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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

requirements.txt

@@ -1,3 +1,3 @@
-gradio-client @ git+https://github.com/gradio-app/gradio@e05eb8df38a4ca20993e94ca4e209cf8110bb677#subdirectory=client/python
-https://gradio-pypi-previews.s3.amazonaws.com/e05eb8df38a4ca20993e94ca4e209cf8110bb677/gradio-5.49.1-py3-none-any.whl
+gradio-client @ git+https://github.com/gradio-app/gradio@d007e6cf617baba5c62e49ec2b7ce278aa863a79#subdirectory=client/python
+https://gradio-pypi-previews.s3.amazonaws.com/d007e6cf617baba5c62e49ec2b7ce278aa863a79/gradio-6.0.0-py3-none-any.whl
 spacy

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: text_analysis\n", "### This simple demo takes advantage of Gradio's HighlightedText, JSON and HTML outputs to create a clear NER segmentation.\n", "        "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio spacy"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "os.system('python -m spacy download en_core_web_sm')\n", "import spacy  # type: ignore\n", "from spacy import displacy  # type: ignore\n", "\n", "nlp = spacy.load(\"en_core_web_sm\")\n", "\n", "def text_analysis(text):\n", "    doc = nlp(text)\n", "    html = displacy.render(doc, style=\"dep\", page=True)\n", "    html = (\n", "        \"<div style='max-width:100%; max-height:360px; overflow:auto'>\"\n", "        + html\n", "        + \"</div>\"\n", "    )\n", "    pos_count = {\n", "        \"char_count\": len(text),\n", "        \"token_count\": 0,\n", "    }\n", "    pos_tokens = []\n", "\n", "    for token in doc:\n", "        pos_tokens.extend([(token.text, token.pos_), (\" \", None)])\n", "\n", "    return pos_tokens, pos_count, html\n", "\n", "demo = gr.Interface(\n", "    text_analysis,\n", "    gr.Textbox(placeholder=\"Enter sentence here...\"),\n", "    [\"highlight\", \"json\", \"html\"],\n", "    examples=[\n", "        [\"What a beautiful morning for a walk!\"],\n", "        [\"It was the best of times, it was the worst of times.\"],\n", "    ],\n", ")\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: text_analysis\n", "### This simple demo takes advantage of Gradio's HighlightedText, JSON and HTML outputs to create a clear NER segmentation.\n", "        "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio spacy"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import os\n", "os.system('python -m spacy download en_core_web_sm')\n", "import spacy  # type: ignore\n", "from spacy import displacy  # type: ignore\n", "\n", "nlp = spacy.load(\"en_core_web_sm\")\n", "\n", "def text_analysis(text):\n", "    doc = nlp(text)\n", "    html = displacy.render(doc, style=\"dep\", page=True)\n", "    html = (\n", "        \"<div style='max-width:100%; max-height:360px; overflow:auto'>\"\n", "        + html\n", "        + \"</div>\"\n", "    )\n", "    pos_count = {\n", "        \"char_count\": len(text),\n", "        \"token_count\": 0,\n", "    }\n", "    pos_tokens = []\n", "\n", "    for token in doc:\n", "        pos_tokens.extend([(token.text, token.pos_), (\" \", None)])\n", "\n", "    return pos_tokens, pos_count, html\n", "\n", "demo = gr.Interface(\n", "    text_analysis,\n", "    gr.Textbox(placeholder=\"Enter sentence here...\"),\n", "    [\"highlight\", \"json\", \"html\"],\n", "    examples=[\n", "        [\"What a beautiful morning for a walk!\"],\n", "        [\"It was the best of times, it was the worst of times.\"],\n", "    ],\n", "    api_name=\"predict\",\n", ")\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -33,6 +33,7 @@ demo = gr.Interface(
         ["What a beautiful morning for a walk!"],
         ["It was the best of times, it was the worst of times."],
     ],
+    api_name="predict",
 )
 
 demo.launch()