Spaces:
Sleeping
Sleeping
File size: 5,380 Bytes
6a53dd4 e5dc65d 6a53dd4 e5dc65d 6a53dd4 e5dc65d 6a53dd4 d7ac3f6 e5dc65d 6a53dd4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 |
from src.modeling_t5 import T5ForSequenceClassification
import selfies as sf
import pandas as pd
from transformers import AutoTokenizer, pipeline
from chemistry_adapters.amino_acids import AminoAcidAdapter
from tqdm import tqdm
import gradio as gr
class xBitterT5_predictor:
def __init__(
self,
xBitterT5_640_ckpt="cbbl-skku-org/xBitterT5-640",
xBitterT5_720_ckpt="cbbl-skku-org/xBitterT5-720",
device="cpu",
):
self.xBitterT5_640_ckpt = xBitterT5_640_ckpt
self.xBitterT5_720_ckpt = xBitterT5_720_ckpt
self.device = device
self.tokenizer = AutoTokenizer.from_pretrained(xBitterT5_640_ckpt)
self.xBitterT5_640 = self.load_model(xBitterT5_640_ckpt)
self.xBitterT5_720 = self.load_model(xBitterT5_720_ckpt)
self.classifier_640 = pipeline(
"text-classification",
model=self.xBitterT5_640,
tokenizer=self.tokenizer,
device=self.device,
)
self.classifier_720 = pipeline(
"text-classification",
model=self.xBitterT5_720,
tokenizer=self.tokenizer,
device=self.device,
)
def load_model(self, ckpt):
model = T5ForSequenceClassification.from_pretrained(ckpt)
model.eval()
model.to(self.device)
return model
def convert_sequence_to_smiles(self, sequence):
adapter = AminoAcidAdapter()
return adapter.convert_amino_acid_sequence_to_smiles(sequence)
def conver_smiles_to_selfies(self, smiles):
return sf.encoder(smiles)
def predict(
self,
input_dict,
model_type="xBitterT5-720",
batch_size=4,
):
assert model_type in ["xBitterT5-640", "xBitterT5-720"]
df = pd.DataFrame(
{"id": list(input_dict.keys()), "sequence": list(input_dict.values())}
)
df["smiles"] = df.apply(
lambda row: self.convert_sequence_to_smiles(row["sequence"]),
axis=1,
)
df["selfies"] = df.apply(
lambda row: self.conver_smiles_to_selfies(row["smiles"]),
axis=1,
)
df["sequence"] = df.apply(
lambda row: "<bop>"
+ "".join("<p>" + aa for aa in row["sequence"])
+ "<eop>",
axis=1,
)
df["selfies"] = df.apply(lambda row: "<bom>" + row["selfies"] + "<eom>", axis=1)
df["text"] = df["sequence"] + df["selfies"]
text_inputs = df["text"].tolist()
if model_type == "xBitterT5-640":
classifier = self.classifier_640
else:
classifier = self.classifier_720
result = []
for i in tqdm(range(0, len(text_inputs), batch_size)):
batch = text_inputs[i : i + batch_size]
result.extend(classifier(batch))
y_pred, y_prob = [], []
for pred in result:
if pred["label"] == "bitter":
y_prob.append(pred["score"])
y_pred.append(1)
else:
y_prob.append(1 - pred["score"])
y_pred.append(0)
return {i: [y_prob[j], y_pred[j]] for j, i in enumerate(df["id"].tolist())}
predictor = xBitterT5_predictor()
def process_fasta(fasta_text):
"""
Processes the input FASTA format text into a dictionary {id: sequence}.
"""
fasta_dict = {}
current_id = None
current_sequence = []
for line in fasta_text.strip().split("\n"):
line = line.strip()
if line.startswith(">"): # Header line
if current_id:
fasta_dict[current_id] = "".join(current_sequence)
current_id = line[1:] # Remove '>'
current_sequence = []
else:
current_sequence.append(line)
# Add the last sequence
if current_id:
fasta_dict[current_id] = "".join(current_sequence)
return fasta_dict
# Create a Gradio interface
def predict(choice, fasta_text):
"""
Wrapper for Gradio to process the FASTA text.
"""
fasta_dict = process_fasta(fasta_text)
result = predictor.predict(fasta_dict, model_type=choice)
result_df = pd.DataFrame(
{
"id": list(result.keys()),
"probability": [i[0] for i in result.values()],
"class": ["bitter" if i[1] == 1 else "non-bitter" for i in result.values()],
}
)
# text_result = f"ID\tClass\tProbability\n"
# for key, value in result.items():
# text_result += (
# f"{key}\t{'bitter' if value[1] == 1 else 'non-bitter'}\t{value[0]}\n"
# )
return result_df
interface = gr.Interface(
fn=predict,
inputs=[
gr.Dropdown(
choices=["xBitterT5-640", "xBitterT5-720"],
label="Select xBitterT5 variant",
value="xBitterT5-720",
),
gr.Textbox(
label="Enter peptide sequences in FASTA format",
lines=10,
placeholder=">id1\nVAPFPE\n>id2\nRRPP\n>id3\nGH\nid4\nGVDTK",
),
],
# outputs=gr.Textbox(label="Predictions", type="text"),
outputs=gr.Dataframe(
headers=["ID", "Class", "Probability"],
),
title="xBitterT5",
description=("Prediction of bitter peptides using xBitterT5."),
flagging_mode="never",
)
# Launch the Gradio app
interface.launch()
|