
+                        with gr.Column():
                             infer_data_for_predict = gr.File(
-                                label='Upload a Custom Dataset', file_count="single", type='filepath', visible=True)
+                                label='Upload a custom dataset', file_count="single", type='filepath', visible=True)
                     with gr.Column() as pair_generate:
                         with gr.Row():
-                            gr.File(label='Example SDF Compound Library',
+                            gr.File(label='Example SDF compound library',
                                     value='data/examples/compound_library.sdf', interactive=False)
-                            gr.File(label='Example FASTA Target Library',
+                            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',
+                            gr.File(label='Example CSV compound library',
                                     value='data/examples/compound_library.csv', interactive=False)
-                            gr.File(label='Example CSV Target Library',
+                            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_drug = gr.File(label='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='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."
-                            )
-                            pair_infer_target_family = gr.Dropdown(choices=list(TARGET_FAMILY_MAP.keys()),
-                                                                   value='General',
-                                                                   label='Step 2. Select Target Protein 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 a Prediction Task',
-                                                          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 random splitting validation."
-                                    "Please refer to documentation for detailed benchamrk 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')
-
+                    with gr.Row(visible=True):
+                        pair_infer_task = gr.Dropdown(list(TASK_MAP.keys()), label='Task')
+                        pair_infer_preset = gr.Dropdown(list(PRESET_MAP.keys()), label='Preset')
+                        pair_infer_target_family = gr.Dropdown(choices=['General'],
+                                                               label='Target family',
+                                                               value='General')
 
-                    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."
-                        )
+                    # with gr.Row():
+                    #     pair_infer_email = gr.Textbox(
+                    #         label='Email (optional)',
+                    #         info="Your email will be used to send you notifications when your job finishes."
+                    #     )
 
                     with gr.Row(visible=True):
                         # pair_infer_clr_btn = gr.ClearButton(size='lg')
@@ -1110,10 +1087,10 @@ To predict interactions/binding affinities of a single compound against a librar
 
                 with gr.Row():
                     with gr.Column():
-                        csv_generate = gr.Button(value='Generate raw data (CSV)', interactive=True)
+                        csv_generate = gr.Button(value='Generate raw data (CSV)', interactive=True, variant='primary')
                         csv_download_file = gr.File(label='Download raw data (CSV)', visible=False)
                     with gr.Column():
-                        html_generate = gr.Button(value='Generate report (HTML)', interactive=True)
+                        html_generate = gr.Button(value='Generate report (HTML)', interactive=True, variant='primary')
                         html_download_file = gr.File(label='Download report (HTML)', visible=False)
 
 
@@ -1362,7 +1339,6 @@ QALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES
                     screen_df = pd.read_csv(Path('data/drug_libraries', DRUG_LIBRARY_MAP[library]))
                 else:
                     screen_df = process_drug_library_upload(library_upload)
-                    print(screen_df.shape)
                     if len(screen_df) >= CUSTOM_DATASET_MAX_LEN:
                         raise gr.Error(f'The uploaded compound library has more records '
                                        f'than the allowed maximum (CUSTOM_DATASET_MAX_LEN).')
@@ -1600,5 +1576,3 @@ if __name__ == "__main__":
     demo.launch(
         show_api=False,
     )
-
-#%%