modified: app.ipynb

app.ipynb

@@ -15,46 +15,75 @@
    "id": "026f4508",
    "metadata": {},
    "source": [
-    "### Dogs V Cats"
+    "### Bear Classifier"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 54,
    "id": "ed9b1499",
    "metadata": {},
    "outputs": [],
    "source": [
     "#|export\n",
     "from fastai.vision.all import *\n",
-    "import gradio as gr\n"
+    "import gradio as gr\n",
+    "from fastbook import *\n",
+    "from fastai.vision.widgets import *\n",
+    "import gradio as gr\n",
+    "btn_upload = widgets.FileUpload()\n",
+    "out_pl = widgets.Output()\n",
+    "lbl_pred = widgets.Label()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 55,
    "id": "7b05e3e0",
    "metadata": {},
    "outputs": [],
    "source": [
     "#|export\n",
-    "def is_cat(x): return x[0].isupper() "
+    "# def on_data_change(change):\n",
+    "#     lbl_pred.value = ''\n",
+    "#     img = PILImage.create(btn_upload.data[-1])\n",
+    "#     out_pl.clear_output()\n",
+    "#     with out_pl: display(img.to_thumb(128,128))\n",
+    "#     pred,pred_idx,probs = learn_inf.predict(img)\n",
+    "#     lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'\n",
+    "\n",
+    "class DataLoaders(GetAttr):\n",
+    "    def __init__(self, *loaders): self.loaders = loaders\n",
+    "    def __getitem__(self, i): return self.loaders[i]\n",
+    "    train,valid = add_props(lambda i,self: self[i])\n",
+    "    \n",
+    "def is_cat(x): return x[0].isupper()\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 56,
    "id": "0407168f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'fastai.learner.Learner'>\n"
+     ]
+    }
+   ],
    "source": [
     "#|export\n",
-    "learn = load_learner('export.pkl')"
+    "learn = load_learner('export.pkl')\n",
+    "print(type(learn))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 57,
    "id": "d3b1540f",
    "metadata": {},
    "outputs": [
@@ -65,7 +94,7 @@
        "PILImage mode=RGB size=192x192"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -81,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 58,
    "id": "ac681618",
    "metadata": {},
    "outputs": [
@@ -126,8 +155,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: total: 156 ms\n",
-      "Wall time: 112 ms\n"
+      "CPU times: total: 172 ms\n",
+      "Wall time: 292 ms\n"
      ]
     },
     {
@@ -136,7 +165,7 @@
        "('teddy', tensor(2), tensor([1.6561e-06, 1.1294e-16, 1.0000e+00]))"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -147,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 59,
    "id": "9f3a2ab2",
    "metadata": {},
    "outputs": [],
@@ -158,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 60,
    "id": "c476f09a",
    "metadata": {},
    "outputs": [],
@@ -171,18 +200,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 61,
    "id": "09839ebb",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\teent\\anaconda3\\Lib\\site-packages\\fastai\\torch_core.py:263: UserWarning: 'has_mps' is deprecated, please use 'torch.backends.mps.is_built()'\n",
-      "  return getattr(torch, 'has_mps', False)\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -228,7 +249,7 @@
        " 'teddy': 0.9999983310699463}"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }

app.py

@@ -1,18 +1,40 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
 
 # %% auto 0
-__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'is_cat', 'classify_img']
+__all__ = ['btn_upload', 'out_pl', 'lbl_pred', 'learn', 'categories', 'image', 'label', 'examples', 'intf', 'DataLoaders',
+           'is_cat', 'classify_img']
 
 # %% app.ipynb 2
-from  fastai.vision.all import *
+from fastai.vision.all import *
 import gradio as gr
-
+from fastbook import *
+from fastai.vision.widgets import *
+import gradio as gr
+btn_upload = widgets.FileUpload()
+out_pl = widgets.Output()
+lbl_pred = widgets.Label()
 
 # %% app.ipynb 3
-def is_cat(x): return x[0].isupper() 
+# def on_data_change(change):
+#     lbl_pred.value = ''
+#     img = PILImage.create(btn_upload.data[-1])
+#     out_pl.clear_output()
+#     with out_pl: display(img.to_thumb(128,128))
+#     pred,pred_idx,probs = learn_inf.predict(img)
+#     lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'
+
+class DataLoaders(GetAttr):
+    def __init__(self, *loaders): self.loaders = loaders
+    def __getitem__(self, i): return self.loaders[i]
+    train,valid = add_props(lambda i,self: self[i])
+    
+def is_cat(x): return x[0].isupper()
+
+
 
 # %% app.ipynb 4
 learn = load_learner('export.pkl')
+print(type(learn))
 
 # %% app.ipynb 7
 categories = ('black', 'grizzly', 'teddy')