Update app.py

app.py

@@ -1,5 +1,6 @@
 import gradio as gr
 from transformers import pipeline
+from typing import Dict, List
 
 # Available models for zero-shot classification
 AVAILABLE_MODELS = [
@@ -10,13 +11,28 @@ AVAILABLE_MODELS = [
     "mjwong/mcontriever-xnli"
 ]
 
-def classify_text(model_name, text, labels):
+def classify_text(
+        model_name: str, 
+        text: str, 
+        labels: str
+    ) -> Dict[str, float]:
+    """
+    Classifies the input text into one of the provided labels using a zero-shot classification model.
+    
+    Args:
+        model_name: The name of the Hugging Face model to use.
+        text: The input text to classify.
+        labels: A comma-separated string of candidate labels.
+    
+    Returns:
+        Dict[str, float]: A dictionary mapping each label to its classification score.
+    """
     classifier = pipeline("zero-shot-classification", model=model_name)
     labels_list = [label.strip() for label in labels.split(",")]
     result = classifier(text, candidate_labels=labels_list)
     return {label: score for label, score in zip(result["labels"], result["scores"])}
 
-# Example Input
+# Example Input with Mutually Exclusive Labels from News Articles
 examples = [
     [
         "The government announced a new economic policy today aimed at reducing inflation and stabilizing the currency market.", 
@@ -70,21 +86,27 @@ footer {display:none !important}
 }
 """
 
+# Initialize Gradio interface
 with gr.Blocks(css=css) as iface:
     gr.Markdown("# Zero-Shot Text Classifier")
-    gr.Markdown("Select a model, enter text, and a set of labels to classify it using a zero-shot classification model.")
-    
+    gr.Markdown("Select a model, enter text, and a set of labels to classify the text using a zero-shot classification model.")
+
+    # Dropdown to select a model
     model_dropdown = gr.Dropdown(AVAILABLE_MODELS, label="Choose Model")
-    
+
+    # Input fields for text and labels
     with gr.Row():
         text_input = gr.Textbox(label="Enter Text", placeholder="Type or paste text here...")
         label_input = gr.Textbox(label="Enter Labels (comma-separated)", placeholder="e.g., sports, politics, technology")
-    
+
+    # Output display
     output_label = gr.Label(label="Classification Scores")
-    
+
+    # Classification button
     submit_button = gr.Button("Classify")
     submit_button.click(fn=classify_text, inputs=[model_dropdown, text_input, label_input], outputs=output_label)
-    
+
+    # Example input/output pairs
     gr.Examples(examples, inputs=[text_input, label_input])
 
 # Launch the app