| 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.") |