updated to work on huggingface

app.py

@@ -1,7 +1,22 @@
+__all__ = ['learn','classify_image','categories','image','label','examples','myinterface']
+
+
 import gradio as gr
+from fastai.vision.all import *
+
+learn = load_learner('elf_export.pkl')
+categories = ('dragonborne','dwarf','elf','halfling','human','tiefling')
+
+def classify_image (img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories,map(float,probs)))
+
+image = gr.inputs.Image (shape = (192,192))
+label = gr.outputs.Label ()
+examples = ['tiefling.jpg']
+
+myinterface = gr.Interface(fn = classify_image,inputs = image,outputs = label, examples = examples)
+myinterface.launch (inline=False)
+
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()

elf.ipynb

@@ -2,19 +2,37 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'gradio'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mgradio\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mgr\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mgreet\u001b[39m(name):\n\u001b[1;32m      4\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mHello \u001b[39m\u001b[39m\"\u001b[39m \u001b[39m+\u001b[39m name \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m!!\u001b[39m\u001b[39m\"\u001b[39m\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'gradio'"
+     "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/html": [
+       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -27,6 +45,43 @@
     "iface.launch()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('elf',\n",
+       " TensorBase(2),\n",
+       " TensorBase([2.4853e-03, 4.2622e-03, 9.9193e-01, 1.0128e-03, 2.4441e-04,\n",
+       "             6.5845e-05]))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "from fastai.vision.all import *\n",
+    "\n",
+    "learn = load_learner('elf_export.pkl')\n",
+    "learn.predict ('tiefling.jpg')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -34,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +99,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },
@@ -63,7 +118,7 @@
   "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+    "hash": "25613e3f86799f8fc5e212a950c5421386eb1419de4db49b5cf413f9a619b293"
    }
   }
  },