Added blog post

Added blog post comment and changed top 3 labels code

FilmStarsApp.ipynb

@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "37d47b60",
    "metadata": {},
    "outputs": [],
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "96a084dc",
    "metadata": {},
    "outputs": [],
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "0dd4db80",
    "metadata": {},
    "outputs": [],
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "cf73c204",
    "metadata": {},
    "outputs": [],
@@ -53,89 +53,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "18bfac9a",
    "metadata": {},
    "outputs": [],
    "source": [
     "#|export\n",
+    "labels = learn50.dls.vocab\n",
     "def classify_image(pic):\n",
     "    pred,pred_idx,probs = learn50.predict(PILImage.create(pic))\n",
-    "    ps, idxs = probs.topk(3)\n",
-    "    return dict(zip([learn50.dls.vocab[idx] for idx in idxs], map(float,ps)))\n"
+    "    #ps, idxs = probs.topk(3)\n",
+    "    return  {labels[i]: float(probs[i]) for i in range(len(labels))}\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "d49e1a0e",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'Penelope Cruz': 0.5769789218902588,\n",
-       " 'Halle Berry': 0.12028565257787704,\n",
-       " 'Angelina Jolie': 0.08253376185894012}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "classify_image(pic)"
+    "classify_image(pic);"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "773dab23",
    "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": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#|export\n",
     "image = gr.components.Image()\n",
-    "label = gr.components.Label()\n",
     "examples = ['Angelina Jolie.jpg','Penelope Cruz.jpg','Jack Nicholson.jpg']\n",
     "title = \"Identify film stars similar to your image\"\n",
     "description = \"Try uploading a photograph of yourself\"\n",
-    "intfr = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples, title=title, description=description)\n",
+    "article = \"<p style='text-align: center'><a href='https://science4performance.com/2023/01/01/eddy-goes-to-hollywood/' target='_blank'>Blog post</a></p>\"\n",
+    "intfr = gr.Interface(fn=classify_image, inputs=image, outputs=gr.components.Label(num_top_classes=3), examples=examples, title=title, description=description, article=article)\n",
     "intfr.launch(inline=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 17,
    "id": "705f241c",
    "metadata": {},
    "outputs": [],

app.py

@@ -1,7 +1,7 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../FilmStarsApp.ipynb.
 
 # %% auto 0
-__all__ = ['learn50', 'image', 'label', 'examples', 'title', 'intfr', 'classify_image']
+__all__ = ['learn50', 'labels', 'image', 'examples', 'title', 'description', 'article', 'intfr', 'classify_image']
 
 # %% ../FilmStarsApp.ipynb 2
 from fastai.vision.all import *
@@ -11,17 +11,18 @@ import gradio as gr
 learn50 = load_learner('filmstars50.pkl')
 
 # %% ../FilmStarsApp.ipynb 5
+labels = learn50.dls.vocab
 def classify_image(pic):
     pred,pred_idx,probs = learn50.predict(PILImage.create(pic))
-    ps, idxs = probs.topk(3)
-    return dict(zip([learn50.dls.vocab[idx] for idx in idxs], map(float,ps)))
+    #ps, idxs = probs.topk(3)
+    return  {labels[i]: float(probs[i]) for i in range(len(labels))}
 
 
 # %% ../FilmStarsApp.ipynb 7
 image = gr.components.Image()
-label = gr.components.Label()
 examples = ['Angelina Jolie.jpg','Penelope Cruz.jpg','Jack Nicholson.jpg']
 title = "Identify film stars similar to your image"
-description = "Try uploading a photo of yourself"
-intfr = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples, title=title, description=description)
+description = "Try uploading a photograph of yourself"
+article = "<p style='text-align: center'><a href='https://science4performance.com/2023/01/01/eddy-goes-to-hollywood/' target='_blank'>Blog post</a></p>"
+intfr = gr.Interface(fn=classify_image, inputs=image, outputs=gr.components.Label(num_top_classes=3), examples=examples, title=title, description=description, article=article)
 intfr.launch(inline=False)