Update app.py

app.py

@@ -1,22 +1,39 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load the model
+# Load model from Hugging Face Hub
 classifier = pipeline("text-classification", model="sandbox338/hatespeech")
 
-# Define function to return only the label
+# Map model labels to readable labels
+label_map = {
+    "LABEL_0": "Non-hate speech",
+    "LABEL_1": "Political hate speech",
+    "LABEL_2": "Offensive language"
+}
+
+# Classification function
 def classify_text(text):
     result = classifier(text)[0]
-    return result['label'] 
+    label = result['label']
+    return label_map.get(label, "Unknown")
+
+# Example inputs for testing
+examples = [
+    ["Hii ni ujumbe wa kawaida bila matusi."],
+    ["Wanasiasa hawa ni wabaya na lazima waondoke!"],
+    ["Unasema upuuzi na wewe ni mjinga kabisa!"]
+]
 
-# Gradio interface
+# Gradio Interface
 interface = gr.Interface(
     fn=classify_text,
-    inputs=gr.Textbox(lines=4, placeholder="Enter Swahili text here..."),
+    inputs=gr.Textbox(lines=4, placeholder="Andika maandishi ya Kiswahili hapa..."),
     outputs="text",
     title="Swahili Hate Speech Classifier",
-    description="Classifies text as one of: Non-hate speech, Political hate speech, or Offensive language."
+    description="Classifies Swahili text as one of the following:\n- Non-hate speech\n- Political hate speech\n- Offensive language",
+    examples=examples
 )
 
 if __name__ == "__main__":
     interface.launch()
+