diff --git "a/EDA_AppLegacy.ipynb" "b/EDA_AppLegacy.ipynb" new file mode 100644--- /dev/null +++ "b/EDA_AppLegacy.ipynb" @@ -0,0 +1,2828 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Part 2: Dataset & EDA" + ], + "metadata": { + "id": "2iF7EiHwhN3o" + } + }, + { + "cell_type": "markdown", + "source": [ + "Data Validation & Cleaning:" + ], + "metadata": { + "id": "TYN31hM92m1t" + } + }, + { + "cell_type": "code", + "source": [ + "from google.colab import files\n", + "import os\n", + "\n", + "# 1. Upload the file\n", + "print(\"Please select the file to upload:\")\n", + "uploaded = files.upload()\n", + "\n", + "# 2. Confirm the upload and print the filename\n", + "for filename in uploaded.keys():\n", + " print(f'User uploaded file \"{filename}\" with length {len(uploaded[filename])} bytes')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 105 + }, + "id": "JLMH-COfhzmE", + "outputId": "72721d89-19e7-44a9-f2c5-046812532aa2" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Please select the file to upload:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " \n", + " Upload widget is only available when the cell has been executed in the\n", + " current browser session. Please rerun this cell to enable.\n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Saving RecipeData_10K.csv to RecipeData_10K (2).csv\n", + "User uploaded file \"RecipeData_10K (2).csv\" with length 9583974 bytes\n" + ] + } + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "Wmoru9w-hAC8" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import collections\n", + "import re\n", + "\n", + "# Load the dataset (Make sure the filename matches what you just generated)\n", + "df = pd.read_csv(\"RecipeData_10K.csv\")\n" + ] + }, + { + "cell_type": "code", + "source": [ + "# 1. View the first 5 rows\n", + "print(\"--- HEAD (First 5 Rows) ---\")\n", + "display(df.head())\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 224 + }, + "id": "r9Pqpl3LhgI_", + "outputId": "97ec94c6-6c63-4f08-aec6-71d8a89d59e7" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--- HEAD (First 5 Rows) ---\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " Title \\\n", + "0 Zesty Mediterranean Lamb Chops Platter \n", + "1 Szechuan Asian_Fusion Tofu with Cashews \n", + "2 Zesty Mediterranean Hummus Plate Medley \n", + "3 Tuscan Italian Ravioli with Mushrooms \n", + "4 Lemon Mediterranean Shawarma with Yogurt Sauce \n", + "\n", + " Ingredients \\\n", + "0 - Lamb Chops 6\\n- Lemon (freshly squeezed) 1\\n... \n", + "1 - Tofu 500g\\n- Cashews 100g\\n- Soy Sauce 3 tbs... \n", + "2 - Chickpeas 500g\\n- Olive Oil 2 tbsp\\n- Lemon ... \n", + "3 - Flour 500g\\n- Eggs 3\\n- Fillings: ricotta ch... \n", + "4 - Chicken or lamb (1kg)\\n- Olive oil\\n- Lemon ... \n", + "\n", + " Instructions \\\n", + "0 Place lamb chops in a shallow dish, drizzle wi... \n", + "1 Combine all ingredients except green onions in... \n", + "2 Place all the ingredients in a food processor,... \n", + "3 In a bowl mix flour with eggs and water slowly... \n", + "4 Mix lemon juice, minced garlic, red onion, ore... \n", + "\n", + " Raw_Output \n", + "0 For Zesty Mediterranean Lamb Chops Platter\\nIn... \n", + "1 Title: Szechuan Asian_Fusion Tofu with Cashews... \n", + "2 Title: Zesty Mediterranean Hummus Plate Medley... \n", + "3 For Tuscan Italian Ravioli with Mushrooms\\nIng... \n", + "4 For:\\nTitle: Lemon Mediterranean Shawarma with... " + ], + "text/html": [ + "\n", + "
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TitleIngredientsInstructionsRaw_Output
0Zesty Mediterranean Lamb Chops Platter- Lamb Chops 6\\n- Lemon (freshly squeezed) 1\\n...Place lamb chops in a shallow dish, drizzle wi...For Zesty Mediterranean Lamb Chops Platter\\nIn...
1Szechuan Asian_Fusion Tofu with Cashews- Tofu 500g\\n- Cashews 100g\\n- Soy Sauce 3 tbs...Combine all ingredients except green onions in...Title: Szechuan Asian_Fusion Tofu with Cashews...
2Zesty Mediterranean Hummus Plate Medley- Chickpeas 500g\\n- Olive Oil 2 tbsp\\n- Lemon ...Place all the ingredients in a food processor,...Title: Zesty Mediterranean Hummus Plate Medley...
3Tuscan Italian Ravioli with Mushrooms- Flour 500g\\n- Eggs 3\\n- Fillings: ricotta ch...In a bowl mix flour with eggs and water slowly...For Tuscan Italian Ravioli with Mushrooms\\nIng...
4Lemon Mediterranean Shawarma with Yogurt Sauce- Chicken or lamb (1kg)\\n- Olive oil\\n- Lemon ...Mix lemon juice, minced garlic, red onion, ore...For:\\nTitle: Lemon Mediterranean Shawarma with...
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Garnish with green onions before serving.\",\n \"Mix lemon juice, minced garlic, red onion, oregano, salt, and pepper in a large bowl. Add the meat, and gently toss to coat. Marinate for at least 30 minutes. Cook shawarma in\",\n \"Place all the ingredients in a food processor, blend until smooth and creamy, then season to taste. Serve with pita bread, sliced cucumber, and cherry tomatoes for dipping.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Raw_Output\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"Title: Szechuan Asian_Fusion Tofu with Cashews\\nIngredients:\\n- Tofu 500g\\n- Cashews 100g\\n- Soy Sauce 3 tbsp\\n- Sesame Oil 1 tbsp\\n- Red Chili Flakes 1 tsp\\n- Ginger (a few slices)\\n- Garlic (3 cloves)\\n- Rice Wine 1 tbsp\\n- Green Onions (for garnish)\\nInstructions:\\nCombine all ingredients except green onions in a pan, bring to a simmer and cook for about 20 minutes until the tofu is soft. Garnish with green onions before serving.\",\n \"For:\\nTitle: Lemon Mediterranean Shawarma with Yogurt Sauce\\nIngredients:\\n- Chicken or lamb (1kg)\\n- Olive oil\\n- Lemon 2\\n- Garlic 3 cloves\\n- Red onion 1\\n- Pita bread 6\\n- Tzatziki sauce 50ml (for serving)\\n- Cilantro\\n- Oregano\\n- Salt and pepper\\n- Feta cheese (optional, for serving)\\nInstructions:\\nMix lemon juice, minced garlic, red onion, oregano, salt, and pepper in a large bowl. Add the meat, and gently toss to coat. Marinate for at least 30 minutes. Cook shawarma in\",\n \"Title: Zesty Mediterranean Hummus Plate Medley\\n Ingredients:\\n- Chickpeas 500g\\n- Olive Oil 2 tbsp\\n- Lemon Juice 1 lemon\\n- Garlic 3 cloves\\n- Cilantro 1 cup\\n- Red Onion 1/2\\n- Tahini 2 tbsp\\n- Paprika\\n- Oregano\\n- Sumac\\n- Salt\\n- Fresh Basil\\nInstructions:\\nPlace all the ingredients in a food processor, blend until smooth and creamy, then season to taste. Serve with pita bread, sliced cucumber, and cherry tomatoes for dipping.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "df.info()\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X9MbpR2ZiLAv", + "outputId": "6dab7a6b-72e6-4649-8365-74379406ff81" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "RangeIndex: 10000 entries, 0 to 9999\n", + "Data columns (total 4 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Title 10000 non-null object\n", + " 1 Ingredients 10000 non-null object\n", + " 2 Instructions 9996 non-null object\n", + " 3 Raw_Output 10000 non-null object\n", + "dtypes: object(4)\n", + "memory usage: 312.6+ KB\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Option 1: Set column width to show up to 500 characters\n", + "pd.set_option('display.max_colwidth', 500)\n", + "\n", + "# Find rows where 'Instructions' is null or empty\n", + "empty_instructions = df[df['Instructions'].isnull() | (df['Instructions'] == '')]\n", + "\n", + "# Now display the empty rows again\n", + "print(\"--- RAW OUTPUT FOR EMPTY INSTRUCTIONS ---\")\n", + "display(empty_instructions[['Title', 'Raw_Output']])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 296 + }, + "id": "TQqcedo0nztq", + "outputId": "e138490c-5d6d-4d53-a316-7685e8b8de40" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--- RAW OUTPUT FOR EMPTY INSTRUCTIONS ---\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " Title \\\n", + "566 Zesty Mediterranean Shakshuka with Yogurt Sauce \n", + "3408 Zesty Mediterranean Hummus Plate Bowl \n", + "8269 Szechuan Asian Fusion Beef with Cashews \n", + "8716 Ginger Asian_Fusion Rice Bowl with Cashews \n", + "\n", + " Raw_Output \n", + "566 For Zesty Mediterranean Shakshuka with Yogurt Sauce\\nIngredients:\\n- Eggs 6\\n- Tomatoes 1kg, chopped\\n- Onion 1, diced\\n- Bell peppers 2, diced\\n- Garlic 3 cloves, minced\\n- Capsicum (jalapeño or banana) 1, seeded and finely chopped\\n- Pimiento (aubergine) 1, diced\\n- Tomatoes paste 2 tbsp\\n- Olive oil 2 tbsp\\n- Paprika 1 tsp\\n- Sumac 1 tsp\\n- Cumin 1 tsp\\n- Salt and pepper to taste\\n- Fresh parsley 2 tbsp, chopped\\n- Plain yogurt 1/2 cup\\n- Lemon wedges\\nInstructions: \n", + "3408 - Chickpeas 500g\\n- Lemon 1\\n- Garlic 3 cloves\\n- Cilantro (fresh) 2 tbsp\\n- Tahini 2 tbsp\\n- Olive oil 2 tbsp\\n- Water or lemon juice as needed\\n- Salt and pepper to taste\\n- Vegetables: cherry tomatoes, cucumber, red onion, bell peppers, olives\\n- Croutons\\n- Feta cheese\\nTitle: Zesty Mediterranean Hummus Plate Bowl\\nIngredients:\\n- Hummus 2 cups\\n- Vegetables: cherry tomatoes, cucumber, red onion, bell peppers, olives 1 cup mixed\\n- Croutons 3 oz\\n- Feta cheese 1/2 cup\\nInstructions: \n", + "8269 For those seeking an exciting blend of flavors in their meals, here’s the recipe for Szechuan Asian Fusion Beef with Cashews.\\n\\nTitle: Szechuan Asian Fusion Beef with Cashews\\n\\nIngredients:\\n- Beef 500g\\n- Noodles 200g\\n- Cashews 100g\\n- Lemongrass 3 stalks\\n- Garlic (6 cloves)\\n- Ginger (3 slices)\\n- Scallions (to taste)\\n- Soy Sauce 1/4 cup\\n- Sesame Oil 2 tbsp\\n- Spices: Sichuan peppercorns, coriander seeds\\n- Rice Vinegar\\n- Salt to taste\\n- Sesame Seeds for garnish\\n\\nInstructions: \n", + "8716 Title: Ginger Asian_Fusion Rice Bowl with Cashews\\n Ingredients:\\n - Basmati Rice 500g\\n - Chicken 200g\\n - Carrots 1\\n - Broccoli 100g\\n - Green Beans 100g\\n - Soy Sauce 2 tbsp\\n - Oyster Sauce 1 tbsp\\n - Shredded Ginger 1 tsp\\n - Sesame Oil 1 tsp\\n - Cashews 50g\\n - Green Onions (chopped) 2\\n - Rice Vinegar 1 tsp\\n - Salt to taste\\n - Saffron strands (for garnish)\\n Instructions: " + ], + "text/html": [ + "\n", + "
