matanninio commited on
Commit
fbc2291
·
1 Parent(s): 06f1eeb

moved creation and collection of tasks and models into memmal_demo

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Files changed (2) hide show
  1. app.py +2 -54
  2. mammal_demo/__init__.py +58 -0
app.py CHANGED
@@ -1,14 +1,6 @@
1
  import gradio as gr
2
 
3
- from mammal_demo.demo_framework import (
4
- ModelRegistry,
5
- TaskRegistry,
6
- )
7
- from mammal_demo.dti_task import DtiTask
8
- from mammal_demo.ppi_task import PpiTask
9
- from mammal_demo.ps_task import PsTask
10
- from mammal_demo.tcr_task import TcrTask
11
-
12
  MAIN_MARKDOWN_TEXT = """
13
 
14
  The **[ibm/biomed.omics.bl.sm.ma-ted-458m](https://huggingface.co/models?sort=trending&search=ibm%2Fbiomed.omics.bl)** model family is a biomedical foundation model and its finetuned variants trained on over 2 billion biological samples across multiple modalities, including proteins, small molecules, and single-cell gene data.
@@ -20,51 +12,7 @@ The syntax allows for dynamic combinations of tokens and scalars, enabling class
20
  This page demonstraits a variety of drug discovery and biomedical tasks for the model family. Select the task to access the specific demos.
21
  """
22
 
23
-
24
- all_tasks = TaskRegistry()
25
- all_models = ModelRegistry()
26
-
27
- # first create the required tasks
28
- # Note that the tasks need access to the models, as the model to use depends on the state of the widget
29
- # we pass the all_models dict and update it when we actualy have the models.
30
-
31
- ppi_task = all_tasks.register_task(PpiTask(model_dict=all_models))
32
- tdi_task = all_tasks.register_task(DtiTask(model_dict=all_models))
33
- ps_task = all_tasks.register_task(PsTask(model_dict=all_models))
34
- tcr_task = all_tasks.register_task(TcrTask(model_dict=all_models))
35
-
36
- # create the model holders. hold the model and the tokenizer, lazy download
37
- # note that the list of relevent tasks needs to be stated.
38
- all_models.register_model(
39
- model_path="ibm/biomed.omics.bl.sm.ma-ted-458m.dti_bindingdb_pkd",
40
- task_list=[tdi_task],
41
- )
42
- all_models.register_model(
43
- model_path="ibm/biomed.omics.bl.sm.ma-ted-458m.dti_bindingdb_pkd_peer",
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- task_list=[tdi_task],
45
- )
46
-
47
- all_models.register_model(
48
- model_path="ibm/biomed.omics.bl.sm.ma-ted-458m.tcr_epitope_bind",
49
- task_list=[tcr_task],
50
- )
51
- all_models.register_model(
52
- model_path="ibm/biomed.omics.bl.sm.ma-ted-458m.protein_solubility",
53
- task_list=[ps_task],
54
- )
55
- all_models.register_model(
56
- model_path="ibm/biomed.omics.bl.sm.ma-ted-458m",
57
- task_list=[ppi_task],
58
- )
59
- all_models.register_model(
60
- "ibm/biomed.omics.bl.sm.ma-ted-458m.moleculenet_clintox_tox"
61
- )
62
- all_models.register_model(
63
- "ibm/biomed.omics.bl.sm.ma-ted-458m.moleculenet_clintox_fda"
64
- )
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- all_models.register_model(
66
- "ibm/biomed.omics.bl.sm.ma-ted-458m.moleculenet_bbbp"
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- )
68
 
69
 
70
  def create_application():
 
1
  import gradio as gr
2
 
3
+ import mammal_demo
 
 
 
 
 
 
 
 
4
  MAIN_MARKDOWN_TEXT = """
5
 
6
  The **[ibm/biomed.omics.bl.sm.ma-ted-458m](https://huggingface.co/models?sort=trending&search=ibm%2Fbiomed.omics.bl)** model family is a biomedical foundation model and its finetuned variants trained on over 2 billion biological samples across multiple modalities, including proteins, small molecules, and single-cell gene data.
 
12
  This page demonstraits a variety of drug discovery and biomedical tasks for the model family. Select the task to access the specific demos.
13
  """
14
 
15
+ all_tasks, all_models = mammal_demo.tasks_and_models()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
16
 
17
 
18
  def create_application():
mammal_demo/__init__.py CHANGED
@@ -0,0 +1,58 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ from mammal_demo.demo_framework import (
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+ ModelRegistry,
4
+ TaskRegistry,
5
+ )
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+ from mammal_demo.dti_task import DtiTask
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+ from mammal_demo.ppi_task import PpiTask
8
+ from mammal_demo.ps_task import PsTask
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+ from mammal_demo.tcr_task import TcrTask
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+
11
+
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+ def tasks_and_models():
13
+ all_tasks = TaskRegistry()
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+ all_models = ModelRegistry()
15
+
16
+ # first create the required tasks
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+ # Note that the tasks need access to the models, as the model to use depends on the state of the widget
18
+ # we pass the all_models dict and update it when we actualy have the models.
19
+
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+ ppi_task = all_tasks.register_task(PpiTask(model_dict=all_models))
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+ tdi_task = all_tasks.register_task(DtiTask(model_dict=all_models))
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+ ps_task = all_tasks.register_task(PsTask(model_dict=all_models))
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+ tcr_task = all_tasks.register_task(TcrTask(model_dict=all_models))
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+
25
+ # create the model holders. hold the model and the tokenizer, lazy download
26
+ # note that the list of relevent tasks needs to be stated.
27
+ all_models.register_model(
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+ model_path="ibm/biomed.omics.bl.sm.ma-ted-458m.dti_bindingdb_pkd",
29
+ task_list=[tdi_task],
30
+ )
31
+ all_models.register_model(
32
+ model_path="ibm/biomed.omics.bl.sm.ma-ted-458m.dti_bindingdb_pkd_peer",
33
+ task_list=[tdi_task],
34
+ )
35
+
36
+ all_models.register_model(
37
+ model_path="ibm/biomed.omics.bl.sm.ma-ted-458m.tcr_epitope_bind",
38
+ task_list=[tcr_task],
39
+ )
40
+ all_models.register_model(
41
+ model_path="ibm/biomed.omics.bl.sm.ma-ted-458m.protein_solubility",
42
+ task_list=[ps_task],
43
+ )
44
+ all_models.register_model(
45
+ model_path="ibm/biomed.omics.bl.sm.ma-ted-458m",
46
+ task_list=[ppi_task],
47
+ )
48
+ all_models.register_model(
49
+ "ibm/biomed.omics.bl.sm.ma-ted-458m.moleculenet_clintox_tox"
50
+ )
51
+ all_models.register_model(
52
+ "ibm/biomed.omics.bl.sm.ma-ted-458m.moleculenet_clintox_fda"
53
+ )
54
+ all_models.register_model(
55
+ "ibm/biomed.omics.bl.sm.ma-ted-458m.moleculenet_bbbp"
56
+ )
57
+
58
+ return all_tasks,all_models