Create app.py

app.py

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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.")