Update app.py

app.py

@@ -38,32 +38,14 @@ def ner(
 with gr.Blocks(title="GLiNER-medium-v2.1") as demo:
     gr.Markdown(
         """
-        # GLiNER-medium-v2.1, zero-shot NER
+        # GLiNER bio finetuned model
 
-        GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios. This model has the commercially permissive Apache 2.0 license.
+        Devarsh Patel's assignment submission for Full Stack Developer at Healthy Vignettes. 
 
-        ## Links
-
-        * Model: https://huggingface.co/urchade/gliner_medium-v2.1
-        * All GLiNER models: https://huggingface.co/models?library=gliner
-        * Paper: https://arxiv.org/abs/2311.08526
-        * Repository: https://github.com/urchade/GLiNER
+        Email me at: [email protected]
         """
     )
-    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`.
-            """
-        )
-
+    
     input_text = gr.Textbox(
         value=examples[0][0], label="Text input", placeholder="Enter your text here"
     )