Update app.py

app.py

@@ -1,47 +1,33 @@
-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
-
+import requests
 
 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.
+*   Please enter a **sentence** or a few sentences 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...")
 
+
+api_url = "https://4190-212-112-100-194.ngrok-free.app//classify"
+
 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%}**")
+        try:
+            response = requests.post(api_url, json={'text': user_input})
+            response.raise_for_status()  # Raise an exception for bad status codes
+
+            result = response.json()
+            if 'error' in result:
+                st.error(f"Error: {result['error']}")
+            else:
+                st.write(f"Prediction: **{result['label']}**")
+                st.write(f"Confidence: **{result['confidence']:.2%}**")
+        except requests.exceptions.RequestException as e:
+            st.error(f"Connection error: {e}")
     else:
         st.warning("Please enter some text for classification.")