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app.py

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+import streamlit as st
+from joblib import load
+from sklearn.pipeline import Pipeline
+
+# Load the pre-trained model
+model: Pipeline = load('app/trained_intent_classifier.joblib')
+
+def classify_intent(text, model, threshold=0.7):
+    # Predict the probability distribution over the classes
+    probs = model.predict_proba([text])[0]
+    # Get the maximum probability and its corresponding class
+    confidence = max(probs)
+    intent = model.classes_[probs.argmax()]
+
+    # Check if the confidence meets the threshold
+    if confidence < threshold:
+        return "NLU fallback: Intent could not be confidently determined"
+    else:
+        return f"Intent: {intent}, Confidence: {confidence:.2f}"
+
+def main():
+    st.title("Intent Classification App")
+    st.write("""
+    This app uses a machine learning model to classify user intents based on the text they provide.
+    Simply enter some text below and click 'Classify' to see the predicted intent and confidence level.
+    """)
+
+    # Sidebar for settings
+    st.sidebar.title("Settings")
+    threshold = st.sidebar.slider("Confidence Threshold", 0.0, 1.0, 0.7, 0.01)
+    st.sidebar.write("Adjust the confidence threshold to classify intents.")
+
+    # User input in the main area
+    user_input = st.text_area("Enter your text here:", height=150)
+
+    if st.button("Classify"):
+        if user_input:
+            # Classify the intent
+            result = classify_intent(user_input, model, threshold=threshold)
+            st.success(f"Classified as: {result}")
+        else:
+            st.error("Please enter some text to classify.")
+
+if __name__ == "__main__":
+    main()

app/trained_intent_classifier.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:601050c7eec5b5672448b300dbc4e5bad71b6421e4ed6c15ad60906bf1a9cc13
+size 30421

requirements.txt

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+numpy
+pandas
+scikit-learn
+streamlit
+joblib