fixing bugs

app.ipynb

@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "e49d72ea",
+   "id": "7e08d187",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,7 +13,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "6ffe5ce8",
+   "id": "e449d107",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,7 +24,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "dd7a7a33",
+   "id": "7fa3f38f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "501504ae",
+   "id": "ba04f1d8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -52,7 +52,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "bbedafc3",
+   "id": "5c77d812",
    "metadata": {},
    "outputs": [
     {
@@ -259,7 +259,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "321e5e2a",
+   "id": "7d39cbf6",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -267,7 +267,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "eab7bd30",
+   "id": "02a65a7b",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -275,7 +275,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "aab565a0",
+   "id": "ab54f129",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -283,7 +283,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1f6ef9c6",
+   "id": "00153d99",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -291,7 +291,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0d8b4820",
+   "id": "d3beb9d9",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -299,7 +299,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "29bc0545",
+   "id": "384fdf33",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -307,7 +307,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "eec30b86",
+   "id": "301ee4c4",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -315,14 +315,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "da262cb1",
+   "id": "4ef0f13c",
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "markdown",
-   "id": "1c886008",
+   "id": "2712605e",
    "metadata": {},
    "source": [
     "## export notebook"
@@ -330,53 +330,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "e8b709a5",
+   "execution_count": 11,
+   "id": "217ceef9",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ImportError",
-     "evalue": "cannot import name 'notebook2script' from 'nbdev.export' (/home/gilopez/mambaforge/envs/fastai/lib/python3.9/site-packages/nbdev/export.py)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn [10], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# export notebook\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnbdev\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexport\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m notebook2script\n",
-      "\u001b[0;31mImportError\u001b[0m: cannot import name 'notebook2script' from 'nbdev.export' (/home/gilopez/mambaforge/envs/fastai/lib/python3.9/site-packages/nbdev/export.py)"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# export notebook\n",
-    "from nbdev.export import notebook2script"
+    "from nbdev.export import nb_export"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "582eea60",
+   "execution_count": 12,
+   "id": "033dba80",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'notebook2script' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn [9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnotebook2script\u001b[49m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapp.ipynb\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'notebook2script' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "notebook2script('app.ipynb')"
+    "nb_export('app.ipynb')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1626b5fd",
+   "id": "24a6fd20",
    "metadata": {},
    "outputs": [],
    "source": []

app.py

@@ -1,7 +1,23 @@
-import gradio as gr
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb.
+
+# %% auto 0
+__all__ = ['learn', 'labels', 'predict']
+
+# %% ../app.ipynb 1
+# write this at the top of each cell which has to be exported to final script
 
-def greet(name):
-    return "Hello " + name + "!!"
+# %% ../app.ipynb 2
+from fastai.vision.all import *
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+# %% ../app.ipynb 3
+# this identifies the type of classifier
+learn = load_learner('export.pkl')
+labels = learn.dls.vocab
+def predict(img):
+    img = PILImage.create(img)
+    pred,pred_idx,probs = learn.predict(img)
+    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+
+# %% ../app.ipynb 4
+import gradio as gr
+gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3)).launch(share=True)

learning_gradio/app.py

@@ -1,23 +0,0 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb.
-
-# %% auto 0
-__all__ = ['learn', 'labels', 'predict']
-
-# %% ../app.ipynb 1
-# write this at the top of each cell which has to be exported to final script
-
-# %% ../app.ipynb 2
-from fastai.vision.all import *
-
-# %% ../app.ipynb 3
-# this identifies the type of classifier
-learn = load_learner('export.pkl')
-labels = learn.dls.vocab
-def predict(img):
-    img = PILImage.create(img)
-    pred,pred_idx,probs = learn.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
-
-# %% ../app.ipynb 4
-import gradio as gr
-gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3)).launch(share=True)