{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "3962a4bb", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/raghu/mambaforge/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], "source": [ "#/export\n", "!pip install -Uqq gradio \n", "\n", "from fastai.vision.all import *\n", "import gradio as gr\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "9052b46c", "metadata": {}, "outputs": [], "source": [ "#/export\n", "def is_cat(x): return x[0].isupper()\n", "learn = load_learner('model.pkl')" ] }, { "cell_type": "code", "execution_count": 5, "id": "5ffdd5bd", "metadata": {}, "outputs": [], "source": [ "#/export\n", "\n", "categories = ('Dog','Cat')\n", "\n", "def classify_image(img):\n", " pred,idx,probs = learn.predict(img)\n", " return dict(zip(categories, map(float,probs)))" ] }, { "cell_type": "code", "execution_count": 10, "id": "94824e4c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running on local URL: http://127.0.0.1:7860\n", "\n", "To create a public link, set `share=True` in `launch()`.\n" ] }, { "data": { "text/plain": [] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#/export\n", "\n", "image = gr.inputs.Image(shape=(192,192))\n", "label = gr.outputs.Label()\n", "examples = ['dog.jpg','cat.jpeg','dunno.jpg']\n", "\n", "intf = gr.Interface(fn=classify_image, inputs = image, outputs = label, examples = examples)\n", "intf.launch(inline=False)" ] }, { "cell_type": "code", "execution_count": 13, "id": "98939b64", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Export successful\n" ] } ], "source": [ "import nbdev\n", "nbdev.export.nb_export('app.ipynb', './')\n", "print('Export successful')\n" ] }, { "cell_type": "code", "execution_count": null, "id": "24ddb16e", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "0e103a72", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.8" } }, "nbformat": 4, "nbformat_minor": 5 }