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TitleRaw_Output
566Zesty Mediterranean Shakshuka with Yogurt SauceFor Zesty Mediterranean Shakshuka with Yogurt Sauce\\nIngredients:\\n- Eggs 6\\n- Tomatoes 1kg, chopped\\n- Onion 1, diced\\n- Bell peppers 2, diced\\n- Garlic 3 cloves, minced\\n- Capsicum (jalapeño or banana) 1, seeded and finely chopped\\n- Pimiento (aubergine) 1, diced\\n- Tomatoes paste 2 tbsp\\n- Olive oil 2 tbsp\\n- Paprika 1 tsp\\n- Sumac 1 tsp\\n- Cumin 1 tsp\\n- Salt and pepper to taste\\n- Fresh parsley 2 tbsp, chopped\\n- Plain yogurt 1/2 cup\\n- Lemon wedges\\nInstructions:
3408Zesty Mediterranean Hummus Plate Bowl- Chickpeas 500g\\n- Lemon 1\\n- Garlic 3 cloves\\n- Cilantro (fresh) 2 tbsp\\n- Tahini 2 tbsp\\n- Olive oil 2 tbsp\\n- Water or lemon juice as needed\\n- Salt and pepper to taste\\n- Vegetables: cherry tomatoes, cucumber, red onion, bell peppers, olives\\n- Croutons\\n- Feta cheese\\nTitle: Zesty Mediterranean Hummus Plate Bowl\\nIngredients:\\n- Hummus 2 cups\\n- Vegetables: cherry tomatoes, cucumber, red onion, bell peppers, olives 1 cup mixed\\n- Croutons 3 oz\\n- Feta cheese 1/2 cup\\nInstructions:
8269Szechuan Asian Fusion Beef with CashewsFor those seeking an exciting blend of flavors in their meals, here’s the recipe for Szechuan Asian Fusion Beef with Cashews.\\n\\nTitle: Szechuan Asian Fusion Beef with Cashews\\n\\nIngredients:\\n- Beef 500g\\n- Noodles 200g\\n- Cashews 100g\\n- Lemongrass 3 stalks\\n- Garlic (6 cloves)\\n- Ginger (3 slices)\\n- Scallions (to taste)\\n- Soy Sauce 1/4 cup\\n- Sesame Oil 2 tbsp\\n- Spices: Sichuan peppercorns, coriander seeds\\n- Rice Vinegar\\n- Salt to taste\\n- Sesame Seeds for garnish\\n\\nInstructions:
8716Ginger Asian_Fusion Rice Bowl with CashewsTitle: Ginger Asian_Fusion Rice Bowl with Cashews\\n Ingredients:\\n - Basmati Rice 500g\\n - Chicken 200g\\n - Carrots 1\\n - Broccoli 100g\\n - Green Beans 100g\\n - Soy Sauce 2 tbsp\\n - Oyster Sauce 1 tbsp\\n - Shredded Ginger 1 tsp\\n - Sesame Oil 1 tsp\\n - Cashews 50g\\n - Green Onions (chopped) 2\\n - Rice Vinegar 1 tsp\\n - Salt to taste\\n - Saffron strands (for garnish)\\n Instructions:
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "summary": "{\n \"name\": \"display(empty_instructions[['Title', 'Raw_Output']])\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"Title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Zesty Mediterranean Hummus Plate Bowl\",\n \"Ginger Asian_Fusion Rice Bowl with Cashews\",\n \"Zesty Mediterranean Shakshuka with Yogurt Sauce\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Raw_Output\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"- Chickpeas 500g\\n- Lemon 1\\n- Garlic 3 cloves\\n- Cilantro (fresh) 2 tbsp\\n- Tahini 2 tbsp\\n- Olive oil 2 tbsp\\n- Water or lemon juice as needed\\n- Salt and pepper to taste\\n- Vegetables: cherry tomatoes, cucumber, red onion, bell peppers, olives\\n- Croutons\\n- Feta cheese\\nTitle: Zesty Mediterranean Hummus Plate Bowl\\nIngredients:\\n- Hummus 2 cups\\n- Vegetables: cherry tomatoes, cucumber, red onion, bell peppers, olives 1 cup mixed\\n- Croutons 3 oz\\n- Feta cheese 1/2 cup\\nInstructions:\",\n \"Title: Ginger Asian_Fusion Rice Bowl with Cashews\\n Ingredients:\\n - Basmati Rice 500g\\n - Chicken 200g\\n - Carrots 1\\n - Broccoli 100g\\n - Green Beans 100g\\n - Soy Sauce 2 tbsp\\n - Oyster Sauce 1 tbsp\\n - Shredded Ginger 1 tsp\\n - Sesame Oil 1 tsp\\n - Cashews 50g\\n - Green Onions (chopped) 2\\n - Rice Vinegar 1 tsp\\n - Salt to taste\\n - Saffron strands (for garnish)\\n Instructions:\",\n \"For Zesty Mediterranean Shakshuka with Yogurt Sauce\\nIngredients:\\n- Eggs 6\\n- Tomatoes 1kg, chopped\\n- Onion 1, diced\\n- Bell peppers 2, diced\\n- Garlic 3 cloves, minced\\n- Capsicum (jalape\\u00f1o or banana) 1, seeded and finely chopped\\n- Pimiento (aubergine) 1, diced\\n- Tomatoes paste 2 tbsp\\n- Olive oil 2 tbsp\\n- Paprika 1 tsp\\n- Sumac 1 tsp\\n- Cumin 1 tsp\\n- Salt and pepper to taste\\n- Fresh parsley 2 tbsp, chopped\\n- Plain yogurt 1/2 cup\\n- Lemon wedges\\nInstructions:\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "4 coulmns has \"Instructions\" coulmn empty. The paser that created the coulmns \"Title\", \"Ingridients\", \"Instructions\", didn't find \"instructions\" on these 4 recipes. It is indeed an outstanding behaviour of the data, but it is not a bug as some recipes on reaility might not have detailed cooking insturcions." + ], + "metadata": { + "id": "S4EoGJfKoeeV" + } + }, + { + "cell_type": "code", + "source": [ + "print(f\"\\nDuplicate Rows: {df.duplicated().sum()}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3H6gxxw0iMOQ", + "outputId": "670af8c7-29ea-446b-b8b3-2579fcb9a422" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Duplicate Rows: 0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 1. Total number of recipes with duplicate titles\n", + "duplicate_count = df['Title'].duplicated().sum()\n", + "print(f\"Total duplicate titles: {duplicate_count}\")\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MN_5Z75_iV8g", + "outputId": "6e1354cd-321c-4888-d979-3c1e774fb82f" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Total duplicate titles: 6932\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The dataset contains 6,932 duplicate titles (out of 10,000 entries), yet the duplicate row count is 0. This indicates that while many recipes share the same name (e.g., \"Rustic Italian Pasta\"), the actual content (Ingredients and Instructions) is unique for every single entry." + ], + "metadata": { + "id": "mI0X0QTOkGnZ" + } + }, + { + "cell_type": "code", + "source": [ + "# Change the label to match the code\n", + "print(\"\\n--- Top 20 Repeated Titles ---\")\n", + "print(df['Title'].value_counts().head(20))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wNgvgdDSiljP", + "outputId": "e289fb44-faa6-4490-93f8-b33d31ff17d1" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "--- Top 20 Repeated Titles ---\n", + "Title\n", + "Glazed Dessert Muffins Crumble 12\n", + "Soy-Glazed Asian_Fusion Prawns with Cashews 12\n", + "Fluffy Dessert Cookies Supreme 12\n", + "Glazed Dessert Brownies Dream 11\n", + "Homemade Dessert Cookies Crumble 11\n", + "Homemade Dessert Pudding Supreme 10\n", + "Zesty Mediterranean Hummus Plate Feast 10\n", + "Roasted Mediterranean Couscous Bowl 10\n", + "Cinnamon Dessert Cheesecake Bites 10\n", + "Fresh Mediterranean Couscous Stew 10\n", + "Wok-Fried Asian_Fusion Prawns Surprise 10\n", + "Chocolate Dessert Muffins Bites 10\n", + "Chocolate Dessert Rugelach Crumble 10\n", + "Soy-Glazed Asian_Fusion Beef with Cashews 10\n", + "Szechuan Asian_Fusion Noodles with Cashews 9\n", + "Fluffy Dessert Tart Crumble 9\n", + "Vanilla Dessert Rugelach Crumble 9\n", + "Cinnamon Dessert Pudding Swirl 9\n", + "Crispy Asian_Fusion Noodles Surprise 9\n", + "Wok-Fried Asian_Fusion Duck Dragon Style 9\n", + "Name: count, dtype: int64\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "We don't have one massive duplicate (like \"Pasta\" appearing 5,000 times). Instead, our duplicates are spread out very evenly across thousands of different recipes. This confirms that our data generator worked perfectly. it distributed the prompts widely rather than getting stuck on just one dish." + ], + "metadata": { + "id": "cF8bZj58lCn6" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "duplicates_df = df[df.duplicated(subset=['Title'], keep=False)].sort_values('Title')\n", + "\n", + "print(\"\\n--- Preview of Duplicate Rows ---\")\n", + "display(duplicates_df[['Title', 'Ingredients']].head(10))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 398 + }, + "id": "R0JwCjO-inZx", + "outputId": "eab83943-58bc-4f73-ec9f-90ff153e6d66" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "--- Preview of Duplicate Rows ---\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " Title \\\n", + "5416 Baked Italian Alfredo Al Forno \n", + "7776 Baked Italian Alfredo Al Forno \n", + "3776 Baked Italian Alfredo Al Forno \n", + "6607 Baked Italian Alfredo Delight \n", + "6731 Baked Italian Alfredo Delight \n", + "5957 Baked Italian Alfredo Delight \n", + "6581 Baked Italian Alfredo Delight \n", + "3535 Baked Italian Alfredo Rustica \n", + "8882 Baked Italian Alfredo Rustica \n", + "5497 Baked Italian Alfredo Special \n", + "\n", + " Ingredients \n", + "5416 - Pasta 300g\\n- Alfredo Sauce 500ml\\n- Mozzarella cheese 200g grated\\n- Parmesan cheese 50g grated\\n- Fresh basil leaves (optional) \n", + "7776 - Pasta 500g\\n- Cream 300ml\\n- Parmesan cheese 150g\\n- Butter 100g\\n- Garlic cloves (2)\\n- Black pepper\\n- Salt\\n- Fresh parsley \n", + "3776 - Pasta 200g\\n- Cheese Mozzarella 200g\\n- Butter 150g\\n- Milk 500ml\\n- Garlic 3 cloves\\n- Basil 1 tbsp \n", + "6607 - Penne Pasta 300g\\n- Milk 500ml\\n- Butter 100g\\n- Parmesan Cheese 150g\\n- Garlic Powder 1 tsp\\n- Fresh Basil 2 sprigs\\n- Salt and Pepper to taste \n", + "6731 - Pasta (fusilli or spaghetti) 400g\\n- Heavy cream 300ml\\n- Parmesan cheese 100g\\n- Garlic cloves (2)\\n- Chopped basil (1/4 cup)\\n- Salt to taste\\n- Black pepper to taste \n", + "5957 - Milk 1L\\n- Butter 200g\\n- Flour 250g\\n- Parmesan Cheese 500g\\n- Egg (1)\\n- Garlic (2 cloves minced)\\n- Salt (to taste)\\n- Pepper (to taste)\\n- Fresh Basil (for garnish) \n", + "6581 - Pasta 400g\\n- Alfredo Sauce 400ml\\n- Parmesan Cheese 50g\\n- Garlic Cloves (2)\\n- Fresh Basil 2 tsp \n", + "3535 - Pasta 200g\\n- Cheese Mozzarella 200g\\n- Butter 150g\\n- Milk 300ml\\n- Egg 1\\n- Garlic 3 cloves\\n- Parsley\\n- Pepper\\n- Salt \n", + "8882 - Pasta (e.g. fusilli) 500g\\n- Heavy Cream 300ml\\n- Parmesan Cheese 150g\\n- Garlic 3 cloves\\n- Eggs 2\\n- Onion 1 large\\n- Basil Leaves 1 handful\\n- Pepper to taste \n", + "5497 - Cream 1L\\n- Pasta 500g\\n- Butter 100g\\n- Parmesan Cheese 200g\\n- Garlic 4\\n- Spinach 2 cups\\n- Salt and Pepper to taste\\n- Fresh Basil " + ], + "text/html": [ + "\n", + "
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TitleIngredients
5416Baked Italian Alfredo Al Forno- Pasta 300g\\n- Alfredo Sauce 500ml\\n- Mozzarella cheese 200g grated\\n- Parmesan cheese 50g grated\\n- Fresh basil leaves (optional)
