Update app.py

app.py

@@ -4,7 +4,14 @@ import gradio as gr
 from datasets import load_dataset
 
 # Load the BL dataset with streaming
-dataset_iter = iter(load_dataset("TheBritishLibrary/blbooks", split="train", streaming=True, trust_remote_code=True))
+dataset_iter = iter(
+    load_dataset(
+        "TheBritishLibrary/blbooks",
+        split="train",
+        streaming=True,
+        trust_remote_code=True
+    ).shuffle(buffer_size=10000, seed=42)  # Shuffling added here
+)
 
 # Load the model
 model = GLiNER.from_pretrained("max-long/textile_machines_3_oct", trust_remote_code=True)
@@ -48,25 +55,6 @@ with gr.Blocks(title="Textile Machinery NER Demo") as demo:
         """
     )
     
-    gr.Code(
-        '''
-from gliner import GLiNER
-model = GLiNER.from_pretrained("max-long/textile_machines_3_oct")
-text = "Your sample text here."
-labels = ["textile machinery"]
-entities = model.predict_entities(text, labels)
-for entity in entities:
-    print(entity["text"], "=>", entity["label"])
-        ''',
-        language="python",
-    )
-    gr.Code(
-        """
-Textile Machine 1 => textile machinery
-Textile Machine 2 => textile machinery
-        """
-    )
-    
     # Display a random example
     input_text = gr.Textbox(
         value=" ",