adding documentation

app.py

@@ -1,21 +1,27 @@
+# imports
 import gradio as gr
 from transformers import pipeline
 
+# load model via inference pipeline
 classifier_pipe = pipeline("text-classification", model="LennardZuendorf/legalis-BERT", top_k=None)
 
 
+# function to predict the case winner via the model
 def predict_fun(text):
     predictions = classifier_pipe(text)
     return {p["label"]: p["score"] for p in predictions[0]}
 
 
+# gradio interface as a block setup
 with gr.Blocks(title='Legalis') as interface:
+    # top row
     with gr.Row():
         gr.Markdown(
             """
             # Legalis BERT Demo
             Start typing below to see the output.
             """)
+    # middle row with input text, predict button and output label
     with gr.Row():
         with gr.Column():
             input_text = gr.Textbox(label="Case Facts")
@@ -26,6 +32,8 @@ with gr.Blocks(title='Legalis') as interface:
     with gr.Row():
         interpretation = gr.components.Interpretation(input_text, visible=False)
 
+    # link predict button to predict function
     predict.click(predict_fun, input_text, label)
 
+# launch command
 interface.launch()