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finalapp.ipynb

@@ -0,0 +1,274 @@
+{
+ "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"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

finalapp.py

@@ -0,0 +1,274 @@
+{
+ "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"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}