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requirements.txt

@@ -1,2 +1,2 @@
 tensorflow
-https://gradio-main-build.s3.amazonaws.com/e0eea96766b70704016c325b260bdf77147c2f68/gradio-3.27.0-py3-none-any.whl
+https://gradio-main-build.s3.amazonaws.com/6e7abf8645661f57a08f88e2bd55d31f101e2065/gradio-3.27.0-py3-none-any.whl

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: digit_classifier"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio tensorflow"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["from urllib.request import urlretrieve\n", "\n", "import tensorflow as tf\n", "\n", "import gradio\n", "import gradio as gr\n", "\n", "urlretrieve(\n", "    \"https://gr-models.s3-us-west-2.amazonaws.com/mnist-model.h5\", \"mnist-model.h5\"\n", ")\n", "model = tf.keras.models.load_model(\"mnist-model.h5\")\n", "\n", "\n", "def recognize_digit(image):\n", "    image = image.reshape(1, -1)\n", "    prediction = model.predict(image).tolist()[0]\n", "    return {str(i): prediction[i] for i in range(10)}\n", "\n", "\n", "im = gradio.Image(shape=(28, 28), image_mode=\"L\", invert_colors=False, source=\"canvas\")\n", "\n", "demo = gr.Interface(\n", "    recognize_digit,\n", "    im,\n", "    gradio.Label(num_top_classes=3),\n", "    live=True,\n", "    interpretation=\"default\",\n", "    capture_session=True,\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": 302934307671667531413257853548643485645, "metadata": {}, "source": ["# Gradio Demo: digit_classifier"]}, {"cell_type": "code", "execution_count": null, "id": 272996653310673477252411125948039410165, "metadata": {}, "outputs": [], "source": ["!pip install -q gradio tensorflow"]}, {"cell_type": "code", "execution_count": null, "id": 288918539441861185822528903084949547379, "metadata": {}, "outputs": [], "source": ["from urllib.request import urlretrieve\n", "\n", "import tensorflow as tf\n", "\n", "import gradio as gr\n", "\n", "urlretrieve(\n", "    \"https://gr-models.s3-us-west-2.amazonaws.com/mnist-model.h5\", \"mnist-model.h5\"\n", ")\n", "model = tf.keras.models.load_model(\"mnist-model.h5\")\n", "\n", "\n", "def recognize_digit(image):\n", "    image = image.reshape(1, -1)\n", "    prediction = model.predict(image).tolist()[0]\n", "    return {str(i): prediction[i] for i in range(10)}\n", "\n", "\n", "im = gr.Image(shape=(28, 28), image_mode=\"L\", invert_colors=False, source=\"canvas\")\n", "\n", "demo = gr.Interface(\n", "    recognize_digit,\n", "    im,\n", "    gr.Label(num_top_classes=3),\n", "    live=True,\n", "    interpretation=\"default\",\n", "    capture_session=True,\n", ")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -2,7 +2,6 @@ from urllib.request import urlretrieve
 
 import tensorflow as tf
 
-import gradio
 import gradio as gr
 
 urlretrieve(
@@ -17,12 +16,12 @@ def recognize_digit(image):
     return {str(i): prediction[i] for i in range(10)}
 
 
-im = gradio.Image(shape=(28, 28), image_mode="L", invert_colors=False, source="canvas")
+im = gr.Image(shape=(28, 28), image_mode="L", invert_colors=False, source="canvas")
 
 demo = gr.Interface(
     recognize_digit,
     im,
-    gradio.Label(num_top_classes=3),
+    gr.Label(num_top_classes=3),
     live=True,
     interpretation="default",
     capture_session=True,