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app.py

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+from PIL import Image
+from transformers import ViTFeatureExtractor, ViTForImageClassification
+import warnings
+import requests
+import gradio as gr
+
+warnings.filterwarnings('ignore')
+
+# Load the pre-trained Vision Transformer model and feature extractor
+model_name = "google/vit-base-patch16-224"
+feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
+model = ViTForImageClassification.from_pretrained(model_name)
+
+# API key for the nutrition information
+api_key = 'Place API key here'
+
+def identify_image(image_path):
+    """Identify the food item in the image."""
+    image = Image.open(image_path)
+    inputs = feature_extractor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class_idx = logits.argmax(-1).item()
+    predicted_label = model.config.id2label[predicted_class_idx]
+    food_name = predicted_label.split(',')[0]
+    return food_name
+
+def get_calories(food_name):
+    """Get the calorie information of the identified food item."""
+    api_url = 'https://api.api-ninjas.com/v1/nutrition?query={}'.format(food_name)
+    response = requests.get(api_url, headers={'X-Api-Key': api_key})
+    if response.status_code == requests.codes.ok:
+        nutrition_info = response.json()
+    else:
+        nutrition_info = {"Error": response.status_code, "Message": response.text}
+    return nutrition_info
+
+def format_nutrition_info(nutrition_info):
+    """Format the nutritional information into an HTML table."""
+    if "Error" in nutrition_info:
+        return f"Error: {nutrition_info['Error']} - {nutrition_info['Message']}"
+    
+    if len(nutrition_info) == 0:
+        return "No nutritional information found."
+
+    nutrition_data = nutrition_info[0]
+    table = f"""
+    <table border="1" style="width: 100%; border-collapse: collapse;">
+        <tr><th colspan="4" style="text-align: center;"><b>Nutrition Facts</b></th></tr>
+        <tr><td colspan="4" style="text-align: center;"><b>Food Name: {nutrition_data['name']}</b></td></tr>
+        <tr>
+            <td style="text-align: left;"><b>Calories</b></td><td style="text-align: right;">{nutrition_data['calories']}</td>
+            <td style="text-align: left;"><b>Serving Size (g)</b></td><td style="text-align: right;">{nutrition_data['serving_size_g']}</td>
+        </tr>
+        <tr>
+            <td style="text-align: left;"><b>Total Fat (g)</b></td><td style="text-align: right;">{nutrition_data['fat_total_g']}</td>
+            <td style="text-align: left;"><b>Saturated Fat (g)</b></td><td style="text-align: right;">{nutrition_data['fat_saturated_g']}</td>
+        </tr>
+        <tr>
+            <td style="text-align: left;"><b>Protein (g)</b></td><td style="text-align: right;">{nutrition_data['protein_g']}</td>
+            <td style="text-align: left;"><b>Sodium (mg)</b></td><td style="text-align: right;">{nutrition_data['sodium_mg']}</td>
+        </tr>
+        <tr>
+            <td style="text-align: left;"><b>Potassium (mg)</b></td><td style="text-align: right;">{nutrition_data['potassium_mg']}</td>
+            <td style="text-align: left;"><b>Cholesterol (mg)</b></td><td style="text-align: right;">{nutrition_data['cholesterol_mg']}</td>
+        </tr>
+        <tr>
+            <td style="text-align: left;"><b>Total Carbohydrates (g)</b></td><td style="text-align: right;">{nutrition_data['carbohydrates_total_g']}</td>
+            <td style="text-align: left;"><b>Fiber (g)</b></td><td style="text-align: right;">{nutrition_data['fiber_g']}</td>
+        </tr>
+        <tr>
+            <td style="text-align: left;"><b>Sugar (g)</b></td><td style="text-align: right;">{nutrition_data['sugar_g']}</td>
+            <td></td><td></td>
+        </tr>
+    </table>
+    """
+    return table
+
+def main_process(image_path):
+    """Identify the food item and fetch its calorie information."""
+    food_name = identify_image(image_path)
+    nutrition_info = get_calories(food_name)
+    formatted_nutrition_info = format_nutrition_info(nutrition_info)
+    return formatted_nutrition_info
+
+# Define the Gradio interface
+def gradio_interface(image):
+    formatted_nutrition_info = main_process(image)
+    return formatted_nutrition_info
+
+# Create the Gradio UI
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=gr.Image(type="filepath"),
+    outputs="html",
+    title="Food Identification and Nutrition Info",
+    description="Upload an image of food to get nutritional information.",
+    allow_flagging="never"  # Disable flagging
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    iface.launch()

