Upload folder using huggingface_hub

README.md

@@ -1,12 +1,12 @@
+
 ---
-title: Digit Classifier 3-x
-emoji: 🏃
-colorFrom: purple
-colorTo: gray
+title: digit_classifier_3-x 
+emoji: 🔥
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
-sdk_version: 4.3.0
-app_file: app.py
+sdk_version: 3.50.1
+app_file: run.py
 pinned: false
+hf_oauth: true
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

requirements.txt

@@ -0,0 +1 @@
+tensorflow

run.ipynb

@@ -0,0 +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 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

@@ -0,0 +1,31 @@
+from urllib.request import urlretrieve
+
+import tensorflow as tf
+
+import gradio as gr
+
+urlretrieve(
+    "https://gr-models.s3-us-west-2.amazonaws.com/mnist-model.h5", "mnist-model.h5"
+)
+model = tf.keras.models.load_model("mnist-model.h5")
+
+
+def recognize_digit(image):
+    image = image.reshape(1, -1)
+    prediction = model.predict(image).tolist()[0]
+    return {str(i): prediction[i] for i in range(10)}
+
+
+im = gr.Image(shape=(28, 28), image_mode="L", invert_colors=False, source="canvas")
+
+demo = gr.Interface(
+    recognize_digit,
+    im,
+    gr.Label(num_top_classes=3),
+    live=True,
+    interpretation="default",
+    capture_session=True,
+)
+
+if __name__ == "__main__":
+    demo.launch()