7776Baked Italian Alfredo Al Forno- Pasta 500g\\n- Cream 300ml\\n- Parmesan cheese 150g\\n- Butter 100g\\n- Garlic cloves (2)\\n- Black pepper\\n- Salt\\n- Fresh parsley
3776Baked Italian Alfredo Al Forno- Pasta 200g\\n- Cheese Mozzarella 200g\\n- Butter 150g\\n- Milk 500ml\\n- Garlic 3 cloves\\n- Basil 1 tbsp
6607Baked Italian Alfredo Delight- Penne Pasta 300g\\n- Milk 500ml\\n- Butter 100g\\n- Parmesan Cheese 150g\\n- Garlic Powder 1 tsp\\n- Fresh Basil 2 sprigs\\n- Salt and Pepper to taste
6731Baked Italian Alfredo Delight- Pasta (fusilli or spaghetti) 400g\\n- Heavy cream 300ml\\n- Parmesan cheese 100g\\n- Garlic cloves (2)\\n- Chopped basil (1/4 cup)\\n- Salt to taste\\n- Black pepper to taste
5957Baked Italian Alfredo Delight- Milk 1L\\n- Butter 200g\\n- Flour 250g\\n- Parmesan Cheese 500g\\n- Egg (1)\\n- Garlic (2 cloves minced)\\n- Salt (to taste)\\n- Pepper (to taste)\\n- Fresh Basil (for garnish)
6581Baked Italian Alfredo Delight- Pasta 400g\\n- Alfredo Sauce 400ml\\n- Parmesan Cheese 50g\\n- Garlic Cloves (2)\\n- Fresh Basil 2 tsp
3535Baked Italian Alfredo Rustica- Pasta 200g\\n- Cheese Mozzarella 200g\\n- Butter 150g\\n- Milk 300ml\\n- Egg 1\\n- Garlic 3 cloves\\n- Parsley\\n- Pepper\\n- Salt
8882Baked Italian Alfredo Rustica- Pasta (e.g. fusilli) 500g\\n- Heavy Cream 300ml\\n- Parmesan Cheese 150g\\n- Garlic 3 cloves\\n- Eggs 2\\n- Onion 1 large\\n- Basil Leaves 1 handful\\n- Pepper to taste
5497Baked Italian Alfredo Special- Cream 1L\\n- Pasta 500g\\n- Butter 100g\\n- Parmesan Cheese 200g\\n- Garlic 4\\n- Spinach 2 cups\\n- Salt and Pepper to taste\\n- Fresh Basil
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "summary": "{\n \"name\": \"display(duplicates_df[['Title', 'Ingredients']]\",\n \"rows\": 10,\n \"fields\": [\n {\n \"column\": \"Title\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Baked Italian Alfredo Delight\",\n \"Baked Italian Alfredo Special\",\n \"Baked Italian Alfredo Al Forno\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Ingredients\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"- Pasta (e.g. fusilli) 500g\\n- Heavy Cream 300ml\\n- Parmesan Cheese 150g\\n- Garlic 3 cloves\\n- Eggs 2\\n- Onion 1 large\\n- Basil Leaves 1 handful\\n- Pepper to taste\",\n \"- Pasta 500g\\n- Cream 300ml\\n- Parmesan cheese 150g\\n- Butter 100g\\n- Garlic cloves (2)\\n- Black pepper\\n- Salt\\n- Fresh parsley\",\n \"- Milk 1L\\n- Butter 200g\\n- Flour 250g\\n- Parmesan Cheese 500g\\n- Egg (1)\\n- Garlic (2 cloves minced)\\n- Salt (to taste)\\n- Pepper (to taste)\\n- Fresh Basil (for garnish)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "some recipes might share the same title, but their ingridients are diffrent.\n" + ], + "metadata": { + "id": "4sQFP6MFleLG" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Word Count:" + ], + "metadata": { + "id": "Sy_1XjrW0sWn" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "# 1. Calculate Word Count (if not already done)\n", + "df['Raw_Word_Count'] = df['Raw_Output'].astype(str).apply(lambda x: len(x.split()))\n", + "\n", + "# 2. Filter and Count\n", + "under_40 = df[df['Raw_Word_Count'] < 40]\n", + "under_20 = df[df['Raw_Word_Count'] < 20]\n", + "\n", + "print(f\"Recipes with less than 40 words: {len(under_40)}\")\n", + "print(f\"Recipes with less than 20 words: {len(under_20)}\")\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "peGDWUEa0rUS", + "outputId": "ab56d76d-0a71-4e9d-9001-40c40281439e" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Recipes with less than 40 words: 20\n", + "Recipes with less than 20 words: 5\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 3. Inspect the very short ones (Quality Check)\n", + "print(\"\\n--- Preview of Recipes under 20 words ---\")\n", + "if len(under_20) > 0:\n", + " pd.set_option('display.max_colwidth', None)\n", + " display(under_20[['Title', 'Raw_Output', 'Raw_Word_Count']])\n", + " pd.reset_option('display.max_colwidth')\n", + "else:\n", + " print(\"None found! Your data is very clean.\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 241 + }, + "id": "26A-PD7Q03oV", + "outputId": "9bdb0995-e0dc-4843-c668-d8a07bde22e6" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "--- Preview of Recipes under 20 words ---\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " Title Raw_Output \\\n", + "1344 Rustic Italian Ziti Special (INVALID INPUT PROVIDED) \n", + "3933 Sweet Dessert Muffins Dream (RECIPE_NOT_PROVIDED) \n", + "4376 Fluffy Dessert Cookies Supreme Helper Needed \n", + "4959 Dark Dessert Cheesecake Crumble (RECIPE_NOT_PROVIDED) \n", + "6706 Dark Dessert Pudding Dream (RECIPE_NOT_GENERATED) \n", + "\n", + " Raw_Word_Count \n", + "1344 3 \n", + "3933 1 \n", + "4376 2 \n", + "4959 1 \n", + "6706 1 " + ], + "text/html": [ + "\n", + "
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TitleRaw_OutputRaw_Word_Count
1344Rustic Italian Ziti Special(INVALID INPUT PROVIDED)3
3933Sweet Dessert Muffins Dream(RECIPE_NOT_PROVIDED)1
4376Fluffy Dessert Cookies SupremeHelper Needed2
4959Dark Dessert Cheesecake Crumble(RECIPE_NOT_PROVIDED)1
6706Dark Dessert Pudding Dream(RECIPE_NOT_GENERATED)1
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Your data is very clean\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"Title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"Sweet Dessert Muffins Dream\",\n \"Dark Dessert Pudding Dream\",\n \"Fluffy Dessert Cookies Supreme\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Raw_Output\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"(RECIPE_NOT_PROVIDED)\",\n \"(RECIPE_NOT_GENERATED)\",\n \"(INVALID INPUT PROVIDED)\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Raw_Word_Count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 1,\n \"max\": 3,\n \"num_unique_values\": 3,\n \"samples\": [\n 3,\n 1,\n 2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "as we can see in some recipes with less than 20 words, there isn't a real recipe." + ], + "metadata": { + "id": "5vjrY6oC03eh" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "# 2. Filter for recipes with word count between 20 and 40\n", + "df_short_range = df[(df['Raw_Word_Count'] >= 20) & (df['Raw_Word_Count'] <= 40)]\n", + "\n", + "# 3. Display the first 10 recipes found in this range\n", + "print(f\"Total recipes found in this range: {len(df_short_range)}\")\n", + "print(\"--- Displaying 10 samples (20-40 words) ---\")\n", + "\n", + "# Expand display to read full content\n", + "pd.set_option('display.max_colwidth', None)\n", + "\n", + "display(df_short_range[['Title', 'Ingredients', 'Instructions', 'Raw_Output', 'Raw_Word_Count']].head(10))\n", + "\n", + "# Reset display settings\n", + "pd.reset_option('display.max_colwidth')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 745 + }, + "id": "pPZVboYN1F-z", + "outputId": "06fecd16-422e-431f-edc0-9dc857632bf7" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Total recipes found in this range: 18\n", + "--- Displaying 10 samples (20-40 words) ---\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " Title \\\n", + "1439 Glazed Dessert Pudding Dream \n", + "1587 Glazed Dessert Brownies Dream \n", + "2032 Glazed Dessert Cake Dream \n", + "2281 Cinnamon Dessert Rugelach Swirl \n", + "2699 Soy-Glazed Asian_Fusion Tofu Box \n", + "3049 Glazed Dessert Rugelach Celebration \n", + "3530 Glazed Dessert Cake Swirl \n", + "4845 Glazed Dessert Pudding Swirl \n", + "6827 Glazed Dessert Pudding Bites \n", + "7464 Creamy Dessert Rugelach Crumble \n", + "\n", + " Ingredients \\\n", + "1439 - Pudding 2 cups\\n - Glaze (Confectioners sugar 2 cups, Milk 1/2 cup, Vanilla 1 tsp)\\n - Fruit 1 cup \n", + "1587 - Chocolate brownie mix 1kg\\n- Glazing ingredients: sugar, eggs, vanilla, water \n", + "2032 - Cake (as homemade or store-bought)\\n- Glaze: powdered sugar, water, vanilla extract \n", + "2281 - Rugelach 300g\\n- Cinnamon 1 tablespoon\\n- Glaze: powdered sugar, cream \n", + "2699 Parse Error \n", + "3049 - Rugelach (as many as desired)\\n- Glaze: powdered sugar, cream cheese, vanilla extract, milk \n", + "3530 - Cake base (2 pieces)\\n- Buttercream frosting\\n- Glaze: powdered sugar, milk, vanilla extract \n", + "4845 - Pudding 500g\\n- Glaze (sugar, water, lemon juice, vanilla extract)\\n- Fresh fruit \n", + "6827 - Pudding 1kg\\n- Glaze: powdered sugar, vanilla extract, water\\n- Candied Fruits (optional) \n", + "7464 - Rugelach 6\\n- Butter 150g (melted)\\n- Sugar 100g\\n- Eggs 2\\n- Flour 50g \n", + "\n", + " Instructions \\\n", + "1439 Mix pudding with glaze until smooth, top with fruit and serve. \n", + "1587 Mix together all the ingredients in a bowl until smooth, pour over cooled brownies and let it set before serving. \n", + "2032 Mix powdered sugar, water, and vanilla until smooth. Pour over the cake and serve immediately. \n", + "2281 Spread a little cinnamon over each rugelach, drizzle cream over, and top with powder sugar to make swirls. \n", + "2699 Mix Tofu 250g, soy sauce 3 tbsp, honey 1 tbsp, ginger 1 tsp, garlic 1 clove, red pepper flakes pinch.\\nForm into a box shape and wrap aluminum foil.\\nBake at 375°F for 35 minutes.\\nServe with steamed vegetables. \n", + "3049 Mix the ingredients for glaze until smooth, spread over cooled rugelahs, and serve immediately. \n", + "3530 Spread buttercream frosting on cake bases, swirl it with a knife, and drizzle glaze on top before serving. \n", + "4845 Mix pudding and glaze until smooth, add fresh fruit on top, let cool before serving. \n", + "6827 Mix pudding and glaze together until smooth, optionally add candied fruits for texture. Chill until set, then serve chilled. \n", + "7464 Mix everything together until crumbly, spread over the Rugelach, bake at 180 degrees for 20 minutes. \n", + "\n", + " Raw_Output \\\n", + "1439 Title: Glazed Dessert Pudding Dream\\n Ingredients:\\n - Pudding 2 cups\\n - Glaze (Confectioners sugar 2 cups, Milk 1/2 cup, Vanilla 1 tsp)\\n - Fruit 1 cup\\n Instructions:\\n Mix pudding with glaze until smooth, top with fruit and serve. \n", + "1587 Title: Glazed Dessert Brownies Dream\\nIngredients:\\n- Chocolate brownie mix 1kg\\n- Glazing ingredients: sugar, eggs, vanilla, water\\nInstructions:\\nMix together all the ingredients in a bowl until smooth, pour over cooled brownies and let it set before serving. \n", + "2032 Title: Glazed Dessert Cake Dream\\nIngredients:\\n- Cake (as homemade or store-bought)\\n- Glaze: powdered sugar, water, vanilla extract\\nInstructions:\\nMix powdered sugar, water, and vanilla until smooth. Pour over the cake and serve immediately. \n", + "2281 For Cinnamon Dessert Rugelach Swirl\\nIngredients:\\n- Rugelach 300g\\n- Cinnamon 1 tablespoon\\n- Glaze: powdered sugar, cream\\nInstructions:\\nSpread a little cinnamon over each rugelach, drizzle cream over, and top with powder sugar to make swirls. \n", + "2699 Mix Tofu 250g, soy sauce 3 tbsp, honey 1 tbsp, ginger 1 tsp, garlic 1 clove, red pepper flakes pinch.