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README.md

@@ -5,7 +5,7 @@ emoji: 🔥
 colorFrom: indigo
 colorTo: indigo
 sdk: gradio
-sdk_version: 5.49.1
+sdk_version: 6.0.0
 app_file: run.py
 pinned: false
 hf_oauth: true

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: ner_pipeline"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch transformers"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["from transformers import pipeline\n", "\n", "import gradio as gr\n", "\n", "ner_pipeline = pipeline(\"ner\")  # type: ignore\n", "\n", "examples = [\n", "    \"Does Chicago have any stores and does Joe live here?\",\n", "]\n", "\n", "def ner(text):\n", "    output = ner_pipeline(text)\n", "    return {\"text\": text, \"entities\": output}\n", "\n", "demo = gr.Interface(ner,\n", "             gr.Textbox(placeholder=\"Enter sentence here...\"),\n", "             gr.HighlightedText(),\n", "             examples=examples)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: ner_pipeline"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio torch transformers"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["from transformers import pipeline\n", "\n", "import gradio as gr\n", "\n", "ner_pipeline = pipeline(\"ner\")  # type: ignore\n", "\n", "examples = [\n", "    \"Does Chicago have any stores and does Joe live here?\",\n", "]\n", "\n", "def ner(text):\n", "    output = ner_pipeline(text)\n", "    return {\"text\": text, \"entities\": output}\n", "\n", "demo = gr.Interface(ner,\n", "             gr.Textbox(placeholder=\"Enter sentence here...\"),\n", "             gr.HighlightedText(),\n", "             examples=examples,\n", "             api_name=\"predict\")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -15,7 +15,8 @@ def ner(text):
 demo = gr.Interface(ner,
              gr.Textbox(placeholder="Enter sentence here..."),
              gr.HighlightedText(),
-             examples=examples)
+             examples=examples,
+             api_name="predict")
 
 if __name__ == "__main__":
     demo.launch()