streamlit app

app.py

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+import streamlit as st
+from fastai.text.all import *
+
+@st.cache_resource
+def load_model():
+    learn = load_learner('text_classifier_learner.pth')
+    return learn
+
+learn = load_model()
+
+# Streamlit app
+st.title('ULMFiT Swahili News Article Classifier')
+
+# Text input
+user_text = st.text_area('Enter text for classification')
+
+if st.button('Classify'):
+    if user_text:
+        pred_class, pred_idx, outputs = learn.predict(user_text)
+        st.write(f"Predicted Class: {pred_class}")
+    else:
+        st.write("Please enter text to classify.")
+
+if __name__ == '__main__':
+    st.run()