{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "jZ0wVSoo_5yd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "862facac-1329-47b8-8a08-271a7e1bbd61"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.1-py3-none-any.whl size=5579 sha256=cbb8fe27672a9be08a00065ed281dd9021047c66b3bef442bd4afc3b1f75cd50\n",
            "  Stored in directory: /root/.cache/pip/wheels/01/a6/d1/1c0828c304a4283b2c1639a09ad86f83d7c487ef34c6b4a1bf\n",
            "Successfully built sentence_transformers ffmpy\n",
            "Installing collected packages: sentencepiece, pydub, ffmpy, websockets, typing-extensions, tomlkit, shellingham, semantic-version, python-multipart, orjson, h11, colorama, annotated-types, aiofiles, uvicorn, starlette, pydantic-core, httpcore, pydantic, httpx, gradio-client, fastapi, sentence_transformers, gradio\n",
            "  Attempting uninstall: typing-extensions\n",
            "    Found existing installation: typing_extensions 4.5.0\n",
            "    Uninstalling typing_extensions-4.5.0:\n",
            "      Successfully uninstalled typing_extensions-4.5.0\n",
            "  Attempting uninstall: pydantic\n",
            "    Found existing installation: pydantic 1.10.13\n",
            "    Uninstalling pydantic-1.10.13:\n",
            "      Successfully uninstalled pydantic-1.10.13\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
            "llmx 0.0.15a0 requires openai, which is not installed.\n",
            "llmx 0.0.15a0 requires tiktoken, which is not installed.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.8.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed aiofiles-23.2.1 annotated-types-0.6.0 colorama-0.4.6 fastapi-0.104.1 ffmpy-0.3.1 gradio-4.8.0 gradio-client-0.7.1 h11-0.14.0 httpcore-1.0.2 httpx-0.25.2 orjson-3.9.10 pydantic-2.5.2 pydantic-core-2.14.5 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 sentence_transformers-2.2.2 sentencepiece-0.1.99 shellingham-1.5.4 starlette-0.27.0 tomlkit-0.12.0 typing-extensions-4.8.0 uvicorn-0.24.0.post1 websockets-11.0.3\n"
          ]
        }
      ],
      "source": [
        "%pip install gradio gensim sentence_transformers torch torchvision torchaudio -f https://download.pytorch.org/whl/cu111/torch_stable.html"
      ]
    },
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            ],
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n",
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import networkx as nx\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import linear_kernel\n",
        "from textblob import TextBlob\n",
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "import nltk\n",
        "from nltk import pos_tag\n",
        "from nltk.tokenize import word_tokenize\n",
        "from gensim.models import Word2Vec\n",
        "import spacy\n",
        "from sentence_transformers import SentenceTransformer, util\n",
        "import numpy as np\n",
        "import torch\n",
        "import gradio as gr\n",
        "import os\n",
        "import math\n",
        "from datetime import datetime\n",
        "\n",
        "# Use GPU\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Load a BERT model\n",
        "bert_model = SentenceTransformer('paraphrase-MiniLM-L6-v2', device=device)\n",
        "\n",
        "# Download NLTK resources (if not already downloaded)\n",
        "nltk.download('punkt')\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "nltk.download('vader_lexicon')\n",
        "\n",
        "# Load Spacy model for Named Entity Recognition (NER)\n",
        "nlp = spacy.load(\"en_core_web_sm\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "dG1jLPn5DXeb"
      },
      "outputs": [],
      "source": [
        "# Load Yelp dataset\n",
        "yelp_data = pd.read_csv('restaurants.csv')\n",
        "\n",
        "# Filter out relevant information (e.g., restaurant name, rating, location, categories, hours)\n",
        "restaurants = yelp_data[['name', 'stars', 'city', 'categories', 'hours']]\n",
        "\n",
        "comfort_food_terms = [\n",
