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import torch
from transformers import XLMRobertaTokenizer, XLMRobertaForSequenceClassification
import streamlit as st
model_path = "fine_tuned_xlm_roberta"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = XLMRobertaTokenizer.from_pretrained(model_path)
model = XLMRobertaForSequenceClassification.from_pretrained(model_path)
model.to(device)
model.eval()
def classify_text(text, max_length=128):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length", max_length=max_length)
inputs = {key: val.to(device) for key, val in inputs.items()}
with torch.no_grad():
outputs = model(**inputs)
probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
pred_label = torch.argmax(probabilities, dim=-1).item()
confidence = probabilities[0, pred_label].item()
return "Kyrgyz" if pred_label == 1 else "Non-Kyrgyz", confidence
st.title("Kyrgyz Language Classifier")
st.write("This tool identifies whether the given text is Kyrgyz or not.")
st.markdown("""
**Instructions:**
* Please enter a **sentence** for better accuracy.
* **Note:** The word "**Салам**" might be classified as Non-Kyrgyz. This is a known exception.
""")
user_input = st.text_area("Enter text to classify:", placeholder="Type your sentence here...")
if st.button("Classify"):
if user_input.strip():
label, confidence = classify_text(user_input)
st.write(f"Prediction: **{label}**")
st.write(f"Confidence: **{confidence:.2%}**")
else:
st.warning("Please enter some text for classification.") |