Create app.py

app.py

@@ -0,0 +1,48 @@
+from transformers import pipeline
+import gradio as gr
+#This is a pipeline for text classification using the Arabic MARBERT model for news article classification from Hugging Face.
+Clasification = pipeline('text-classification', model='Ammar-alhaj-ali/arabic-MARBERT-news-article-classification')
+
+#This function will take and input then return the label and the score of that sentence.
+def classification_fun(news_article):
+  results = Clasification(news_article)
+  return results[0]['label'], results[0]['score']
+
+#CSS styling for the Gradio interface
+custom_css = """
+textarea, .gradio-output {
+  direction: rtl;
+  background-color: black;
+  color: white; 
+  border: 2px solid #800020;  
+  border-radius: 5px;
+  padding: 10px;  
+}
+
+label {
+  font-size: 18px;
+  font-weight: bold;
+  text-align: center;
+  background-color: #800020;  
+  color: white;
+  box-shadow: 2px 2px 5px rgba(0,0,0,0.2);
+  padding: 5px;  
+  display: block;
+  margin: 10px 0; 
+}
+
+.gradio-container {
+  background-color: black;
+  padding: 20px; 
+  box-sizing: border-box; 
+}
+"""
+
+my_model = gr.Interface(
+    fn=classification_fun,
+    inputs=gr.Textbox(label="News Articles", lines=10, placeholder="Enter your Article"),
+    outputs=[gr.Textbox(label="Label of the Article"), gr.Number(label="Confidence Score")],
+    css=custom_css
+)
+
+my_model.launch()