\\nForm into a box shape and wrap aluminum foil.\\nBake at 375°F for 35 minutes.\\nServe with steamed vegetables. \n", + "3049 Title: Glazed Dessert Rugelach Celebration\\n Ingredients:\\n- Rugelach (as many as desired)\\n- Glaze: powdered sugar, cream cheese, vanilla extract, milk\\nInstructions:\\nMix the ingredients for glaze until smooth, spread over cooled rugelahs, and serve immediately. \n", + "3530 Title: Glazed Dessert Cake Swirl\\nIngredients:\\n- Cake base (2 pieces)\\n- Buttercream frosting\\n- Glaze: powdered sugar, milk, vanilla extract\\nInstructions:\\nSpread buttercream frosting on cake bases, swirl it with a knife, and drizzle glaze on top before serving. \n", + "4845 Title: Glazed Dessert Pudding Swirl\\nIngredients:\\n- Pudding 500g\\n- Glaze (sugar, water, lemon juice, vanilla extract)\\n- Fresh fruit\\nInstructions:\\nMix pudding and glaze until smooth, add fresh fruit on top, let cool before serving. \n", + "6827 For Glazed Dessert Pudding Bites\\nIngredients:\\n- Pudding 1kg\\n- Glaze: powdered sugar, vanilla extract, water\\n- Candied Fruits (optional)\\nInstructions:\\nMix pudding and glaze together until smooth, optionally add candied fruits for texture. Chill until set, then serve chilled. \n", + "7464 Title: Creamy Dessert Rugelach Crumble\\nIngredients:\\n- Rugelach 6\\n- Butter 150g (melted)\\n- Sugar 100g\\n- Eggs 2\\n- Flour 50g\\nInstructions:\\nMix everything together until crumbly, spread over the Rugelach, bake at 180 degrees for 20 minutes. \n", + "\n", + " Raw_Word_Count \n", + "1439 38 \n", + "1587 39 \n", + "2032 35 \n", + "2281 37 \n", + "2699 39 \n", + "3049 36 \n", + "3530 40 \n", + "4845 36 \n", + "6827 40 \n", + "7464 39 " + ], + "text/html": [ + "\n", + "
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TitleIngredientsInstructionsRaw_OutputRaw_Word_Count
1439Glazed Dessert Pudding Dream- Pudding 2 cups\\n - Glaze (Confectioners sugar 2 cups, Milk 1/2 cup, Vanilla 1 tsp)\\n - Fruit 1 cupMix pudding with glaze until smooth, top with fruit and serve.Title: Glazed Dessert Pudding Dream\\n Ingredients:\\n - Pudding 2 cups\\n - Glaze (Confectioners sugar 2 cups, Milk 1/2 cup, Vanilla 1 tsp)\\n - Fruit 1 cup\\n Instructions:\\n Mix pudding with glaze until smooth, top with fruit and serve.38
1587Glazed Dessert Brownies Dream- Chocolate brownie mix 1kg\\n- Glazing ingredients: sugar, eggs, vanilla, waterMix together all the ingredients in a bowl until smooth, pour over cooled brownies and let it set before serving.Title: Glazed Dessert Brownies Dream\\nIngredients:\\n- Chocolate brownie mix 1kg\\n- Glazing ingredients: sugar, eggs, vanilla, water\\nInstructions:\\nMix together all the ingredients in a bowl until smooth, pour over cooled brownies and let it set before serving.39
2032Glazed Dessert Cake Dream- Cake (as homemade or store-bought)\\n- Glaze: powdered sugar, water, vanilla extractMix powdered sugar, water, and vanilla until smooth. Pour over the cake and serve immediately.Title: Glazed Dessert Cake Dream\\nIngredients:\\n- Cake (as homemade or store-bought)\\n- Glaze: powdered sugar, water, vanilla extract\\nInstructions:\\nMix powdered sugar, water, and vanilla until smooth. Pour over the cake and serve immediately.35
2281Cinnamon Dessert Rugelach Swirl- Rugelach 300g\\n- Cinnamon 1 tablespoon\\n- Glaze: powdered sugar, creamSpread a little cinnamon over each rugelach, drizzle cream over, and top with powder sugar to make swirls.For Cinnamon Dessert Rugelach Swirl\\nIngredients:\\n- Rugelach 300g\\n- Cinnamon 1 tablespoon\\n- Glaze: powdered sugar, cream\\nInstructions:\\nSpread a little cinnamon over each rugelach, drizzle cream over, and top with powder sugar to make swirls.37
2699Soy-Glazed Asian_Fusion Tofu BoxParse ErrorMix Tofu 250g, soy sauce 3 tbsp, honey 1 tbsp, ginger 1 tsp, garlic 1 clove, red pepper flakes pinch.\\nForm into a box shape and wrap aluminum foil.\\nBake at 375°F for 35 minutes.\\nServe with steamed vegetables.Mix Tofu 250g, soy sauce 3 tbsp, honey 1 tbsp, ginger 1 tsp, garlic 1 clove, red pepper flakes pinch.\\nForm into a box shape and wrap aluminum foil.\\nBake at 375°F for 35 minutes.\\nServe with steamed vegetables.39
3049Glazed Dessert Rugelach Celebration- Rugelach (as many as desired)\\n- Glaze: powdered sugar, cream cheese, vanilla extract, milkMix the ingredients for glaze until smooth, spread over cooled rugelahs, and serve immediately.Title: Glazed Dessert Rugelach Celebration\\n Ingredients:\\n- Rugelach (as many as desired)\\n- Glaze: powdered sugar, cream cheese, vanilla extract, milk\\nInstructions:\\nMix the ingredients for glaze until smooth, spread over cooled rugelahs, and serve immediately.36
3530Glazed Dessert Cake Swirl- Cake base (2 pieces)\\n- Buttercream frosting\\n- Glaze: powdered sugar, milk, vanilla extractSpread buttercream frosting on cake bases, swirl it with a knife, and drizzle glaze on top before serving.Title: Glazed Dessert Cake Swirl\\nIngredients:\\n- Cake base (2 pieces)\\n- Buttercream frosting\\n- Glaze: powdered sugar, milk, vanilla extract\\nInstructions:\\nSpread buttercream frosting on cake bases, swirl it with a knife, and drizzle glaze on top before serving.40
4845Glazed Dessert Pudding Swirl- Pudding 500g\\n- Glaze (sugar, water, lemon juice, vanilla extract)\\n- Fresh fruitMix pudding and glaze until smooth, add fresh fruit on top, let cool before serving.Title: Glazed Dessert Pudding Swirl\\nIngredients:\\n- Pudding 500g\\n- Glaze (sugar, water, lemon juice, vanilla extract)\\n- Fresh fruit\\nInstructions:\\nMix pudding and glaze until smooth, add fresh fruit on top, let cool before serving.36
6827Glazed Dessert Pudding Bites- Pudding 1kg\\n- Glaze: powdered sugar, vanilla extract, water\\n- Candied Fruits (optional)Mix pudding and glaze together until smooth, optionally add candied fruits for texture. Chill until set, then serve chilled.For Glazed Dessert Pudding Bites\\nIngredients:\\n- Pudding 1kg\\n- Glaze: powdered sugar, vanilla extract, water\\n- Candied Fruits (optional)\\nInstructions:\\nMix pudding and glaze together until smooth, optionally add candied fruits for texture. Chill until set, then serve chilled.40
7464Creamy Dessert Rugelach Crumble- Rugelach 6\\n- Butter 150g (melted)\\n- Sugar 100g\\n- Eggs 2\\n- Flour 50gMix everything together until crumbly, spread over the Rugelach, bake at 180 degrees for 20 minutes.Title: Creamy Dessert Rugelach Crumble\\nIngredients:\\n- Rugelach 6\\n- Butter 150g (melted)\\n- Sugar 100g\\n- Eggs 2\\n- Flour 50g\\nInstructions:\\nMix everything together until crumbly, spread over the Rugelach, bake at 180 degrees for 20 minutes.39
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "summary": "{\n \"name\": \"pd\",\n \"rows\": 10,\n \"fields\": [\n {\n \"column\": \"Title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"Glazed Dessert Pudding Bites\",\n \"Glazed Dessert Brownies Dream\",\n \"Glazed Dessert Rugelach Celebration\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Ingredients\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"- Pudding 1kg\\n- Glaze: powdered sugar, vanilla extract, water\\n- Candied Fruits (optional)\",\n \"- Chocolate brownie mix 1kg\\n- Glazing ingredients: sugar, eggs, vanilla, water\",\n \"- Rugelach (as many as desired)\\n- Glaze: powdered sugar, cream cheese, vanilla extract, milk\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Instructions\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"Mix pudding and glaze together until smooth, optionally add candied fruits for texture. Chill until set, then serve chilled.\",\n \"Mix together all the ingredients in a bowl until smooth, pour over cooled brownies and let it set before serving.\",\n \"Mix the ingredients for glaze until smooth, spread over cooled rugelahs, and serve immediately.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Raw_Output\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 10,\n \"samples\": [\n \"For Glazed Dessert Pudding Bites\\nIngredients:\\n- Pudding 1kg\\n- Glaze: powdered sugar, vanilla extract, water\\n- Candied Fruits (optional)\\nInstructions:\\nMix pudding and glaze together until smooth, optionally add candied fruits for texture. Chill until set, then serve chilled.\",\n \"Title: Glazed Dessert Brownies Dream\\nIngredients:\\n- Chocolate brownie mix 1kg\\n- Glazing ingredients: sugar, eggs, vanilla, water\\nInstructions:\\nMix together all the ingredients in a bowl until smooth, pour over cooled brownies and let it set before serving.\",\n \"Title: Glazed Dessert Rugelach Celebration\\n Ingredients:\\n- Rugelach (as many as desired)\\n- Glaze: powdered sugar, cream cheese, vanilla extract, milk\\nInstructions:\\nMix the ingredients for glaze until smooth, spread over cooled rugelahs, and serve immediately.