updated array issue

app.ipynb

@@ -189,9 +189,10 @@
     "except ImportError:\n",
     "    can_display = False\n",
     "\n",
-    "# #|export\n",
-    "def on_click_classify(img):\n",
-    "    # img = PILImage.create(btn_upload.data[-1])\n",
+    "def on_click_classify(img_array):\n",
+    "    # Convert numpy array to PIL Image\n",
+    "    img = Image.fromarray(img_array.astype('uint8'), 'RGB')\n",
+    "    \n",
     "    out_pl = widgets.Output()\n",
     "    out_pl.clear_output()\n",
     "    if can_display:\n",
@@ -201,9 +202,10 @@
     "    else:\n",
     "        # Save to a file if display is not available\n",
     "        img.to_thumb(128, 128).save('output_thumbnail.png')\n",
-    "        # print(\"Thumbnail saved to 'output_thumbnail.png'.\")\n",
+    "        print(\"Thumbnail saved to 'output_thumbnail.png'.\")\n",
+    "    \n",
+    "    # Assuming learn_inf is already defined and loaded elsewhere in your code\n",
     "    pred, pred_idx, probs = learn_inf.predict(img)\n",
-    "    # lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'\n",
     "    return f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'"
    ]
   },
@@ -237,7 +239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 48,
    "id": "a6e2a398-f6c8-4444-b0f2-e538a1b36da2",
    "metadata": {},
    "outputs": [
@@ -245,7 +247,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on local URL:  http://127.0.0.1:7865\n",
+      "Running on local URL:  http://127.0.0.1:7866\n",
       "\n",
       "To create a public link, set `share=True` in `launch()`.\n"
      ]
@@ -254,46 +256,9 @@
      "data": {
       "text/plain": []
      },
-     "execution_count": 46,
+     "execution_count": 48,
      "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": [
@@ -316,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 49,
    "id": "f5c57105-41fe-4d79-a2b0-d52ed75ea6cb",
    "metadata": {},
    "outputs": [

app.py

@@ -23,9 +23,10 @@ try:
 except ImportError:
     can_display = False
 
-# #|export
-def on_click_classify(img):
-    # img = PILImage.create(btn_upload.data[-1])
+def on_click_classify(img_array):
+    # Convert numpy array to PIL Image
+    img = Image.fromarray(img_array.astype('uint8'), 'RGB')
+    
     out_pl = widgets.Output()
     out_pl.clear_output()
     if can_display:
@@ -35,9 +36,10 @@ def on_click_classify(img):
     else:
         # Save to a file if display is not available
         img.to_thumb(128, 128).save('output_thumbnail.png')
-        # print("Thumbnail saved to 'output_thumbnail.png'.")
+        print("Thumbnail saved to 'output_thumbnail.png'.")
+    
+    # Assuming learn_inf is already defined and loaded elsewhere in your code
     pred, pred_idx, probs = learn_inf.predict(img)
-    # lbl_pred.value = f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'
     return f'Prediction: {pred}; Probability: {probs[pred_idx]:.04f}'
 
 # %% app.ipynb 17