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{
"cells": [
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import gradio as gr\n",
"import requests\n",
"import json"
]
},
{
"cell_type": "code",
"execution_count": 20,
"outputs": [],
"source": [
"def list_to_dict(data):\n",
" results = {}\n",
"\n",
" for i in range(len(data)):\n",
" # Access the i-th dictionary in the list using an integer index\n",
" d = data[i]\n",
" # Assign the value of the 'label' key to the 'score' value in the results dictionary\n",
" results[d['label']] = d['score']\n",
"\n",
" # The results dictionary will now contain the label-score pairs from the data list\n",
" return results"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 21,
"outputs": [],
"source": [
"\n",
"\n",
"API_URL = \"https://api-inference.huggingface.co/models/nateraw/food\"\n",
"headers = {\"Authorization\": \"Bearer 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\"}\n",
"\n",
"def query(filename):\n",
" with open(filename, \"rb\") as f:\n",
" data = f.read()\n",
" response = requests.request(\"POST\", API_URL, headers=headers, data=data)\n",
" output = json.loads(response.content.decode(\"utf-8\"))\n",
" return list_to_dict(output),json.dumps(output, indent=2, sort_keys=True)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 27,
"outputs": [],
"source": [
"def get_nutrition_info(food_name):\n",
" #Make request to Nutritionix API\n",
" response = requests.get(\n",
" \"https://trackapi.nutritionix.com/v2/search/instant\",\n",
" params={\"query\": food_name},\n",
" headers={\n",
" \"x-app-id\": \"63a710ef\",\n",
" \"x-app-key\": \"3ddc7e3feda88e1cf6dd355fb26cb261\"\n",
" }\n",
" )\n",
" #Parse response and return relevant information\n",
" data = response.json()\n",
" response = data[\"branded\"][0][\"photo\"][\"thumb\"]\n",
"\n",
" # Open the image using PIL\n",
"\n",
" return {\n",
" \"food_name\": data[\"branded\"][0][\"food_name\"],\n",
" \"calories\": data[\"branded\"][0][\"nf_calories\"],\n",
" \"serving_size\": data[\"branded\"][0][\"serving_qty\"],\n",
" \"serving_unit\": data[\"branded\"][0][\"serving_unit\"],\n",
" #\"images\": data[\"branded\"][0][\"photo\"]\n",
" },response"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 28,
"outputs": [
{
"data": {
"text/plain": "({'food_name': 'Hamburger',\n 'calories': 340,\n 'serving_size': 1,\n 'serving_unit': 'sandwich'},\n 'https://d2eawub7utcl6.cloudfront.net/images/nix-apple-grey.png')"
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_nutrition_info(\"Hamburger\")"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 22,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 22,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7869\n",
"Running on public URL: https://f7f1e48778aede65.gradio.app\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
]
},
{
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": "<div><iframe src=\"https://f7f1e48778aede65.gradio.app\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"with gr.Blocks() as demo:\n",
" gr.Markdown(\"Food-Classification-Calorie-Estimation and Volume-Estimation\")\n",
" with gr.Tab(\"Food Classification\"):\n",
" text_input = gr.Image(type=\"filepath\")\n",
" text_output = [gr.Label(num_top_classes=6),\n",
" gr.Textbox()\n",
" ]\n",
" text_button = gr.Button(\"Food Classification\")\n",
" with gr.Tab(\"Food Calorie Estimation\"):\n",
" image_input = gr.Textbox(label=\"Please enter the name of the Food you want to get calorie\")\n",
" image_output = [gr.Textbox(),\n",
" gr.Image(type=\"filepath\")\n",
" ]\n",
" image_button = gr.Button(\"Estimate Calories!\")\n",
" with gr.Tab(\"Volume Estimation\"):\n",
" _image_input = gr.Textbox(label=\"Please enter the name of the Food you want to get calorie\")\n",
" _image_output = [gr.Textbox(),\n",
" gr.Image()\n",
" ]\n",
" _image_button = gr.Button(\"Volume Calculation\")\n",
" with gr.Tab(\"Future Works\"):\n",
" gr.Markdown(\"Future work on Food Classification\")\n",
" gr.Markdown(\n",
" \"Currently the Model is trained on food-101 Dataset, which has 100 classes, In the future iteration of the project we would like to train the model on UNIMIB Dataset with 256 Food Classes\")\n",
" gr.Markdown(\"Future work on Volume Estimation\")\n",
" gr.Markdown(\n",
" \"The volume model has been trained on Apple AR Toolkit and thus can be executred only on Apple devices ie a iOS platform, In futur we would like to train the volume model such that it is Platform independent\")\n",
" gr.Markdown(\"Future work on Calorie Estimation\")\n",
" gr.Markdown(\n",
" \"The Calorie Estimation currently relies on Nutritionix API , In Future Iteration we would like to build our own Custom Database of Major Food Product across New York Restaurent\")\n",
" gr.Markdown(\"https://github.com/Ali-Maq/Food-Classification-Volume-Estimation-and-Calorie-Estimation/blob/main/README.md\")\n",
"\n",
" text_button.click(query, inputs=text_input, outputs=text_output)\n",
" image_button.click(get_nutrition_info, inputs=image_input, outputs=image_output)\n",
" _image_button.click(get_nutrition_info, inputs=_image_input, outputs=_image_output)\n",
" with gr.Accordion(\"Open for More!\"):\n",
" gr.Markdown(\"🍎 Designed and built by Ali Under the Guidance of Professor Dennis Shasha\")\n",
" gr.Markdown(\"Contact me at ali.quidwai@nyu.edu 😊\")\n",
"\n",
"demo.launch(share=True, debug=True)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"is_executing": true
}
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"import numpy as np\n",
"import gradio as gr\n",
"\n",
"def flip_text(x):\n",
" return x[::-1]\n",
"\n",
"def flip_image(x):\n",
" return np.fliplr(x)\n",
"\n",
"with gr.Blocks() as demo:\n",
" gr.Markdown(\"Flip text or image files using this demo.\")\n",
" with gr.Tab(\"Flip Text\"):\n",
" text_input = gr.Textbox()\n",
" text_output = gr.Textbox()\n",
" text_button = gr.Button(\"Flip\")\n",
" with gr.Tab(\"Flip Image\"):\n",
" with gr.Row():\n",
" image_input = gr.Image()\n",
" image_output = gr.Image()\n",
" image_button = gr.Button(\"Flip\")\n",
"\n",
" with gr.Accordion(\"Open for More!\"):\n",
" gr.Markdown(\"Look at me...\")\n",
"\n",
" text_button.click(get_nutrition_info, inputs=text_input, outputs=text_output)\n",
" image_button.click(query, inputs=image_input, outputs=image_output)\n",
"\n",
"demo.launch()"
],
"metadata": {
"collapsed": false,
"pycharm": {
"is_executing": true
}
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
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"nbformat": 4,
"nbformat_minor": 0
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