cat dog fix

app.ipynb

@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "61d35bad",
+   "id": "63954aab",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15,7 +15,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "e6a6be00",
+   "id": "55f211ba",
    "metadata": {},
    "outputs": [
     {
@@ -36,7 +36,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "7686f783",
+   "id": "03b6a602",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "87ad84f9",
+   "id": "79eb2363",
    "metadata": {},
    "outputs": [
     {
@@ -67,7 +67,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "d97bef6b",
+   "id": "55a7aa21",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -77,7 +77,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "b12482d2",
+   "id": "22e702f7",
    "metadata": {},
    "outputs": [
     {
@@ -98,7 +98,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "78294788",
+   "id": "7af4b7e0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,7 +109,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "b041d6c6",
+   "id": "bf119685",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,7 +128,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "afa3cd9e",
+   "id": "bab7c7aa",
    "metadata": {},
    "outputs": [
     {
@@ -149,7 +149,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "5ac8c0b8",
+   "id": "4047a238",
    "metadata": {},
    "outputs": [
     {
@@ -170,7 +170,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "f12019b5",
+   "id": "2d2a09b4",
    "metadata": {},
    "outputs": [
     {
@@ -311,7 +311,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "baeac351",
+   "id": "661b3f97",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -321,17 +321,49 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "66a39511",
+   "id": "dacbe39b",
    "metadata": {},
    "outputs": [],
    "source": [
     "l=load_learner('model.pkl')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "5e606ed9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PILImage.create('dog.jpg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "569c3b37",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'img' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [30]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m l\u001b[38;5;241m.\u001b[39mpredict(\u001b[43mimg\u001b[49m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'img' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "l.predict(img)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "dc54ece1",
+   "id": "b107483d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -344,7 +376,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "ce096d4b",
+   "id": "e9178a7d",
    "metadata": {},
    "outputs": [
     {
@@ -421,14 +453,14 @@
    "source": [
     "image=gr.inputs.Image(shape=(192,192))\n",
     "label=gr.outputs.Label()\n",
-    "inf=gr.Interface(fn=classify,inputs=image,outputs=label)\n",
+    "inf=gr.Interface(fn=classify,inputs=image,outputs=label,)\n",
     "inf.launch(inline=False)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6a08f7e4",
+   "id": "8689a0e8",
    "metadata": {},
    "outputs": [],
    "source": []

app.py

@@ -8,7 +8,7 @@ def is_cat(x): return x[0].isupper()
     
 l=load_learner('model.pkl')
 
-cat=('cat','dog')
+cat=('dog','cat')
 def classify(img):
     s,i,p=l.predict(img)
     return dict(zip(cat,map(float,p)))