new start

.ipynb_checkpoints/app-checkpoint.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6725f79d",
+   "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

app.ipynb

@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6725f79d",
+   "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

app/app.py

@@ -1,24 +0,0 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: ../dogvcat_test.ipynb.
-
-# %% auto 0
-__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'classify_image']
-
-# %% ../dogvcat_test.ipynb 2
-learn = load_learner('model.pkl')
-
-# %% ../dogvcat_test.ipynb 4
-categories = ('Dog', 'Cat')
-
-def classify_image(img):
-    pred,idx,probs = learn.predict(img)
-    #format the output of learn.predict. dict creates dictionary, zip associates the labels with the probabilities and map converts the tensor into a float
-    return dict(zip(categories, map(float,probs)))
-
-# %% ../dogvcat_test.ipynb 6
-image = gr.inputs.Image(shape=(192,192))
-label = gr.outputs.Label()
-examples = ['dog.jpeg', 'cat.jpg', 'dunno.jpeg']
-
-intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
-intf.launch(inline=False)
-