run.ipynb

{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: sentiment_analysis\n", "### This sentiment analaysis demo takes in input text and returns its classification for either positive, negative or neutral using Gradio's Label output.\n", "        "]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio nltk"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import nltk\n", "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n", "\n", "nltk.download(\"vader_lexicon\")\n", "sid = SentimentIntensityAnalyzer()\n", "\n", "def sentiment_analysis(text):\n", "    scores = sid.polarity_scores(text)\n", "    del scores[\"compound\"]\n", "    return scores\n", "\n", "demo = gr.Interface(\n", "    fn=sentiment_analysis,\n", "    inputs=gr.Textbox(placeholder=\"Enter a positive or negative sentence here...\"),\n", "    outputs=\"label\",\n", "    examples=[[\"This is wonderful!\"]])\n", "\n", "demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}