Create app file

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# -------------------------------
+# MODEL CONFIGURATION
+# -------------------------------
+
+model_name = "ELHACHYMI/bert-ner"
+
+example_sent = "Bill Gates founded Microsoft in the United States."
+
+example_list = [
+    "Elon Musk is the CEO of SpaceX and Tesla.",
+    "Cristiano Ronaldo plays for Al Nassr in Saudi Arabia.",
+    "Google was founded in California by Larry Page and Sergey Brin.",
+    "Paris is the capital of France and home to the Eiffel Tower.",
+    "Apple Inc. was created by Steve Jobs in Cupertino.",
+]
+
+# Model info for usage display
+model_info = {
+    model_name: {
+        "link": f"https://huggingface.co/{model_name}",
+        "usage": f"""from transformers import pipeline
+
+ner = pipeline("ner", model="{model_name}", aggregation_strategy="simple")
+text = "{example_sent}"
+print(ner(text))""",
+    }
+}
+
+# Load model
+ner_pipeline = pipeline("ner", model=model_name, aggregation_strategy="simple")
+
+
+# -------------------------------
+# DISPLAY MODEL INFO
+# -------------------------------
+
+def display_model_info(model_name):
+    info = model_info[model_name]
+    usage_code = info["usage"]
+    link_button = f'[ Open model page: {model_name}]({info["link"]})'
+    return usage_code, link_button
+
+
+# -------------------------------
+# NER ANALYSIS FUNCTION
+# -------------------------------
+
+def analyze_text(text):
+    ner_results = ner_pipeline(text)
+
+    highlighted_text = []
+    last_idx = 0
+
+    for entity in ner_results:
+        start = entity["start"]
+        end = entity["end"]
+        label = entity["entity_group"]
+
+        # Text before entity
+        if start > last_idx:
+            highlighted_text.append((text[last_idx:start], None))
+
+        # Entity text
+        highlighted_text.append((text[start:end], label))
+        last_idx = end
+
+    # Remaining text
+    if last_idx < len(text):
+        highlighted_text.append((text[last_idx:], None))
+
+    return highlighted_text
+
+
+# -------------------------------
+# GRADIO INTERFACE
+# -------------------------------
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Named Entity Recognition Demo using BERT")
+    gr.Markdown("### Model: **ELHACHYMI/bert-ner**")
+
+    # Input text
+    text_input = gr.Textbox(
+        label="Enter Text",
+        lines=5,
+        value=example_sent,
+        placeholder="Type a sentence to analyze...",
+    )
+
+    # List of examples buttons
+    with gr.Row():
+        for ex in example_list:
+            gr.Button(ex).click(lambda x=ex: x, outputs=text_input)
+
+    analyze_button = gr.Button("Run NER Analysis")
+
+    # Highlighted output
+    output = gr.HighlightedText(label="NER Result", combine_adjacent=True)
+
+    # Code + link output
+    code_output = gr.Code(label="How to use this model in Python", visible=True)
+    link_output = gr.Markdown()
+
+    # Initialize code + link
+    demo.load(
+        fn=display_model_info,
+        inputs=[gr.Textbox(value=model_name, visible=False)],
+        outputs=[code_output, link_output],
+    )
+
+    # Run analysis
+    analyze_button.click(
+        analyze_text, inputs=[text_input], outputs=output
+    )
+
+demo.launch()