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{
"cells": [
{
"cell_type": "raw",
"metadata": {},
"source": [
"---\n",
"description: Gradio app.py\n",
"output-file: app.html\n",
"title: app\n",
"\n",
"---\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"language": "python"
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#: eval: false\n",
"load_dotenv()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Put the chat backend pieces together"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"language": "python"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"skip\n"
]
}
],
"source": [
"def create_demo(bot: ConversationBot):\n",
" sample_images = []\n",
" all_imgs = [f\"{SAMPLE_IMG_DIR}/{img}\" for img in os.listdir(SAMPLE_IMG_DIR)]\n",
" for i, img in enumerate(all_imgs):\n",
" if i in [\n",
" 1,\n",
" 2,\n",
" 3,\n",
" ]:\n",
" sample_images.append(img)\n",
" with gr.Blocks() as demo:\n",
" gr_img = gr.Image(type=\"filepath\")\n",
" btn = gr.Button(value=\"Submit image\")\n",
" ingredients_msg = gr.Text(label=\"Ingredients from image\")\n",
" btn.click(bot.run_img, inputs=[gr_img], outputs=[ingredients_msg])\n",
" gr.Examples(\n",
" examples=sample_images,\n",
" inputs=gr_img,\n",
" )\n",
"\n",
" chatbot = gr.Chatbot(\n",
" value=[(None, bot.ai_prompt_questions[\"ingredients\"].prompt.template)]\n",
" )\n",
"\n",
" msg = gr.Textbox()\n",
" # clear = gr.Button(\"Clear\")\n",
" gr.Markdown(\n",
" \"\"\"\n",
" **🔃Refresh the page to start from scratch🔃** \n",
" \n",
" Recipe search tool powered by the [Edamam API](https://www.edamam.com/) \n",
" \n",
" \n",
" \"\"\"\n",
" )\n",
" msg.submit(\n",
" fn=bot.respond, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False\n",
" )\n",
" # clear.click(lambda: None, None, chatbot, queue=False).then(bot.reset)\n",
" return demo"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"language": "python"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Closing server running on port: 7860\n",
"Running on local URL: http://127.0.0.1:7860\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#| eval: false\n",
"if \"demo\" in globals():\n",
" demo.close()\n",
"\n",
"demo = create_demo(\n",
" ConversationBot(\n",
" vegan_ingred_finder=vegan_ingred_finder, img_cap=img_cap, verbose=True\n",
" )\n",
")\n",
"demo.launch()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "python3",
"language": "python",
"name": "python3"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|