Upload example.py

example.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load tokenizer and model from Hugging Face model hub
+model_name = "dejanseo/Intent-XS"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+model.eval()  # Set the model to evaluation mode
+
+# Human-readable labels
+label_map = {
+    1: 'Commercial',
+    2: 'Non-Commercial',
+    3: 'Branded',
+    4: 'Non-Branded',
+    5: 'Informational',
+    6: 'Navigational',
+    7: 'Transactional',
+    8: 'Commercial Investigation',
+    9: 'Local',
+    10: 'Entertainment'
+}
+
+# Function to perform inference
+def get_predictions(text):
+    inputs = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+    probabilities = torch.sigmoid(logits).squeeze()
+    predictions = (probabilities > 0.5).int()
+    return probabilities.numpy(), predictions.numpy()
+
+# Streamlit user interface
+st.title('Multi-label Classification with Intent-XS')
+query = st.text_input("Enter your query:")
+
+if st.button('Submit'):
+    if query:
+        probabilities, predictions = get_predictions(query)
+        result = {label_map[i+1]: f"Probability: {prob:.2f}" for i, prob in enumerate(probabilities) if predictions[i] == 1}
+        if result:
+            st.write("Predicted Categories:")
+            for label, prob in result.items():
+                st.write(f"{label}: {prob}")
+        else:
+            st.write("No relevant categories predicted.")
+    else:
+        st.write("Please enter a query to get predictions.")