upgrade

app.ipynb

@@ -0,0 +1,125 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dogs vs Cats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|default_exp app\n",
+    "!pip install gradio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "from fastai.vision.all import *\n",
+    "import gradio as gr\n",
+    "\n",
+    "def is_cat(x): return x[0].isupper()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "im = PILImage.create('dog.jpg')\n",
+    "im.thumbnail((192,192))\n",
+    "im"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "\n",
+    "learn = load_learner(\"model.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%time learn.predict(im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "categories = ('Dog', 'Cat')\n",
+    "\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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classify_image(im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "image = gr.inputs.Image(shape=(192,192))\n",
+    "label = gr.outputs.Label()\n",
+    "examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg']\n",
+    "\n",
+    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
+    "intf.launch(inline=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "myenv",
+   "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"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

app.py

@@ -1,9 +1,49 @@
+
+# %% [markdown]
+# Dogs vs Cats
+
+# %%
+#|default_exp app
+!pip install gradio
+
+# %%
+#|export
+from fastai.vision.all import *
 import gradio as gr
 
+def is_cat(x): return x[0].isupper()
+
+# %%
+im = PILImage.create('dog.jpg')
+im.thumbnail((192,192))
+im
+
+# %%
+#|export
+
+learn = load_learner("model.pkl")
+
+# %%
+%time learn.predict(im)
+
+# %%
+#|export
+categories = ('Dog', 'Cat')
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+# %%
+classify_image(im)
+
+# %%
+#|export
+image = gr.inputs.Image(shape=(192,192))
+label = gr.outputs.Label()
+examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg']
 
-def greet(name):
-    return "Hello " + name + "!!"
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)
 
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()

old_app.py

@@ -0,0 +1,9 @@
+import gradio as gr
+
+
+def greet(name):
+    return "Hello " + name + "!!"
+
+
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()