Add application file

app.py

@@ -0,0 +1,54 @@
+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}%")