food_identification.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Identified food: Granny Smith\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from PIL import Image\n",
+    "from transformers import ViTFeatureExtractor, ViTForImageClassification\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "model_name = \"google/vit-base-patch16-224\"\n",
+    "feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)\n",
+    "model = ViTForImageClassification.from_pretrained(model_name)\n",
+    "\n",
+    "def identify_food(image_path):\n",
+    "    \"\"\"Identify the food item in the image.\"\"\"\n",
+    "    image = Image.open(image_path)\n",
+    "    inputs = feature_extractor(images=image, return_tensors=\"pt\")\n",
+    "    outputs = model(**inputs)\n",
+    "    logits = outputs.logits\n",
+    "    predicted_class_idx = logits.argmax(-1).item()\n",
+    "    predicted_label = model.config.id2label[predicted_class_idx]\n",
+    "    food_name = predicted_label.split(',')[0]\n",
+    "    return food_name\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "myenv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

predict_food.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'>' not supported between instances of 'str' and 'int'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[5], line 56\u001b[0m\n\u001b[0;32m     54\u001b[0m test_food \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGranny Smith apple\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     55\u001b[0m nutrition_info \u001b[38;5;241m=\u001b[39m get_nutrition_info(test_food)\n\u001b[1;32m---> 56\u001b[0m analysis \u001b[38;5;241m=\u001b[39m \u001b[43manalyze_nutrition\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnutrition_info\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     57\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAnalysis for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtest_food\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     58\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mNutritional Data:\u001b[39m\u001b[38;5;124m\"\u001b[39m, analysis[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnutrition_data\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
+      "Cell \u001b[1;32mIn[5], line 34\u001b[0m, in \u001b[0;36manalyze_nutrition\u001b[1;34m(nutrition_info)\u001b[0m\n\u001b[0;32m     31\u001b[0m suggestions \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m     33\u001b[0m \u001b[38;5;66;03m# Analyze nutritional values and provide suggestions\u001b[39;00m\n\u001b[1;32m---> 34\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mcalories\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m300\u001b[39;49m:\n\u001b[0;32m     35\u001b[0m     suggestions\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHigh in calories - Consider portion control\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     36\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fat_total_g \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m10\u001b[39m:\n",
+      "\u001b[1;31mTypeError\u001b[0m: '>' not supported between instances of 'str' and 'int'"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "\n",
+    "api_key = 'jVjdskzcX1Fv3o5OFJPxbw==EHXvm2HiyPYoOm2p'\n",
+    "\n",
+    "def get_nutrition_info(food_name):\n",
+    "    \"\"\"Get the nutritional information for the identified food item.\"\"\"\n",
+    "    api_url = f'https://api.api-ninjas.com/v1/nutrition?query={food_name}'\n",
+    "    response = requests.get(api_url, headers={'X-Api-Key': api_key})\n",
+    "    if response.status_code == requests.codes.ok:\n",
+    "        return response.json()\n",
+    "    else:\n",
+    "        return {\"Error\": response.status_code, \"Message\": response.text}\n",
+    "\n",
+    "def analyze_nutrition(nutrition_info):\n",
+    "    \"\"\"Analyze the nutritional information and provide suggestions.\"\"\"\n",
+    "    if \"Error\" in nutrition_info:\n",
+    "        return f\"Error: {nutrition_info['Error']} - {nutrition_info['Message']}\"\n",
+    "\n",
+    "    if len(nutrition_info) == 0:\n",
+    "        return \"No nutritional information found for the given food item.\"\n",
+    "\n",
+    "    nutrition_data = nutrition_info[0]\n",
+    "    suggestions = []\n",
+    "\n",
+    "    if nutrition_data['calories'] > 500:\n",
+    "        suggestions.append(\"This food is high in calories. Consider portion control.\")\n",
+    "    if nutrition_data['fat_total_g'] > 20:\n",
+    "        suggestions.append(\"High in fat. Consider low-fat alternatives.\")\n",
+    "    if nutrition_data['sugar_g'] > 20:\n",
+    "        suggestions.append(\"High in sugar. Consider limiting sugar intake.\")\n",
+    "\n",
+    "    return {\n",
+    "        \"nutrition_data\": nutrition_data,\n",
+    "        \"suggestions\": suggestions\n",
+    "    }\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "myenv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

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+gradio>=3.39
+transformers>=4.34.0
+torch>=2.1.0
+torchvision>=0.16.0
+requests>=2.31.0
+python-dotenv>=1.0.0