Upload folder using huggingface_hub

app.py

@@ -1,25 +1,47 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load the NER models
-# Load the NER models
+# Model names (keeping it programmatic)
+model_names = [
+    "dslim/bert-base-NER",
+    "dslim/bert-base-NER-uncased",
+    "dslim/bert-large-NER",
+    "dslim/distilbert-NER",
+]
+
+example_sent = (
+    "Nim Chimpsky was a chimpanzee at Columbia University named after Noam Chomsky."
+)
+
+# Programmatically build the model info dict
+model_info = {
+    model_name: {
+        "link": f"https://huggingface.co/{model_name}",
+        "usage": f"""from transformers import pipeline
+ner = pipeline("ner", model="{model_name}", grouped_entities=True)
+result = ner("{example_sent}")
+print(result)""",
+    }
+    for model_name in model_names
+}
+
+# Load models into a dictionary programmatically for the analyze function
 models = {
-    "dslim/bert-base-NER": pipeline(
-        "ner", model="dslim/bert-base-NER", grouped_entities=True
-    ),
-    "dslim/bert-base-NER-uncased": pipeline(
-        "ner", model="dslim/bert-base-NER-uncased", grouped_entities=True
-    ),
-    "dslim/bert-large-NER": pipeline(
-        "ner", model="dslim/bert-large-NER", grouped_entities=True
-    ),
-    "dslim/distilbert-NER": pipeline(
-        "ner", model="dslim/distilbert-NER", grouped_entities=True
-    ),
+    model_name: pipeline("ner", model=model_name, grouped_entities=True)
+    for model_name in model_names
 }
 
 
-def process(text, model_name):
+# Function to display model info (link and usage code)
+def display_model_info(model_name):
+    info = model_info[model_name]
+    usage_code = info["usage"]
+    link_button = f'[Open model page for {model_name} ]({info["link"]})'
+    return usage_code, link_button
+
+
+# Function to run NER on input text
+def analyze_text(text, model_name):
     ner = models[model_name]
     ner_results = ner(text)
     highlighted_text = []
@@ -41,20 +63,44 @@ def process(text, model_name):
 
 
 with gr.Blocks() as demo:
-    gr.Markdown("# Named Entity Recognition with BERT Models")
-    with gr.Row():
-        model_selector = gr.Dropdown(
-            choices=list(models.keys()),
-            value=list(models.keys())[0],
-            label="Select Model",
-        )
+    gr.Markdown("# Named Entity Recognition (NER) with BERT Models")
+
+    # Dropdown for model selection
+    model_selector = gr.Dropdown(
+        choices=list(model_info.keys()),
+        value=list(model_info.keys())[0],
+        label="Select Model",
+    )
+
+    # Textbox for input text
     text_input = gr.Textbox(
         label="Enter Text",
         lines=5,
-        value="Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, therefore very close to the Manhattan Bridge.",
+        value=example_sent,
+    )
+    analyze_button = gr.Button("Run NER Model")
+    output = gr.HighlightedText(label="NER Result", combine_adjacent=True)
+
+    # Outputs: usage code, model page link, and analyze button
+    code_output = gr.Code(label="Use this model", visible=True)
+    link_output = gr.Markdown(
+        f"[Open model page for {model_selector} ]({model_selector})"
+    )
+    # Button for analyzing the input text
+    analyze_button.click(
+        analyze_text, inputs=[text_input, model_selector], outputs=output
+    )
+
+    # Trigger the code output and model link when model is changed
+    model_selector.change(
+        display_model_info, inputs=[model_selector], outputs=[code_output, link_output]
+    )
+
+    # Call the display_model_info function on load to set initial values
+    demo.load(
+        fn=display_model_info,
+        inputs=[model_selector],
+        outputs=[code_output, link_output],
     )
-    output = gr.HighlightedText(label="Named Entities")
-    analyze_button = gr.Button("Analyze")
-    analyze_button.click(process, inputs=[text_input, model_selector], outputs=output)
 
 demo.launch()