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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 gradio_process_fasta(fasta_text):
"""
Wrapper for Gradio to process the FASTA text.
"""
fasta_dict = process_fasta(fasta_text)
result = predictor.predict(fasta_dict)
return result
interface = gr.Interface(
fn=gradio_process_fasta,
inputs=gr.Textbox(
label="Enter FASTA format text", lines=10, placeholder=">id1\nATGC\n>id2\nCGTA"
),
outputs=gr.JSON(label="Processed FASTA Dictionary with Probabilities and Classes"),
title="FASTA to Dictionary with Probabilities and Classes",
description=("Enter a FASTA-formatted text"),
)
# Launch the Gradio app
interface.launch()
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