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import streamlit as st
import torch
from torch import nn
from transformers import BertModel, AutoTokenizer, AutoModel, pipeline
from time import time
import matplotlib.pyplot as plt
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device = 'cpu'
from PIL import Image
# dict for decoding / enclding labels
labels = {'cs.NE': 0, 'cs.CL': 1, 'cs.AI': 2, 'stat.ML': 3, 'cs.CV': 4, 'cs.LG': 5}
labels_decoder = {'cs.NE': 'Neural and Evolutionary Computing', 'cs.CL': 'Computation and Language', 'cs.AI': 'Artificial Intelligence',
'stat.ML': 'Machine Learning (stat)', 'cs.CV': 'Computer Vision', 'cs.LG': 'Machine Learning'}
model_name = 'bert-base-uncased'
tokenizer = AutoTokenizer.from_pretrained(model_name)
class BertClassifier(nn.Module):
def __init__(self, n_classes, dropout=0.5, model_name='bert-base-uncased'):
super(BertClassifier, self).__init__()
self.bert = BertModel.from_pretrained(model_name)
self.dropout = nn.Dropout(dropout)
self.linear = nn.Linear(768, n_classes)
self.relu = nn.ReLU()
def forward(self, input_id, mask):
_, pooled_output = self.bert(input_ids=input_id, attention_mask=mask,return_dict=False)
dropout_output = self.dropout(pooled_output)
linear_output = self.linear(dropout_output)
final_layer = self.relu(linear_output)
return final_layer
@st.cache(suppress_st_warning=True)
def build_model():
model = BertClassifier(n_classes=len(labels))
st.markdown("Model created")
model.load_state_dict(torch.load('model_weights_1.pt', map_location=torch.device('cpu')))
model.eval()
st.markdown("Model weights loaded")
return model
def inference(txt):
# infers classes for text topic based on loaded trained model
t2 = tokenizer(txt.lower().replace('\n', ''),
padding='max_length', max_length = 512, truncation=True,
return_tensors="pt")
inp2 = t2['input_ids'].to(device)
mask2 = t2['attention_mask'].unsqueeze(0).to(device)
out = model(inp2, mask2)
out = out.cpu().detach().numpy().reshape(-1)
out = out/out.sum() * 100
res = [(l, o) for l, o in zip (list(labels.keys()), out.tolist())]
return res
def infer_and_display_result(txt):
start_time = time()
res = inference(txt, mode=None)
res.sort(key = lambda x : - x[1])
st.markdown("###Inference results:")
for lbl, score in res:
if score >=1:
st.write(f"[ {lbl:<7}] {labels_decoder[lbl]:<35} {score:.1f}%")
res_plot = [] # storage for plot data
total=0
for r in res:
if total < 95:
res_plot.append(r)
total += r[1]
else:
break
res.sort(key = lambda x : x[1])
fig, ax = plt.subplots(figsize=(10, len(res_plot)))
for r in res_plot :
ax.barh(r[0], r[1])
st.pyplot(fig)
st.markdown(f"cycle time = {time() - start_time:.2f} s.")
# ======================================
st.title('Big-data cloud application for scientific article topic inference using in-memory computing and stuff.')
image = Image.open('dilbert_big_data.jpg')
st.image(image)
st.write('test application for ML-2 class, YSDA-2022' )
# st.markdown("<img width=200px src='https://i.pinimg.com/736x/11/33/19/113319f0ffe91f4bb0f468914b9916da.jpg'>", unsafe_allow_html=True)
# st.markdown("###Predict topic by abstract.")
text = st.text_area("ENTER ARTICLE TITLE OR ABSTRACT HERE")
action = st.button('click here to infer topic')
action2 = st.button('to uppercase')
if action:
model = build_model()
infer_and_display_result(text)
if action2:
st.write(text.upper())
action3 = st.button('to lowercase')
if action3:
st.write(text.lower())
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