Update app.py

app.py

@@ -48,19 +48,6 @@ with gr.Blocks(title="Textile Machinery NER Demo") as demo:
         """
     )
     
-    with gr.Accordion("How to run this model locally", open=False):
-        gr.Markdown(
-            """
-            ## Installation
-            To use this model, you must install the GLiNER Python library:
-            ```
-            !pip install gliner
-            ```
-         
-            ## Usage
-            Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`.
-            """
-        )
         gr.Code(
             '''
 from gliner import GLiNER
@@ -82,7 +69,7 @@ Textile Machine 2 => textile machinery
     
     # Display a random example
     input_text = gr.Textbox(
-        value="Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.",
+        value=" ",
         label="Text input",
         placeholder="Enter your text here",
         lines=5
@@ -135,38 +122,6 @@ Textile Machine 2 => textile machinery
         inputs=[input_text, labels, threshold, nested_ner],
         outputs=[output_highlighted, output_entities]
     )
-    
-    # Define examples
-    examples = [
-        [
-            "However, both models lack other frequent DM symptoms including the fibre-type dependent atrophy, myotonia, cataract and male-infertility.",
-            "textile machinery",
-            0.3,
-            False,
-        ],
-        [
-            "Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.",
-            "textile machinery",
-            0.3,
-            False,
-        ],
-        [
-            "The Shore Line route of the CNS & M until 1955 served, from south to north, the Illinois communities of Chicago, Evanston, Wilmette...",
-            "textile machinery",
-            0.3,
-            False,
-        ],
-        # Add more examples as needed
-    ]
-    
-    gr.Examples(
-        examples=examples,
-        inputs=[input_text, labels, threshold, nested_ner],
-        outputs=[output_highlighted, output_entities],
-        fn=ner,
-        label="Examples",
-        cache_examples=True,
-    )
 
 demo.queue()
 demo.launch(debug=True)