added proper support for name overide in Molnet tasks and a reasonable texts for the new tasks

mammal_demo/__init__.py

@@ -17,48 +17,71 @@ def tasks_and_models():
 # Note that the tasks need access to the models, as the model to use depends on the state of the widget
 # we pass the all_models dict and update it when we actualy have the models.
 
-    ppi_task = all_tasks.register_task(PpiTask(model_dict=all_models))
-    tdi_task = all_tasks.register_task(DtiTask(model_dict=all_models))
-    ps_task = all_tasks.register_task(PsTask(model_dict=all_models))
-    tcr_task = all_tasks.register_task(TcrTask(model_dict=all_models))
-    bbbp_task = all_tasks.register_task(MolnetTask(model_dict=all_models,task_name="BBBP"))
-    toxicity_task = all_tasks.register_task(MolnetTask(model_dict=all_models,task_name="TOXICITY"))
-    fda_appr_task = all_tasks.register_task(MolnetTask(model_dict=all_models,task_name="FDA_APPR"))
+    ppi_task_name = all_tasks.register_task(PpiTask(model_dict=all_models))
+    tdi_task_name = all_tasks.register_task(DtiTask(model_dict=all_models))
+    ps_task_name = all_tasks.register_task(PsTask(model_dict=all_models))
+    tcr_task_name = all_tasks.register_task(TcrTask(model_dict=all_models))
+    bbbp_task =  MolnetTask(model_dict=all_models,task_name="BBBP", name= "Blood-Brain Barrier Penetration")
+    bbbp_task.markup_text = """
+# Mammal based small molecule blood-brain barrier penetration demonstration
+
+Given a drug (in SMILES), estimate the likelihood that it will penetrate the Blood-Brain Barrier.
+"""
+    bbbp_task_name = all_tasks.register_task(bbbp_task)
+    
+    toxicity_task = MolnetTask(model_dict=all_models,task_name="TOXICITY", name= "Drug Toxicity Trials Failer")
+    toxicity_task.markup_text = """
+# Mammal based small molecule toxicity trials failer estimation demonstration
+
+Given a drug (in SMILES), estimate the likelihood that it will fail in clinical toxicity trials.
+"""
+    toxicity_task_name = all_tasks.register_task(toxicity_task)
+    
+    
+    
+    fda_appr_task=MolnetTask(model_dict=all_models,task_name="FDA_APPR", name="drug FDA approval demonstration")
+    fda_appr_task.markup_text = """
+# Mammal based small molecule drug FDA approval demonstration
+
+Given a drug (in SMILES), estimate the likelihood that it will be approved by the FDA.
+    """
+    fda_appr_task_name = all_tasks.register_task(fda_appr_task)
+   
 
 # create the model holders. hold the model and the tokenizer, lazy download
 # note that the list of relevent tasks needs to be stated.
     all_models.register_model(
     model_path="ibm/biomed.omics.bl.sm.ma-ted-458m.dti_bindingdb_pkd",
-    task_list=[tdi_task],
+    task_list=[tdi_task_name],
 )
     all_models.register_model(
     model_path="ibm/biomed.omics.bl.sm.ma-ted-458m.dti_bindingdb_pkd_peer",
-    task_list=[tdi_task],
+    task_list=[tdi_task_name],
 )
 
     all_models.register_model(
     model_path="ibm/biomed.omics.bl.sm.ma-ted-458m.tcr_epitope_bind",
-    task_list=[tcr_task],
+    task_list=[tcr_task_name],
 )
     all_models.register_model(
     model_path="ibm/biomed.omics.bl.sm.ma-ted-458m.protein_solubility",
-    task_list=[ps_task],
+    task_list=[ps_task_name],
 )
     all_models.register_model(
     model_path="ibm/biomed.omics.bl.sm.ma-ted-458m",
-    task_list=[ppi_task],
+    task_list=[ppi_task_name],
 )
     all_models.register_model(
     "ibm/biomed.omics.bl.sm.ma-ted-458m.moleculenet_clintox_tox",
-    task_list=[toxicity_task]
+    task_list=[toxicity_task_name]
 )
     all_models.register_model(
     "ibm/biomed.omics.bl.sm.ma-ted-458m.moleculenet_clintox_fda",
-    task_list=[fda_appr_task]
+    task_list=[fda_appr_task_name]
 )
     all_models.register_model(
     "ibm/biomed.omics.bl.sm.ma-ted-458m.moleculenet_bbbp",
-    task_list=[bbbp_task],
+    task_list=[bbbp_task_name],
 )
     
     return all_tasks,all_models

mammal_demo/molnet_task.py

@@ -7,17 +7,18 @@ from mammal_demo.demo_framework import MammalObjectBroker, MammalTask
 
 
 class MolnetTask(MammalTask):
-    def __init__(self, model_dict, task_name="BBBP"):
-        super().__init__(name=f"Molnet: {task_name}", model_dict=model_dict)
+    def __init__(self, model_dict, task_name="BBBP", name=None):
+        if name is None:
+            name=f"Molnet: {task_name}"
+        super().__init__(name=name, model_dict=model_dict)
         self.description = f"MOLNET {task_name}"
         self.examples = {
             "drug_seq": "CC(=O)NCCC1=CNc2c1cc(OC)cc2",
         }
         self.task_name=task_name
         self.markup_text = """
-# Mammal based Drug-Target binding affinity demonstration
+# Mammal demonstration
 
-Given a protein sequence and a drug (in SMILES), estimate the binding affinity.
 """
 
     def  crate_sample_dict(self, sample_inputs: dict, model_holder: MammalObjectBroker) -> dict: