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import pandas as pd
import numpy as np
import torch.nn.functional as F
import torch
import os
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from transformers import BertTokenizerFast as BertTokenizer, AutoModelForSequenceClassification, AutoTokenizer,AutoModel,BertModel, AdamW, get_linear_schedule_with_warmup
import pytorch_lightning as pl
from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
from pytorch_lightning.loggers import TensorBoardLogger
import streamlit as st
import torchmetrics
pwd = os.path.dirname(__file__)
MODEL_PATH = os.path.join(pwd,"data.pt")
print(MODEL_PATH)
BERT_MODEL_NAME = 'albert-base-v1'
tokenizer = AutoTokenizer.from_pretrained(BERT_MODEL_NAME)
class MeshNetwork(pl.LightningModule):
def __init__(self):
super().__init__()
self.bert = AutoModelForSequenceClassification.from_pretrained(BERT_MODEL_NAME, num_labels=13,return_dict=True)
self.criterion = F.cross_entropy
def forward(self, input_ids, attention_mask):
output = self.bert(input_ids=input_ids, attention_mask=attention_mask)
return output.logits
def training_step(self, batch, batch_idx):
input_ids = batch["input_ids"]
attention_mask = batch["attention_mask"]
y = batch['labels']
y_hat = self.forward(input_ids, attention_mask)
loss = self.criterion(y_hat, y)
# Calculate acc
predictions = F.softmax(y_hat, dim=1).argmax(dim=1)
acc = torchmetrics.functional.accuracy(predictions, y)
self.log("train_acc", acc, on_step=False,prog_bar=True, on_epoch=True, logger=True)
self.log("train_loss", loss, prog_bar=True, on_epoch=True, logger=True)
return {"loss": loss, "predictions": y_hat, "labels": y}
def validation_step(self, batch, batch_idx):
input_ids = batch["input_ids"]
attention_mask = batch["attention_mask"]
y = batch["labels"]
y_hat = self.forward(input_ids, attention_mask)
loss = self.criterion(y_hat, y)
predictions = F.softmax(y_hat, dim=1).argmax(dim=1)
acc = torchmetrics.functional.accuracy(predictions, y)
self.log("val_acc", acc, prog_bar=True, on_step = False,on_epoch=True, logger=True)
self.log("val_loss", loss, prog_bar=True, on_epoch = True, logger=True)
def test_step(self, batch, batch_idx):
input_ids = batch["input_ids"]
attention_mask = batch["attention_mask"]
y = batch["labels"]
y_hat = self.forward(input_ids, attention_mask)
loss = self.criterion(y_hat, y)
predictions = F.softmax(y_hat, dim=1).argmax(dim=1)
acc = torchmetrics.functional.accuracy(predictions, y)
self.log("test_acc", acc, prog_bar=True, on_step=False,on_epoch=True, logger=True)
self.log("test_loss", loss, prog_bar=True, on_epoch = True, logger=True)
def configure_optimizers(self):
optimizer = torch.optim.Adam(params = self.parameters())
return optimizer
st.title("MeSH Classify")
model = MeshNetwork()
with st.spinner("Loading model..."):
model.load_state_dict(torch.load(MODEL_PATH))
model.eval()
print(model)
st.success("Model loaded.")
user_input = st.text_input("Enter text to be classified.")
st.write("Check MeSH categories: [link](https://www.ncbi.nlm.nih.gov/mesh/1000048)")
st.markdown("***")
if st.button("Classify Text"):
if user_input:
encoding = tokenizer.encode_plus(
user_input,
add_special_tokens=True,
return_token_type_ids=False,
padding="max_length",
truncation=True,
return_attention_mask=True,
return_tensors='pt',
)
input_ids=encoding["input_ids"].flatten()
attention_mask=encoding["attention_mask"].flatten()
y_hat = model(input_ids=input_ids.reshape(-1, 512),attention_mask = attention_mask.reshape(-1, 512))
prob = F.softmax(y_hat, dim=1)
probs = prob.detach().numpy()
st.table(probs)
predictions = prob.argmax(dim=1)
st.write(predictions.detach().numpy())
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