Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# 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 = {
+    model_name: pipeline("ner", model=model_name, grouped_entities=True)
+    for model_name in model_names
+}
+
+
+# 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 = []
+    last_idx = 0
+    for entity in ner_results:
+        start = entity["start"]
+        end = entity["end"]
+        label = entity["entity_group"]
+        # Add non-entity text
+        if start > last_idx:
+            highlighted_text.append((text[last_idx:start], None))
+        # Add entity text
+        highlighted_text.append((text[start:end], label))
+        last_idx = end
+    # Add any remaining text after the last entity
+    if last_idx < len(text):
+        highlighted_text.append((text[last_idx:], None))
+    return highlighted_text
+
+
+with gr.Blocks() as demo:
+    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=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],
+    )
+
+demo.launch()
+
+
+