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Raw_Word_Count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 35,\n \"max\": 40,\n \"num_unique_values\": 6,\n \"samples\": [\n 38,\n 39,\n 40\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "it seems the issue is only on recipes with less than 20 words.we will remove these recipes:" + ], + "metadata": { + "id": "jMeluSi_1Jpi" + } + }, + { + "cell_type": "code", + "source": [ + "# Identify indices where word count is less than 20 and drop them\n", + "df.drop(df[df['Raw_Word_Count'] < 20].index, inplace=True)\n", + "\n", + "print(f\"Cleaning complete. Current rows: {len(df)}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "q-UyAk4a18p9", + "outputId": "a6b503d3-c235-4238-a95d-99995e0801ed" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cleaning complete. Current rows: 9995\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Checking the data after the cleaing:" + ], + "metadata": { + "id": "zlQcw52azyS2" + } + }, + { + "cell_type": "code", + "source": [ + "print(df['Raw_Word_Count'].describe())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MP8t23g82PjM", + "outputId": "b7a21e5e-c795-4d0d-eb7c-066d244385d5" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "count 9995.000000\n", + "mean 85.890245\n", + "std 12.803133\n", + "min 20.000000\n", + "25% 79.000000\n", + "50% 88.000000\n", + "75% 95.000000\n", + "max 118.000000\n", + "Name: Raw_Word_Count, dtype: float64\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Checking for AI errors by common key error words:\n", + "This data validation step identifies and removes \"non-sentient\" artifacts, such as AI refusals or system error messages (e.g., \"AI helper,\" \"Invalid request\"), which contain no culinary value." + ], + "metadata": { + "id": "ahEIo6_I2vS_" + } + }, + { + "cell_type": "code", + "source": [ + "pd.set_option('display.max_colwidth', None)\n", + "\n", + "# 2. Define flags and create the search pattern\n", + "keywords = [\"Error\", \"Helper\", \"Invalid\"]\n", + "pattern = '|'.join(keywords) # Creates \"Error|Helper|Invalid\"\n", + "\n", + "# 3. Calculate metrics for the quality check\n", + "# 'na=False' ensures we don't get errors if there are empty rows\n", + "mask = df['Raw_Output'].str.contains(pattern, case=False, na=False)\n", + "flagged_df = df[mask]\n", + "\n", + "# 4. Print Summary Statistics\n", + "print(\"=\"*40)\n", + "print(\"📊 DATA QUALITY REPORT\")\n", + "print(\"=\"*40)\n", + "print(f\"Total recipes flagged: {len(flagged_df)}\")\n", + "\n", + "for kw in keywords:\n", + " count = df['Raw_Output'].str.contains(kw, case=False, na=False).sum()\n", + " print(f\"Count for '{kw}': {count}\")\n", + "\n", + "# 5. Display the flagged rows with full text\n", + "if not flagged_df.empty:\n", + " print(\"\\n--- DETAILED VIEW OF FLAGGED ROWS ---\")\n", + " # Displaying Title and Raw_Output to see the specific error messages\n", + " display(flagged_df[['Title', 'Raw_Output']])\n", + "else:\n", + " print(\"\\n✅ No error keywords found in the dataset.\")\n", + "\n", + "# 6. Optional: Reset display settings to default if you want neat tables again later\n", + "# pd.reset_option('display.max_colwidth')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "O8q0zQe72u3U", + "outputId": "d61ca569-5ee8-4ad4-cdfd-3fbd4ba202b8" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "========================================\n", + "📊 DATA QUALITY REPORT\n", + "========================================\n", + "Total recipes flagged: 16\n", + "Count for 'Error': 1\n", + "Count for 'Helper': 12\n", + "Count for 'Invalid': 3\n", + "\n", + "--- DETAILED VIEW OF FLAGGED ROWS ---\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " Title \\\n", + "248 Creamy Dessert Cookies Celebration \n", + "2069 Vanilla Dessert Cake Crumble \n", + "2480 Dark Chocolate Pudding Supreme \n", + "2564 Golden Mediterranean Shakshuka Bowl \n", + "3575 Homemade Dessert Tart Swirl \n", + "4671 Homemade Dessert Tart Bars \n", + "5331 Fresh Mediterranean Shakshuka Salad \n", + "6438 Cinnamon Dessert Cake Dream \n", + "7131 Golden Mediterranean Lamb Chops Bowl \n", + "8043 Spicy Mediterranean Lamb Chops Feast \n", + "8785 Dark Dessert Pudding Dream \n", + "9090 Chocolate Dessert Cookies Supreme \n", + "9174 Herbed Mediterranean Shawarma Bowl \n", + "9310 Golden Mediterranean Hummus Plate Salad \n", + "9573 Creamy Dessert Cake Celebration \n", + "9742 Dark Dessert Tart Swirl \n", + "\n", + " Raw_Output \n", + "248 Helper: Title: Creamy Dessert Cookies Celebration\\nIngredients:\\n- Butter 2 sticks\\n- Sugar 1 cup\\n- Egg yolk 2\\n- Vanilla extract 1 tsp\\n- Flour 2 cups\\n- Milk 1/2 cup\\n- Chocolate chips 1 cup\\nInstructions:\\nCream together butter and sugar until fluffy. Add egg yolk and vanilla. In another bowl, mix flour and milk. Gradually add wet to dry mixture and fold until no dry bits remain. Fold in chocolate chips. Chill the dough in refrigerator for 30 mins. Roll into balls and bake at 350°F (175°C) for about 12 mins \n", + "2069 (RECIPE_NOT_PROVIDED_INVALID_REQUEST)\\nTitle: Vanilla Dessert Cake Crumble\\nIngredients:\\n- Cake crumbs 4 cups\\n- Vanilla ice cream 1 gallon\\n- Whipped cream (to garnish)\\n- Fresh berries (optional)\\nInstructions:\\nCombine cake crumbs in a baking dish. Top with layers of vanilla ice cream and optional fresh berries, if desired. Garnish with whipped cream before serving. \n", + "2480 (INVALID REQUEST) The task requested a dessert pudding, but \"Dark Dessert Pudding Supreme\" doesn't align with typical dessert recipes or ingredient lists. Assuming a possible misnomer, I'll fabricate a basic chocolate pudding to fit the format:\\n\\nTitle: Dark Chocolate Pudding Supreme\\nIngredients:\\n- Dark Chocolate (300g)\\n- Milk (500ml)\\n- Eggs (4)\\n- Sugar (150g)\\n- Vanilla Extract (1 tsp)\\nInstructions:\\nMelt the dark chocolate and mix it with milk. Beat eggs and sugar together until thick and creamy. Gradually add the chocolate mixture into the egg mixture while stirring continuously. Strain the mixture to remove lumps. Pour the mixture into \n", + "2564 Helper: Title: Golden Mediterranean Shakshuka Bowl\\nIngredients:\\n- Eggs 3\\n- Onions (2) finely chopped\\n- Tomatoes (3 large), chopped\\n- Garlic (4 cloves), minced\\n- Bell peppers (1 small), sliced\\n- Zucchini (1 medium), diced\\n- Red onion (1/4), sliced\\n- Olive oil\\n- Pitted Kalamata olives (150g)\\n- Feta cheese (75g)\\n- Fresh parsley (handful)\\n- Fresh oregano (about 1 tsp)\\n- Salt and black pepper\\n- Skirt steak (150g) thinly sliced\\nInstructions:\\nHeat olive oil in a pan over medium heat. Add onions \n", + "3575 Helper Title: Homemade Dessert Tart Swirl\\nIngredients:\\n- Flour 2 cups\\n- Butter 1/2 cup\\n- Sugar 1/3 cup\\n- Egg yolk 1\\n- Milk 1/2 cup\\n- Vanilla essence 1 tsp\\n- Fruit compote 1 cup\\nInstructions:\\nCream together butter and sugar until light and fluffy. Beat in egg yolk and vanilla essence. Gradually add flour while mixing until dough is formed. Roll out on floured surface and place on prepared tart tin. Cut into shapes and bake at 180°C for about 25 minutes or until golden brown. Mix fruit compote with melted chocolate and swirl over tarts before cooling.\n", + "
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TitleRaw_Output
248Creamy Dessert Cookies CelebrationHelper: Title: Creamy Dessert Cookies Celebration\\nIngredients:\\n- Butter 2 sticks\\n- Sugar 1 cup\\n- Egg yolk 2\\n- Vanilla extract 1 tsp\\n- Flour 2 cups\\n- Milk 1/2 cup\\n- Chocolate chips 1 cup\\nInstructions:\\nCream together butter and sugar until fluffy. Add egg yolk and vanilla. In another bowl, mix flour and milk. Gradually add wet to dry mixture and fold until no dry bits remain. Fold in chocolate chips. Chill the dough in refrigerator for 30 mins. Roll into balls and bake at 350°F (175°C) for about 12 mins
2069Vanilla Dessert Cake Crumble(RECIPE_NOT_PROVIDED_INVALID_REQUEST)\\nTitle: Vanilla Dessert Cake Crumble\\nIngredients:\\n- Cake crumbs 4 cups\\n- Vanilla ice cream 1 gallon\\n- Whipped cream (to garnish)\\n- Fresh berries (optional)\\nInstructions:\\nCombine cake crumbs in a baking dish. Top with layers of vanilla ice cream and optional fresh berries, if desired. Garnish with whipped cream before serving.
2480Dark Chocolate Pudding Supreme(INVALID REQUEST) The task requested a dessert pudding, but \"Dark Dessert Pudding Supreme\" doesn't align with typical dessert recipes or ingredient lists. Assuming a possible misnomer, I'll fabricate a basic chocolate pudding to fit the format:\\n\\nTitle: Dark Chocolate Pudding Supreme\\nIngredients:\\n- Dark Chocolate (300g)\\n- Milk (500ml)\\n- Eggs (4)\\n- Sugar (150g)\\n- Vanilla Extract (1 tsp)\\nInstructions:\\nMelt the dark chocolate and mix it with milk. Beat eggs and sugar together until thick and creamy. Gradually add the chocolate mixture into the egg mixture while stirring continuously. Strain the mixture to remove lumps. Pour the mixture into
2564Golden Mediterranean Shakshuka BowlHelper: Title: Golden Mediterranean Shakshuka Bowl\\nIngredients:\\n- Eggs 3\\n- Onions (2) finely chopped\\n- Tomatoes (3 large), chopped\\n- Garlic (4 cloves), minced\\n- Bell peppers (1 small), sliced\\n- Zucchini (1 medium), diced\\n- Red onion (1/4), sliced\\n- Olive oil\\n- Pitted Kalamata olives (150g)\\n- Feta cheese (75g)\\n- Fresh parsley (handful)\\n- Fresh oregano (about 1 tsp)\\n- Salt and black pepper\\n- Skirt steak (150g) thinly sliced\\nInstructions:\\nHeat olive oil in a pan over medium heat. Add onions
3575Homemade Dessert Tart SwirlHelper Title: Homemade Dessert Tart Swirl\\nIngredients:\\n- Flour 2 cups\\n- Butter 1/2 cup\\n- Sugar 1/3 cup\\n- Egg yolk 1\\n- Milk 1/2 cup\\n- Vanilla essence 1 tsp\\n- Fruit compote 1 cup\\nInstructions:\\nCream together butter and sugar until light and fluffy. Beat in egg yolk and vanilla essence. Gradually add flour while mixing until dough is formed. Roll out on floured surface and place on prepared tart tin. Cut into shapes and bake at 180°C for about 25 minutes or until golden brown. Mix fruit compote with melted chocolate and swirl over tarts before cooling.<END_RECIPE
4671Homemade Dessert Tart BarsHelper:\\nTitle: Homemade Dessert Tart Bars\\nIngredients:\\n- Flour 500g\\n- Butter 200g\\n- Eggs 4\\n- Sugar 300g\\n- Jams 300g\\n- Cinnamon 1tsp\\nInstructions:\\nPreheat oven to 375°F. Mix flour and butter until crumbly, add eggs and cinnamon, mix well. Roll into pan and press evenly. Spread jam over base. Bake for 20 minutes or until golden brown. Serve warm.
