Create app.py

app.py

@@ -0,0 +1,27 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load the text classification pipeline with the custom model
+pipe = pipeline("text-classification", model="1231czx/llama3_it_ultra_list_and_bold500")
+
+def classify_text(text):
+    # Perform text classification using the pipeline
+    result = pipe(text)
+    return result[0]['label'], result[0]['score']
+
+# Gradio interface setup
+title = "Custom Model Text Classification"
+description = "Provide a block of text, and the custom LLaMA-based model will classify it."
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=classify_text,
+    inputs=gr.Textbox(label="Input Text"),
+    outputs=[gr.Textbox(label="Predicted Label"), gr.Number(label="Confidence Score")],
+    title=title,
+    description=description,
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch()