let's deploy to huggingface spaces

.ipynb_checkpoints/app-checkpoint.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

app.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c483e658-eca5-48e4-ae77-d59403453d12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|default_exp app"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9ffc7d71-f919-4a81-9ba0-46edbe0c1f99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "\n",
+    "from fastai.vision.all import *\n",
+    "import gradio as gr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "06a2e43c-5935-45c7-aee1-bbe8594aff01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "learn = load_learner('model.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b43a8038-20d4-41c1-b9c9-c05b35f2a491",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "categories = ('Cloudy','Rain','Shine Or Partly Cloudy','Sunrise') \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": 6,
+   "id": "49d4264f-ae56-4ac0-b0a4-a876385f5ee2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "('Rain', tensor(1), tensor([7.6823e-04, 9.9922e-01, 1.0592e-05, 4.4878e-08]))"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "learn.predict('rain.jpg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a8794806-a335-4331-978a-2d8ba3ba8bff",
+   "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": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#|export\n",
+    "#image = gr.inputs.Image(shape=(192,192))\n",
+    "#label = gr.outputs.Label()\n",
+    "examples = ['cloudy.jpg','rain.jpg','shine.jpg','sunrise.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": 9,
+   "id": "6f6d0837-0f14-4d58-8ce3-13b7e25376b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "cannot import name 'nbdev_export' from 'nbdev.export' (/home/shoaib/mambaforge/lib/python3.10/site-packages/nbdev/export.py)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnbdev\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexport\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m nbdev_export\n",
+      "\u001b[0;31mImportError\u001b[0m: cannot import name 'nbdev_export' from 'nbdev.export' (/home/shoaib/mambaforge/lib/python3.10/site-packages/nbdev/export.py)"
+     ]
+    }
+   ],
+   "source": [
+    "from nbdev.export import notebook2script"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9fa6c39c-a5e5-42e8-a4c3-122d6bdbeef1",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

app.py

@@ -1,7 +1,14 @@
+from fastai.vision.all import *
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+learn = load_learner('model.pkl')
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+categories = ('Cloudy','Rain','Shine Or Partly Cloudy','Sunrise') 
+def classify_image(img):
+        pred,idx,probs = learn.predict(img)
+        return dict(zip(categories,map(float,probs)))
+
+examples = ['cloudy.jpg','rain.jpg','shine.jpg','sunrise.jpg']
+
+intf = gr.Interface(fn=classify_image , inputs='image' , outputs = 'label' , examples=examples)
+intf.launch(inline=False)

flagged/log.csv

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+img,output,flag,username,timestamp
+"{""path"":""flagged/img/021e165a0faf70b4ed47/sunrise11.jpg"",""url"":""http://127.0.0.1:7860/file=/tmp/gradio/18ec15d2d98034992b07009ca7f865faefbea12d/sunrise11.jpg"",""size"":10013,""orig_name"":""sunrise11.jpg"",""mime_type"":""""}","{""label"":null,""confidences"":null}",,,2023-12-14 17:37:19.369506

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eab876af2c0c4295460df42ac3899defa86f6fb9ff6ca64d3279fed96cb094c2
+size 46983550

model.pkl:Zone.Identifier

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+[ZoneTransfer]
+ZoneId=3
+HostUrl=about:internet