5331Fresh Mediterranean Shakshuka SaladHelper:\\nTitle: Fresh Mediterranean Shakshuka Salad\\nIngredients:\\n- Tomatoes 500g\\n- Egg yolks 3\\n- Spinach 50g\\n- Onions 2\\n- Garlic cloves 4\\n- Olive oil 2 tbsp\\n- Red pepper flakes to taste\\n- Lemon juice 2 tbsp\\n- Kalamata olives 2 tbsp\\n- Feta cheese 100g\\n- Olive oil for drizzling\\nInstructions:\\nBlend tomatoes and onions until smooth, add garlic, red pepper flakes, lemon juice, feta and olives. Heat olive oil in a pan, pour in tomato mixture, heat gently for 5 mins then place eggs on top and let them cook through
6438Cinnamon Dessert Cake DreamHelper Title: Cinnamon Dessert Cake Dream\\nIngredients:\\n- Flour 500g\\n- Sugar 400g\\n- Eggs 3\\n- Butter 200g\\n- Milk 200ml\\n- Cinnamon 2tsp\\n- Vanilla essence 1tsp\\n- Chocolate chips 50g\\nInstructions:\\nMix flour, sugar, eggs, cinnamon, and vanilla essence together in a large bowl. Add butter and milk, mix well until smooth. Fold in chocolate chips. Pour batter into a greased cake tin. Bake at 180°C for 30 minutes or until a toothpick comes out clean.
7131Golden Mediterranean Lamb Chops BowlHelper: \\nTitle: Golden Mediterranean Lamb Chops Bowl\\nIngredients:\\n- Lamb chops 4\\n- Olive oil\\n- Garlic cloves\\n- Lemon wedges\\n- Cucumbers\\n- Tomatoes\\n- Feta cheese\\n- Pitted Kalamata olives\\n- Fresh parsley\\n- Mixed salad leaves\\nInstructions:\\nDrizzle the lamb chops lightly with olive oil, sprinkle them with minced garlic, and then season with salt and pepper. Heat a pan on high flame, add a bit more olive oil, and sear the chops for 3 minutes each side or until golden brown. Remove from heat and set aside. In a mixing bowl, toss together your mixed salad leaves, cucumbers, tomatoes, and pitted
8043Spicy Mediterranean Lamb Chops Feast(INVALID_INPUT)\\nRETRY WITH A VALID FOOD ITEM LIKE 'LAMB CHOPS'\\nTitle: Spicy Mediterranean Lamb Chops Feast\\nIngredients:\\n- Lamb chops 1kg\\n- Tomatoes 8\\n- Cucumber 2\\n- Onions (1 medium)\\n- Lemon 1\\n- Garlic 3 cloves\\n- Olive oil\\n- Fresh oregano\\n- Red pepper flakes\\n- Salt and pepper\\nInstructions:\\nCut lamb chops, slice tomatoes, chop onions, crush garlic. In a large pan, drizzle olive oil, add tomatoes and onions, and sauté until tender. Season lamb chops with red pepper flakes, salt, and pepper. Add garlic to the pan, then place the lamb chops in the
8785Dark Dessert Pudding Dream(RECIPE_NOT_CREATED_ERROR) The instruction to generate an example recipe in a specific format has been misunderstood. The task was not fully executed as requested for 'Dark Dessert Pudding Dream'. I apologize for the oversight. Please provide a different recipe name or request me to follow the given format accurately.
9090Chocolate Dessert Cookies SupremeHelper: I couldn't find specific ingredients or instructions for \"Chocolate Dessert Cookies Supreme\". Here is a generic example that might fit:\\n\\nTitle: Chocolate Dessert Cookies Supreme\\nIngredients:\\n- Flour 2 cups\\n- Baking Soda 1 teaspoon\\n- Chocolate Chips 1 cup\\n- Butter 1/2 cup\\n- Sugar 1/2 cup\\n- Eggs 1\\n- Vanilla 1 teaspoon\\n\\nInstructions:\\nPreheat oven to 350 degrees F (175 degrees C). Sift together flour and baking soda. Beat butter and sugar until fluffy. Add eggs and vanilla; mix well. Gradually add dry ingredients just until blended. Fold in chocolate chips. Drop by spoonfuls onto ungreased
9174Herbed Mediterranean Shawarma BowlHelper's Note: Adapted from traditional shawarma bowls, this recipe incorporates fresh herbs and spices for a vibrant flavor.\\n\\nTitle: Herbed Mediterranean Shawarma Bowl\\nIngredients:\\n- Chicken or lamb (about 500g)\\n- Olive oil\\n- Seasonings: paprika, cumin, garlic powder, black pepper\\n- Herbs: parsley, cilantro, dill\\n- Couscous (or rice) 1 cup\\n- Tzatziki sauce 2 tbsp\\n- Pickled vegetables (like cucumbers, peppers, onions)\\n- Cherry tomatoes sliced\\n- Greek yogurt (for garnish)\\n\\nInstructions:\\nCut the chicken/lamb into thin slices and season with olive oil, spices, and finely chopped
9310Golden Mediterranean Hummus Plate SaladHelper:, Chickpeas 500g, Avocado 1, Tomatoes 100g, Cucumbers 2, Red Onion 1, Lemon Juice 2 tbsp, Olive Oil 3 tbsp, Mint Leaves (fresh) handful, Salt, Black Pepper\\nInstructions:\\nIn a blender or food processor, combine chickpeas, diced avocados, chopped tomatoes, sliced cucumbers, red onion, lemon juice, olive oil, mint leaves, salt, and black pepper. Blend until smooth. Serve the hummus with the sides for added flavor and texture.
9573Creamy Dessert Cake CelebrationHelper: \\nTitle: Creamy Dessert Cake Celebration\\nIngredients:\\n- Cake Batter 1kg\\n- Cream Cheese 3lb\\n- Vanilla Ice Cream (scoop)\\n- Fresh Berries\\n- Whipped Cream\\n- Candied Cherries (sprinkles)\\nInstructions:\\nMix all ingredients together until creamy, fill a pan with this mixture, let it cool and set, then decorate with fresh berries, whipped cream, and candied cherries on top.
9742Dark Dessert Tart SwirlHelper's Note: Please note that \"Dark Dessert Tart Swirl\" might be a unique and imaginative recipe not commonly found; hence the ingredients and instructions could be creatively fabricated based on a dessert tart swirled with something dark.\\n\\nTitle: Dark Dessert Tart Swirl\\nIngredients:\\n- Dark Chocolate Chips 2 cups\\n- Shortcrust Pastry Dough 1 sheet\\n- Vanilla Beans or extract 1\\n- Whipped Cream 1 cup\\n- Caramel Sauce (optional)\\nInstructions:\\nRoll out pastry dough into a circle, place on a baking sheet and spread chocolate evenly over the dough. Spoon caramel sauce if desired, swirl the mixture around to make a swirly pattern, and then top it with whipped cream. Bake
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\n", + " \n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "summary": "{\n \"name\": \"# pd\",\n \"rows\": 16,\n \"fields\": [\n {\n \"column\": \"Title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 16,\n \"samples\": [\n \"Creamy Dessert Cookies Celebration\",\n \"Vanilla Dessert Cake Crumble\",\n \"Homemade Dessert Tart Bars\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Raw_Output\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 16,\n \"samples\": [\n \"Helper: Title: Creamy Dessert Cookies Celebration\\nIngredients:\\n- Butter 2 sticks\\n- Sugar 1 cup\\n- Egg yolk 2\\n- Vanilla extract 1 tsp\\n- Flour 2 cups\\n- Milk 1/2 cup\\n- Chocolate chips 1 cup\\nInstructions:\\nCream together butter and sugar until fluffy. Add egg yolk and vanilla. In another bowl, mix flour and milk. Gradually add wet to dry mixture and fold until no dry bits remain. Fold in chocolate chips. Chill the dough in refrigerator for 30 mins. Roll into balls and bake at 350\\u00b0F (175\\u00b0C) for about 12 mins\",\n \"(RECIPE_NOT_PROVIDED_INVALID_REQUEST)\\nTitle: Vanilla Dessert Cake Crumble\\nIngredients:\\n- Cake crumbs 4 cups\\n- Vanilla ice cream 1 gallon\\n- Whipped cream (to garnish)\\n- Fresh berries (optional)\\nInstructions:\\nCombine cake crumbs in a baking dish. Top with layers of vanilla ice cream and optional fresh berries, if desired. Garnish with whipped cream before serving.\",\n \"Helper:\\nTitle: Homemade Dessert Tart Bars\\nIngredients:\\n- Flour 500g\\n- Butter 200g\\n- Eggs 4\\n- Sugar 300g\\n- Jams 300g\\n- Cinnamon 1tsp\\nInstructions:\\nPreheat oven to 375\\u00b0F. Mix flour and butter until crumbly, add eggs and cinnamon, mix well. Roll into pan and press evenly. Spread jam over base. Bake for 20 minutes or until golden brown. Serve warm.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "we get in here recipes with: \"he instruction to generate an example recipe in a specific format has been misunderstood. The task was not fully executed as requested for 'Dark Dessert Pudding Dream'. I apologize for the oversight. Please provide a different recipe name or request me to follow the given format accurately.\" which is better just to remove this cases." + ], + "metadata": { + "id": "dkkNZhN04DOk" + } + }, + { + "cell_type": "code", + "source": [ + "# Create a filter for the keywords and keep only the rows that do NOT match\n", + "mask = df['Raw_Output'].str.contains(\"Error|Helper|Invalid\", case=False, na=False)\n", + "df = df[~mask].copy()\n", + "\n", + "print(f\"Cleaned! Remaining recipes: {len(df)}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gMzMmR5T3ZZI", + "outputId": "9712b32f-2380-44c3-db2c-cd96fb3ee51b" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cleaned! Remaining recipes: 9979\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Checking duplications on the instructions coulmn:" + ], + "metadata": { + "id": "lXmeucST5C7V" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "# 1. Ensure full text is always visible\n", + "pd.set_option('display.max_colwidth', None)\n", + "\n", + "# 2. Count the number of duplicate instructions\n", + "num_duplicates = df.duplicated(subset=['Instructions']).sum()\n", + "print(f\"Number of duplicate recipes found: {num_duplicates}\")\n", + "\n", + "# 3. Identify and display duplicates including Raw_Output\n", + "if num_duplicates > 0:\n", + " print(\"\\n--- Preview of Duplicate Recipes (with Raw Data) ---\")\n", + " # keep=False shows all copies of the duplicate\n", + " duplicates = df[df.duplicated(subset=['Instructions'], keep=False)].sort_values(by='Instructions')\n", + "\n", + " # We always include 'Raw_Output' as requested\n", + " display(duplicates[['Title', 'Instructions', 'Raw_Output']].head(10))\n", + "else:\n", + " print(\"No duplicates found!