Adjustment 3

.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/app-checkpoint.py

@@ -0,0 +1,20 @@
+import fastai
+from fastai.vision import load_learner
+import gradio as gr
+
+def is_cat(x):
+    return x[0].isupper()
+
+learn = load_learner('model.pkl')
+
+categories = ('Dog', 'Cat')
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories), map(float,probs))
+
+image = gr.inputs.Image(shape=(192,192))
+label = gr.outputs.Label()
+
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label)
+intf.launch(inline=False)

Untitled.ipynb

@@ -0,0 +1,72 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a9c14507-a23e-4bd8-990e-d45a7460339d",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'MNIST_PATH' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[9], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastai\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvision\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mall\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m----> 2\u001b[0m path \u001b[38;5;241m=\u001b[39m untar_data(MNIST_PATH)\n\u001b[0;32m      3\u001b[0m data \u001b[38;5;241m=\u001b[39m image_data_from_folder(path)\n\u001b[0;32m      4\u001b[0m learn \u001b[38;5;241m=\u001b[39m cnn_learner(data, models\u001b[38;5;241m.\u001b[39mresnet18, metrics\u001b[38;5;241m=\u001b[39maccuracy)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'MNIST_PATH' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "from fastai.vision.all import *\n",
+    "import gradio as gr\n",
+    "def is_cat(x):\n",
+    "    return x[0].isupper()\n",
+    "\n",
+    "learn = load_learner('model.pkl')\n",
+    "\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))\n",
+    "\n",
+    "image = gr.inputs.Image(shape=(192,192))\n",
+    "label = gr.outputs.Label()\n",
+    "\n",
+    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label)\n",
+    "intf.launch(inline=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c353f37d-7f82-4179-bf86-7cfa96d3d0b0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

app.py

@@ -1,5 +1,4 @@
-import fastai
-from fastai.vision import load_learner
+from fastai.vision import *
 import gradio as gr
 
 def is_cat(x):

model.pkl

@@ -1,3 +1,3 @@
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-size 47064126
+oid sha256:f60f78e8c4a2a58e05a5503956ac1f9e66eeb24818025efb9808031a61221011
+size 47062270