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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import torch.nn.functional as F
# Load the tokenizer and model from HuggingFace Hub
@st.cache_resource(show_spinner=False)
def load_model():
model_name = "pavlyhalim/BERT_ALL_README"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model.eval()
return tokenizer, model
tokenizer, model = load_model()
st.title("Readability Score Predictor based on BERT")
st.write("""
Enter a sentence, and the model will predict its readability score (from 1 to 6).
""")
user_input = st.text_area("Enter your sentence here:", height=100)
if st.button("Predict Readability Score"):
if user_input.strip() == "":
st.warning("Please enter a sentence.")
else:
with st.spinner('Predicting...'):
inputs = tokenizer(
user_input,
return_tensors="pt",
padding=True,
truncation=True,
max_length=128
)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probabilities = F.softmax(logits, dim=1)
predicted_class = torch.argmax(probabilities, dim=1).item()
predicted_probability = probabilities[0][predicted_class].item()
predicted_label = predicted_class + 1
st.success(f"Predicted Readability Score: **{predicted_label}**")
st.write(f"Confidence: **{predicted_probability * 100:.2f}%**")
st.write("### Class Probabilities:")
for i, prob in enumerate(probabilities[0]):
label = i + 1
st.write(f"Score {label}: {prob.item() * 100:.2f}%") |