\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 348 + }, + "id": "7lMiEeOM47Cs", + "outputId": "8223aac7-715e-4af8-c233-ff7592acecc7" + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Number of duplicate recipes found: 3\n", + "\n", + "--- Preview of Duplicate Recipes (with Raw Data) ---\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " Title Instructions \\\n", + "566 Zesty Mediterranean Shakshuka with Yogurt Sauce NaN \n", + "3408 Zesty Mediterranean Hummus Plate Bowl NaN \n", + "8269 Szechuan Asian Fusion Beef with Cashews NaN \n", + "8716 Ginger Asian_Fusion Rice Bowl with Cashews NaN \n", + "\n", + " Raw_Output \n", + "566 For Zesty Mediterranean Shakshuka with Yogurt Sauce\\nIngredients:\\n- Eggs 6\\n- Tomatoes 1kg, chopped\\n- Onion 1, diced\\n- Bell peppers 2, diced\\n- Garlic 3 cloves, minced\\n- Capsicum (jalapeño or banana) 1, seeded and finely chopped\\n- Pimiento (aubergine) 1, diced\\n- Tomatoes paste 2 tbsp\\n- Olive oil 2 tbsp\\n- Paprika 1 tsp\\n- Sumac 1 tsp\\n- Cumin 1 tsp\\n- Salt and pepper to taste\\n- Fresh parsley 2 tbsp, chopped\\n- Plain yogurt 1/2 cup\\n- Lemon wedges\\nInstructions: \n", + "3408 - Chickpeas 500g\\n- Lemon 1\\n- Garlic 3 cloves\\n- Cilantro (fresh) 2 tbsp\\n- Tahini 2 tbsp\\n- Olive oil 2 tbsp\\n- Water or lemon juice as needed\\n- Salt and pepper to taste\\n- Vegetables: cherry tomatoes, cucumber, red onion, bell peppers, olives\\n- Croutons\\n- Feta cheese\\nTitle: Zesty Mediterranean Hummus Plate Bowl\\nIngredients:\\n- Hummus 2 cups\\n- Vegetables: cherry tomatoes, cucumber, red onion, bell peppers, olives 1 cup mixed\\n- Croutons 3 oz\\n- Feta cheese 1/2 cup\\nInstructions: \n", + "8269 For those seeking an exciting blend of flavors in their meals, here’s the recipe for Szechuan Asian Fusion Beef with Cashews.\\n\\nTitle: Szechuan Asian Fusion Beef with Cashews\\n\\nIngredients:\\n- Beef 500g\\n- Noodles 200g\\n- Cashews 100g\\n- Lemongrass 3 stalks\\n- Garlic (6 cloves)\\n- Ginger (3 slices)\\n- Scallions (to taste)\\n- Soy Sauce 1/4 cup\\n- Sesame Oil 2 tbsp\\n- Spices: Sichuan peppercorns, coriander seeds\\n- Rice Vinegar\\n- Salt to taste\\n- Sesame Seeds for garnish\\n\\nInstructions: \n", + "8716 Title: Ginger Asian_Fusion Rice Bowl with Cashews\\n Ingredients:\\n - Basmati Rice 500g\\n - Chicken 200g\\n - Carrots 1\\n - Broccoli 100g\\n - Green Beans 100g\\n - Soy Sauce 2 tbsp\\n - Oyster Sauce 1 tbsp\\n - Shredded Ginger 1 tsp\\n - Sesame Oil 1 tsp\\n - Cashews 50g\\n - Green Onions (chopped) 2\\n - Rice Vinegar 1 tsp\\n - Salt to taste\\n - Saffron strands (for garnish)\\n Instructions: " + ], + "text/html": [ + "\n", + "
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TitleInstructionsRaw_Output
566Zesty Mediterranean Shakshuka with Yogurt SauceNaNFor Zesty Mediterranean Shakshuka with Yogurt Sauce\\nIngredients:\\n- Eggs 6\\n- Tomatoes 1kg, chopped\\n- Onion 1, diced\\n- Bell peppers 2, diced\\n- Garlic 3 cloves, minced\\n- Capsicum (jalapeño or banana) 1, seeded and finely chopped\\n- Pimiento (aubergine) 1, diced\\n- Tomatoes paste 2 tbsp\\n- Olive oil 2 tbsp\\n- Paprika 1 tsp\\n- Sumac 1 tsp\\n- Cumin 1 tsp\\n- Salt and pepper to taste\\n- Fresh parsley 2 tbsp, chopped\\n- Plain yogurt 1/2 cup\\n- Lemon wedges\\nInstructions:
3408Zesty Mediterranean Hummus Plate BowlNaN- Chickpeas 500g\\n- Lemon 1\\n- Garlic 3 cloves\\n- Cilantro (fresh) 2 tbsp\\n- Tahini 2 tbsp\\n- Olive oil 2 tbsp\\n- Water or lemon juice as needed\\n- Salt and pepper to taste\\n- Vegetables: cherry tomatoes, cucumber, red onion, bell peppers, olives\\n- Croutons\\n- Feta cheese\\nTitle: Zesty Mediterranean Hummus Plate Bowl\\nIngredients:\\n- Hummus 2 cups\\n- Vegetables: cherry tomatoes, cucumber, red onion, bell peppers, olives 1 cup mixed\\n- Croutons 3 oz\\n- Feta cheese 1/2 cup\\nInstructions:
8269Szechuan Asian Fusion Beef with CashewsNaNFor those seeking an exciting blend of flavors in their meals, here’s the recipe for Szechuan Asian Fusion Beef with Cashews.\\n\\nTitle: Szechuan Asian Fusion Beef with Cashews\\n\\nIngredients:\\n- Beef 500g\\n- Noodles 200g\\n- Cashews 100g\\n- Lemongrass 3 stalks\\n- Garlic (6 cloves)\\n- Ginger (3 slices)\\n- Scallions (to taste)\\n- Soy Sauce 1/4 cup\\n- Sesame Oil 2 tbsp\\n- Spices: Sichuan peppercorns, coriander seeds\\n- Rice Vinegar\\n- Salt to taste\\n- Sesame Seeds for garnish\\n\\nInstructions:
8716Ginger Asian_Fusion Rice Bowl with CashewsNaNTitle: Ginger Asian_Fusion Rice Bowl with Cashews\\n Ingredients:\\n - Basmati Rice 500g\\n - Chicken 200g\\n - Carrots 1\\n - Broccoli 100g\\n - Green Beans 100g\\n - Soy Sauce 2 tbsp\\n - Oyster Sauce 1 tbsp\\n - Shredded Ginger 1 tsp\\n - Sesame Oil 1 tsp\\n - Cashews 50g\\n - Green Onions (chopped) 2\\n - Rice Vinegar 1 tsp\\n - Salt to taste\\n - Saffron strands (for garnish)\\n Instructions:
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "repr_error": "0" + } + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "We can see 3 duplications of NaN but, when looking on the Raq output, it is clear the parser function just didn't work well, ans as what matters on the recipe is the raw output, it is ok." + ], + "metadata": { + "id": "h54lw4Wv5Q-n" + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "\n", + "---\n", + "\n" + ], + "metadata": { + "id": "igPVVe4dz7h7" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Data Visualization:" + ], + "metadata": { + "id": "p2C-7yllzyJm" + } + }, + { + "cell_type": "markdown", + "source": [ + "Top 20 Most Frequent Ingredients Generated by AI" + ], + "metadata": { + "id": "L3LL7uOLiynN" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "\n", + "# 1. Function to clean and extract just the ingredient names\n", + "def extract_ingredients(text):\n", + " # Remove newlines, bullets (-), and roughly try to remove numbers/units\n", + " # This is a simple heuristic: keep words with 3+ letters\n", + " words = re.findall(r'[a-zA-Z]{3,}', text.lower())\n", + " # Filter out common non-ingredients if needed (like \"spoon\", \"cup\")\n", + " ignore = {'cup', 'tbsp', 'tsp', 'spoon', 'spoons', 'approx', 'salt', 'pepper'}\n", + " return [w for w in words if w not in ignore]\n", + "\n", + "# 2. Collect all ingredients into one huge list\n", + "all_ingredients = []\n", + "df['Ingredients'].apply(lambda x: all_ingredients.extend(extract_ingredients(str(x))))\n", + "\n", + "# 3. Count top 20\n", + "counter = collections.Counter(all_ingredients)\n", + "top_ingredients = counter.most_common(20)\n", + "\n", + "# 4. Plot\n", + "ingredients, counts = zip(*top_ingredients)\n", + "plt.figure(figsize=(12, 6))\n", + "sns.barplot(x=list(counts), y=list(ingredients), palette=\"viridis\")\n", + "plt.title(\"Top 20 Most Frequent Ingredients Generated by AI\")\n", + "plt.xlabel(\"Frequency\")\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 651 + }, + "id": "k3TRc5YBli69", + "outputId": "d40ac512-956c-42ac-975d-cbb2bdc65efb" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/tmp/ipython-input-4079693601.py:21: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.barplot(x=list(counts), y=list(ingredients), palette=\"viridis\")\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Top 10 Common Phrases in Instructions" + ], + "metadata": { + "id": "xRr4853-it3X" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.feature_extraction.text import CountVectorizer\n", + "\n", + "\n", + "# 1. Function to extract top n-grams\n", + "def get_top_ngrams(corpus, n=2, top_k=10):\n", + " # 'english' stop words removes common filler words like 'the', 'and', etc.\n", + " vec = CountVectorizer(ngram_range=(n, n), stop_words='english').fit(corpus)\n", + " bag_of_words = vec.transform(corpus)\n", + " sum_words = bag_of_words.sum(axis=0)\n", + " words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n", + " words_freq = sorted(words_freq, key=lambda x: x[1], reverse=True)\n", + " return words_freq[:top_k]\n", + "\n", + "# 2. Calculate top 10 bigrams (2-word phrases) from Instructions\n", + "# We use .dropna() to avoid errors with missing rows\n", + "top_bigrams = get_top_ngrams(df['Instructions'].dropna().astype(str), n=2, top_k=10)\n", + "\n", + "# 3. Convert to DataFrame for plotting\n", + "df_bigrams = pd.DataFrame(top_bigrams, columns=['Phrase', 'Frequency'])\n", + "\n", + "# 4. Create the Visualization\n", + "plt.figure(figsize=(12, 6))\n", + "sns.set_style(\"whitegrid\")\n", + "\n", + "# Using a horizontal bar plot for readability\n", + "ax = sns.barplot(data=df_bigrams, x='Frequency', y='Phrase', palette='flare')\n", + "\n", + "# Add titles and labels\n", + "plt.title(\"Top 10 Most Common 2-Word Phrases in Instructions\", fontsize=16, fontweight='bold')\n", + "plt.xlabel(\"Frequency (Count)\", fontsize=12)\n", + "plt.ylabel(\"Bigram Phrases\", fontsize=12)\n", + "\n", + "# Add value labels on the bars for precision\n", + "ax.bar_label(ax.containers[0], padding=3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 694 + }, + "id": "ahY-WhWGmBNF", + "outputId": "aa2bfb15-5bdd-4471-f2b0-7ee84cdc60da" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/tmp/ipython-input-3634198376.py:26: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " ax = sns.barplot(data=df_bigrams, x='Frequency', y='Phrase', palette='flare')\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Top 20 Common Dishes" + ], + "metadata": { + "id": "A6aHytf-zBPd" + } + }, + { + "cell_type": "code", + "source": [ + "# 1. ensure Dish_Type column exists (Run this part if you haven't already)\n", + "common_dishes = [\n", + " \"Pasta\", \"Risotto\", \"Lasagna\", \"Pizza\", \"Gnocchi\", \"Ravioli\", \"Meatballs\",\n", + " \"Curry\", \"Sushi\", \"Ramen\", \"Stir Fry\", \"Dumplings\", \"Pad Thai\", \"Fried Rice\",\n", + " \"Falafel\", \"Shakshuka\", \"Hummus\", \"Shawarma\", \"Kebab\", \"Couscous\",\n", + " \"Cake\", \"Pie\", \"Cookies\", \"Brownies\", \"Ice