Create app.py

app.py

@@ -0,0 +1,26 @@
+# app.py
+
+from transformers import pipeline
+import gradio as gr
+
+# Load the text classification pipeline with the custom model
+pipe = pipeline("text-classification", model="palakagl/bert_TextClassification")
+
+# Define function to classify input text
+def classify_text(text):
+    result = pipe(text)
+    # Format nicely for display
+    return {res["label"]: round(res["score"], 4) for res in result}
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=classify_text,
+    inputs=gr.Textbox(lines=3, placeholder="Enter text to classify..."),
+    outputs=gr.Label(num_top_classes=3),
+    title="BERT Text Classifier",
+    description="Enter text to classify using the BERT model from palakagl/bert_TextClassification."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()