Second commit

.ipynb_checkpoints/Untitled-checkpoint.ipynb

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

Untitled.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83eb7185",
+   "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": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

app.py

@@ -1,9 +1,25 @@
+from fastai.vision.all import *
 import gradio as gr
 
 
-def greet(name):
-    return "Hello " + name + "1"
+def is_cat(x): return
 
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+learn = load_learner('model.pkl')
+
+categories = ('Dog', 'Cat')
+
+
+def classify_image(img):
+    pred, pred_idx, probs = learn.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+
+image = gr.inputs.Image(shape=(192, 192))
+label = gr.outputs.Label()
+examples = ["dog.jpg", "cat.jpg", "dunno.jpg"]
+
+
+iface = gr.Interface(fn=classify_image, inputs=image,
+                     outputs=label, examples=examples)
+iface.launch(inline=False)

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:106a4d704e07b7ef014ca8a9c12599b48bfceb54fd41c4c2a0712c52003c897c
+size 47063121