Cream\", \"Muffins\", \"Tart\", \"Pudding\",\n", + " \"Burger\", \"Ribs\", \"Wings\", \"Chili\", \"Pancakes\", \"Tofu\", \"Chicken\", \"Beef\", \"Duck\"\n", + "]\n", + "\n", + "def extract_dish_type(title):\n", + " title_lower = str(title).lower()\n", + " for dish in common_dishes:\n", + " if dish.lower() in title_lower:\n", + " return dish\n", + " return \"Other\"\n", + "\n", + "# Apply extraction if not done yet\n", + "if 'Dish_Type' not in df.columns:\n", + " df['Dish_Type'] = df['Title'].apply(extract_dish_type)\n", + "\n", + "# 2. Plot ONLY the Top 20 Dishes\n", + "plt.figure(figsize=(12, 8)) # Adjusted size for a single large plot\n", + "sns.set_style(\"whitegrid\")\n", + "\n", + "# Get Top 20\n", + "top_dishes = df['Dish_Type'].value_counts().nlargest(20).index\n", + "\n", + "# Create Plot\n", + "ax = sns.countplot(data=df, x='Dish_Type', order=top_dishes, palette='viridis')\n", + "\n", + "plt.title(\"Top 20 Most Common Dish Types\", fontsize=16, fontweight='bold')\n", + "plt.xlabel(\"Dish Type\", fontsize=12)\n", + "plt.ylabel(\"Count\", fontsize=12)\n", + "plt.xticks(rotation=45)\n", + "\n", + "# Optional: Add numbers on top of bars\n", + "for i in ax.containers:\n", + " ax.bar_label(i,)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 894 + }, + "id": "VFf8s_ngv3OA", + "outputId": "f7a1e185-1397-4025-c372-8c42b6ba6a5a" + }, + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/tmp/ipython-input-3050610154.py:29: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " ax = sns.countplot(data=df, x='Dish_Type', order=top_dishes, palette='viridis')\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Raw output length" + ], + "metadata": { + "id": "KlZot7mCzHxG" + } + }, + { + "cell_type": "code", + "source": [ + "# 1. Calculate Word Count for the ENTIRE Raw_Output column\n", + "df['Raw_Word_Count'] = df['Raw_Output'].astype(str).apply(lambda x: len(x.split()))\n", + "\n", + "# 2. Plot the Distribution\n", + "plt.figure(figsize=(12, 6))\n", + "sns.set_style(\"whitegrid\")\n", + "\n", + "# bins=50 gives a detailed view of the shape\n", + "sns.histplot(df['Raw_Word_Count'], bins=50, kde=True, color='royalblue')\n", + "\n", + "# Add a line for the Average length\n", + "mean_val = df['Raw_Word_Count'].mean()\n", + "plt.axvline(mean_val, color='red', linestyle='--', linewidth=2, label=f'Average: {int(mean_val)} words')\n", + "\n", + "plt.title(\"Distribution of Raw Output Lengths (All 10,000 Recipes)\", fontsize=16)\n", + "plt.xlabel(\"Word Count (Raw Output)\", fontsize=12)\n", + "plt.ylabel(\"Frequency\", fontsize=12)\n", + "plt.legend()\n", + "\n", + "plt.show()\n", + "\n", + "# 3. Print the exact statistics\n", + "print(\"--- Word Count Statistics ---\")\n", + "print(df['Raw_Word_Count'].describe())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 744 + }, + "id": "fSZPnCn4v3G0", + "outputId": "89cf0999-5c54-4271-8e74-8f64c1e90674" + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--- Word Count Statistics ---\n", + "count 9979.000000\n", + "mean 85.892274\n", + "std 12.789463\n", + "min 20.000000\n", + "25% 79.000000\n", + "50% 88.000000\n", + "75% 95.000000\n", + "max 118.000000\n", + "Name: Raw_Word_Count, dtype: float64\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Our dataset has no empty recipes. but haing recipes with less than 40 words is weird, we will exmaine it." + ], + "metadata": { + "id": "MbxvbWu8y5LW" + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "HRzInNGXzcyd" + } + }, + { + "cell_type": "code", + "source": [ + "# 1. Define the Keywords for each Region\n", + "# REMOVED: \"Dessert\" category\n", + "# ADJUSTED: \"Israeli\" is now above \"Mediterranean\" to prioritize it\n", + "region_keywords = {\n", + " \"Italian\": [\"Italian\", \"Pasta\", \"Risotto\", \"Lasagna\", \"Pizza\", \"Gnocchi\", \"Ravioli\", \"Carbonara\", \"Tuscan\", \"Sicilian\", \"Roman\"],\n", + " \"Asian\": [\"Asian\", \"Curry\", \"Sushi\", \"Ramen\", \"Stir Fry\", \"Dumplings\", \"Pad Thai\", \"Fried Rice\", \"Teriyaki\", \"Szechuan\", \"Wok\"],\n", + " \"Israeli\": [\"Israeli\", \"Shakshuka\", \"Tahini\", \"Challah\", \"Matzah\"],\n", + " \"Mediterranean\": [\"Mediterranean\", \"Falafel\", \"Hummus\", \"Shawarma\", \"Kebab\", \"Couscous\", \"Greek\"],\n", + " \"American\": [\"American\", \"Burger\", \"Ribs\", \"Wings\", \"Chili\", \"Pancakes\", \"Mac and Cheese\", \"BBQ\"],\n", + " \"French\": [\"French\", \"Quiche\", \"Souffle\", \"Crepe\", \"Ratatouille\", \"Baguette\", \"Croissant\"]\n", + "}\n", + "\n", + "# 2. Function to check the title against keywords\n", + "def get_region(title):\n", + " title_str = str(title).lower()\n", + "\n", + " # It stops at the first match, so order in the dictionary above matters!\n", + " for region, keywords in region_keywords.items():\n", + " for keyword in keywords:\n", + " if keyword.lower() in title_str:\n", + " return region\n", + " return \"Other/Fusion\"\n", + "\n", + "# 3. Apply the function to create a new column\n", + "df['Region'] = df['Title'].apply(get_region)\n", + "\n", + "# 4. Plot the Results\n", + "plt.figure(figsize=(12, 6))\n", + "sns.set_style(\"whitegrid\")\n", + "\n", + "# Count and Sort\n", + "region_counts = df['Region'].value_counts()\n", + "\n", + "# Create Bar Plot\n", + "sns.barplot(x=region_counts.index, y=region_counts.values, palette=\"magma\")\n", + "\n", + "plt.title(\"Number of Recipes by Region\", fontsize=15)\n", + "plt.xlabel(\"Region\")\n", + "plt.ylabel(\"Count\")\n", + "plt.xticks(rotation=45)\n", + "\n", + "# Add value labels on top of bars\n", + "for i, v in enumerate(region_counts.values):\n", + " plt.text(i, v + 50, str(v), ha='center', fontweight='bold')\n", + "\n", + "plt.show()\n", + "\n", + "# 5. Check the exact counts\n", + "print(df['Region'].value_counts())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 842 + }, + "id": "oc_FJAOBqtr5", + "outputId": "27b17184-f843-4e43-d5ec-4593aa63f1cf" + }, + "execution_count": 23, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/tmp/ipython-input-1584351782.py:35: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.barplot(x=region_counts.index, y=region_counts.values, palette=\"magma\")\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Region\n", + "Other/Fusion 2504\n", + "Asian 2501\n", + "Italian 2491\n", + "Mediterranean 2008\n", + "Israeli 475\n", + "Name: count, dtype: int64\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Methodology & Insights: We classified regions using hierarchical keyword mapping (e.g., \"Pasta\" → Italian), prioritizing \"Israeli\" tags over general \"Mediterranean\" ones and grouping desserts into \"Other/Fusion.\" The analysis reveals a nearly perfect 25% split across the four main categories, confirming the synthetic generation successfully maintained balance" + ], + "metadata": { + "id": "vVjbOEKWsR4O" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Outliers" + ], + "metadata": { + "id": "XFnKEUymsn8c" + } + }, + { + "cell_type": "code", + "source": [ + "df['Raw_Char_Count'] = df['Raw_Output'].astype(str).apply(len)\n", + "\n", + "# 2. Setup the figure\n", + "fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n", + "plt.subplots_adjust(hspace=0.4)\n", + "\n", + "# --- ROW 1: Word Count ---\n", + "sns.boxplot(ax=axes[0, 0], x=df['Raw_Word_Count'], color='skyblue')\n", + "axes[0, 0].set_title('Outliers: Word Count (Boxplot)', fontweight='bold')\n", + "\n", + "sns.histplot(ax=axes[0, 1], x=df['Raw_Word_Count'], kde=True, color='skyblue')\n", + "axes[0, 1].set_title('Distribution: Word Count (Histogram)', fontweight='bold')\n", + "\n", + "# --- ROW 2: Character Count ---\n", + "sns.boxplot(ax=axes[1, 0], x=df['Raw_Char_Count'], color='salmon')\n", + "axes[1, 0].set_title('Outliers: Character Length (Boxplot)', fontweight='bold')\n", + "\n", + "sns.histplot(ax=axes[1, 1], x=df['Raw_Char_Count'], kde=True, color='salmon')\n", + "axes[1, 1].set_title('Distribution: Character Length (Histogram)', fontweight='bold')\n", + "\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 872 + }, + "id": "qMgTl3-F9wd6", + "outputId": "d34aa4a2-fd11-4921-b85c-099c958436cf" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "1. **Word Count Analysis**\n", + "Outliers & Distribution: The data is heavily concentrated between 80 and 95 words, with significant low-end outliers suggesting a subset of very brief recipes.\n", + "\n", + "Contextual Variation: This spread is natural, as \"short and descriptive\" recipes focus on efficiency, while those nearing the 120-word mark likely include \"the story behind the recipe\" or extra cultural context.\n", + "\n", + "2. **Character Length Analysis**\n", + "Outliers & Distribution: The character count distribution is smoother and centered around 500–550 characters, though it shows more high-end outliers than the word count plot.\n", + "\n", + "Vocabulary Density: These outliers highlight the difference between recipes using simple language and those utilizing longer, technical culinary terms or descriptive narratives." + ], + "metadata": { + "id": "5K4izTR9-dNZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "These data points should ***not*** be classified as technical outliers because they reflect the natural stylistic variance found in real-world culinary writing. Shorter entries correspond to concise, descriptive instructions focused purely on efficiency, while longer entries rightfully include narrative elements or the \"story behind the recipe.\" Therefore, this spread in word and character counts indicates a healthy, diverse dataset that mirrors authentic human authorship rather than data quality errors." + ], + "metadata": { + "id": "81Qq76My_A08" + } + } + ] +} \ No newline at end of file