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Running
Running
matanninio
commited on
Commit
·
cec3465
1
Parent(s):
32cc43e
query -> promps
Browse files
app.py
CHANGED
@@ -23,14 +23,14 @@ protein_calmodulin = "MADQLTEEQIAEFKEAFSLFDKDGDGTITTKELGTVMRSLGQNPTEAELQDMISELDQ
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protein_calcineurin = "MSSKLLLAGLDIERVLAEKNFYKEWDTWIIEAMNVGDEEVDRIKEFKEDEIFEEAKTLGTAEMQEYKKQKLEEAIEGAFDIFDKDGNGYISAAELRHVMTNLGEKLTDEEVDEMIRQMWDQNGDWDRIKELKFGEIKKLSAKDTRGTIFIKVFENLGTGVDSEYEDVSKYMLKHQ"
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-
def
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# Formatting prompt to match pre-training syntax
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return f"<@TOKENIZER-TYPE=AA><BINDING_AFFINITY_CLASS><SENTINEL_ID_0><MOLECULAR_ENTITY><MOLECULAR_ENTITY_GENERAL_PROTEIN><SEQUENCE_NATURAL_START>{prot1}<SEQUENCE_NATURAL_END><MOLECULAR_ENTITY><MOLECULAR_ENTITY_GENERAL_PROTEIN><SEQUENCE_NATURAL_START>{prot2}<SEQUENCE_NATURAL_END><EOS>"
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def
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# Create and load sample
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sample_dict = dict()
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sample_dict[ENCODER_INPUTS_STR] =
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# Tokenize
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sample_dict=tokenizer_op(
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@@ -58,9 +58,9 @@ def run_query(query):
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return generated_output,score
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def
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res=
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return res
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def create_application():
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@@ -92,16 +92,16 @@ Given two protein sequences, estimate if the proteins interact or not.
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value=protein_calcineurin,
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)
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with gr.Row():
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run_mammal = gr.Button("Run Mammal
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with gr.Row():
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-
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with gr.Row():
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decoded = gr.Textbox(label="Mammal output")
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run_mammal.click(
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fn=
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inputs=[prot1,prot2],
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outputs=[
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)
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with gr.Row():
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gr.Markdown("```<SENTINEL_ID_0>``` contains the binding affinity class, which is ```<1>``` for interating and ```<0>``` for non-interating")
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protein_calcineurin = "MSSKLLLAGLDIERVLAEKNFYKEWDTWIIEAMNVGDEEVDRIKEFKEDEIFEEAKTLGTAEMQEYKKQKLEEAIEGAFDIFDKDGNGYISAAELRHVMTNLGEKLTDEEVDEMIRQMWDQNGDWDRIKELKFGEIKKLSAKDTRGTIFIKVFENLGTGVDSEYEDVSKYMLKHQ"
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+
def format_prompt(prot1,prot2):
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# Formatting prompt to match pre-training syntax
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return f"<@TOKENIZER-TYPE=AA><BINDING_AFFINITY_CLASS><SENTINEL_ID_0><MOLECULAR_ENTITY><MOLECULAR_ENTITY_GENERAL_PROTEIN><SEQUENCE_NATURAL_START>{prot1}<SEQUENCE_NATURAL_END><MOLECULAR_ENTITY><MOLECULAR_ENTITY_GENERAL_PROTEIN><SEQUENCE_NATURAL_START>{prot2}<SEQUENCE_NATURAL_END><EOS>"
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def run_prompt(prompt):
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# Create and load sample
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sample_dict = dict()
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sample_dict[ENCODER_INPUTS_STR] = prompt
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# Tokenize
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sample_dict=tokenizer_op(
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return generated_output,score
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def create_and_run_prompt(prot1, prot2):
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prompt = format_prompt(prot1, prot2)
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res=prompt, *run_prompt(prompt=prompt)
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return res
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def create_application():
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value=protein_calcineurin,
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)
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with gr.Row():
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run_mammal = gr.Button("Run Mammal prompt for Protein-Protein Interaction",variant='primary')
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with gr.Row():
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prompt_box = gr.Textbox(label="Mammal prompt",lines=5)
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with gr.Row():
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decoded = gr.Textbox(label="Mammal output")
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run_mammal.click(
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fn=create_and_run_prompt,
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inputs=[prot1,prot2],
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outputs=[prompt_box,decoded,gr.Number(label='PPI score')]
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)
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with gr.Row():
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gr.Markdown("```<SENTINEL_ID_0>``` contains the binding affinity class, which is ```<1>``` for interating and ```<0>``` for non-interating")
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