finalapp.py

{
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 {
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 "collapsed": true
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 "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"
 ],
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 "cell_type": "code",
 "execution_count": 21,
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 "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)"
 ],
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 "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"
 ],
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 {
 "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\")"
 ],
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 "cell_type": "code",
 "execution_count": 22,
 "outputs": [],
 "source": [],
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 "cell_type": "code",
 "execution_count": 22,
 "outputs": [],
 "source": [],
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 "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>"
 },
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 "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 [email protected] 😊\")\n",
 "\n",
 "demo.launch(share=True, debug=True)"
 ],
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 },
 {
 "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()"
 ],
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