Upload folder using huggingface_hub

README.md

@@ -1,12 +1,12 @@
+
 ---
-title: Image Component Events Main
-emoji: 🐢
-colorFrom: purple
-colorTo: gray
+title: image_component_events_main 
+emoji: 🔥
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
 sdk_version: 4.3.0
-app_file: app.py
+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 @@
+https://gradio-builds.s3.amazonaws.com/2f805a7dd3d2b64b098f659dadd5d01258290521/gradio-4.3.0-py3-none-any.whl

run.ipynb

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+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_component_events"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            input_img = gr.Image(type=\"filepath\", label=\"Input Image\", sources=[\"upload\", \"clipboard\"])\n", "        with gr.Column():\n", "            output_img = gr.Image(type=\"filepath\", label=\"Output Image\", sources=[\"upload\", \"clipboard\"])\n", "        with gr.Column():\n", "            num_change = gr.Number(label=\"# Change Events\", value=0)\n", "            num_load = gr.Number(label=\"# Upload Events\", value=0)\n", "            num_change_o = gr.Number(label=\"# Change Events Output\", value=0)\n", "            num_clear = gr.Number(label=\"# Clear Events\", value=0)\n", "        input_img.upload(lambda s, n: (s, n + 1), [input_img, num_load], [output_img, num_load])\n", "        input_img.change(lambda n: n + 1, num_change, num_change)\n", "        input_img.clear(lambda n: n + 1, num_clear, num_clear)\n", "        output_img.change(lambda n: n + 1, num_change_o, num_change_o)\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

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+import gradio as gr
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            input_img = gr.Image(type="filepath", label="Input Image", sources=["upload", "clipboard"])
+        with gr.Column():
+            output_img = gr.Image(type="filepath", label="Output Image", sources=["upload", "clipboard"])
+        with gr.Column():
+            num_change = gr.Number(label="# Change Events", value=0)
+            num_load = gr.Number(label="# Upload Events", value=0)
+            num_change_o = gr.Number(label="# Change Events Output", value=0)
+            num_clear = gr.Number(label="# Clear Events", value=0)
+        input_img.upload(lambda s, n: (s, n + 1), [input_img, num_load], [output_img, num_load])
+        input_img.change(lambda n: n + 1, num_change, num_change)
+        input_img.clear(lambda n: n + 1, num_clear, num_clear)
+        output_img.change(lambda n: n + 1, num_change_o, num_change_o)
+
+if __name__ == "__main__":
+    demo.launch()