final commit

app.py

@@ -1,23 +1,71 @@
 import streamlit as st
 from fastai.text.all import *
 
-@st.cache_resource
-def load_model():
-    with st.spinner('Model is being loaded...'):
-        learn = load_learner('models/text_classifier_model.pkl')
-    return learn
-
-st.title('ULMFiT Swahili News Article Classifier')
-
-st.markdown("""
-ULMFiT (Universal Language Model Fine-tuning) is an effective transfer learning method for NLP tasks.
-""")
-
-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"Input text belongs to: {pred_class}")
-    else:
-        st.write("Please enter text to classify.")
+class TextClassifierApp:
+    """
+    A Streamlit app for classifying Swahili news articles using ULMFiT.
+
+    Attributes:
+        learn (Learner): The FastAI learner object for the text classifier.
+
+    Methods:
+        load_model(): Loads the pre-trained model with a spinner indicating the loading process.
+        predict(text: str) -> str: Predicts the class of the given text.
+        run(): Runs the Streamlit app, providing the user interface for text classification.
+    """
+    def __init__(self):
+        """
+        Initializes the TextClassifierApp by loading the model.
+        """
+        self.learn = None
+        self.load_model()
+
+    @st.cache_resource
+    def load_model(self):
+        """
+        Loads the pre-trained model and shows a spinner during the loading process.
+
+        Returns:
+            None
+        """
+        with st.spinner('Model is being loaded...'):
+            self.learn = load_learner('models/text_classifier_model.pkl')
+
+    def predict(self, text):
+        """
+        Predicts the class of the given text.
+
+        Args:
+            text (str): The text to classify.
+
+        Returns:
+            str: The predicted class.
+        """
+        pred_class, pred_idx, outputs = self.learn.predict(text)
+        return pred_class
+
+    def run(self):
+        """
+        Runs the Streamlit app, providing the user interface for text classification.
+
+        Returns:
+            None
+        """
+        st.title('ULMFiT Swahili News Article Classifier')
+
+        st.markdown("""
+        ULMFiT (Universal Language Model Fine-tuning) is an effective transfer learning method for NLP tasks.
+        """)
+
+        user_text = st.text_area('Enter text for classification')
+
+        if st.button('Classify'):
+            if user_text:
+                pred_class = self.predict(user_text)
+                st.write(f"Input text belongs to: {pred_class}")
+            else:
+                st.write("Please enter text to classify.")
+
+if __name__ == '__main__':
+    app = TextClassifierApp()
+    app.run()