        "    \"Home cooking\",\n",
        "    \"Soul food\",\n",
        "    \"Indulgent food\",\n",
        "    \"Feel-good food\",\n",
        "    \"Nostalgia food\",\n",
        "    \"Emotional food\",\n",
        "    \"Guilty pleasure\",\n",
        "    \"Indulgence\",\n",
        "    \"Treat\",\n",
        "    \"Culinary hug\",\n",
        "    \"Soul-soothing food\",\n",
        "    \"Heart-warming food\",\n",
        "]\n",
        "\n",
        "exciting_food_terms = [\n",
        "    \"Adventurous food\",\n",
        "    \"Exotic food\",\n",
        "    \"Culinary adventure\",\n",
        "    \"Sensual food\",\n",
        "    \"Tantalizing food\",\n",
        "    \"Mouthwatering food\",\n",
        "    \"Delectable food\",\n",
        "    \"Irresistible food\",\n",
        "    \"Tempting food\",\n",
        "    \"Gastronomical delight\",\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "Z05Z7XmOW5xe"
      },
      "outputs": [],
      "source": [
        "# Create a TF-IDF vectorizer to convert restaurant categories into numerical features\n",
        "tfidf_vectorizer = TfidfVectorizer(stop_words='english', lowercase=True)\n",
        "tfidf_matrix = tfidf_vectorizer.fit_transform(restaurants['categories']+\", \"+restaurants['city'].fillna(''))\n",
        "\n",
        "# Train Word2Vec model on restaurant names\n",
        "restaurant_names = [word_tokenize(restaurant['categories'].lower()+restaurant['city'].lower()) for _, restaurant in restaurants.iterrows()]\n",
        "word2vec_model = Word2Vec(sentences=restaurant_names, vector_size=100, window=5, min_count=1, workers=4)\n",
        "\n",
        "if os.path.isfile(\"./bert_embeddings.npy\"):\n",
        "    restaurant_embeddings = np.load('bert_embeddings.npy')\n",
        "else:\n",
        "    # Precompute restaurant embeddings on the chosen device for both categories and cities\n",
        "    restaurant_embeddings = [bert_model.encode((restaurant['categories'].lower() + restaurant['city'].lower()), convert_to_tensor=True).detach().cpu().numpy()\n",
        "                            for _, restaurant in restaurants.iterrows()]\n",
        "    np.save('bert_embeddings.npy', np.array(restaurant_embeddings))\n",
        "    restaurant_embeddings = np.load('bert_embeddings.npy')\n",
        "\n",
        "\n",
        "# Build a knowledge graph\n",
        "knowledge_graph = nx.Graph()\n",
        "\n",
        "# Add restaurant nodes with attributes\n",
        "for _, restaurant in restaurants.iterrows():\n",
        "    knowledge_graph.add_node(\n",
        "        restaurant['name'],\n",
        "        stars=restaurant['stars'],\n",
        "        city=restaurant['city'],\n",
        "        categories=restaurant['categories'],\n",
        "        hours=restaurant['hours']\n",
        "    )\n",
        "\n",
        "# Function to extract named entities from user input using Spacy NER\n",
        "def extract_named_entities(user_input):\n",
        "    doc = nlp(user_input)\n",
        "    named_entities = [(ent.text, ent.label_) for ent in doc.ents]\n",
        "    return named_entities\n",
        "\n",
        "# Function to perform sentiment analysis using VADER\n",
        "def analyze_sentiment_vader(text):\n",
        "    sid = SentimentIntensityAnalyzer()\n",
        "    compound_score = sid.polarity_scores(text)['compound']\n",
        "    return compound_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "wtd56Af2EI7h"
      },
      "outputs": [],
      "source": [
        "# Function to get restaurant recommendations based on Word Overlap\n",
        "def get_overlap_recommendations(user_input, num_recommendations):\n",
        "    overlap_scores = pd.DataFrame()\n",
        "    overlap_scores['combined_text'] = (restaurants['name'] + \", \" + restaurants['categories'] + \", \" + restaurants['city']).str.lower()\n",
        "    overlap_scores['overlap_score'] = overlap_scores['combined_text'].apply(lambda x: sum(word in user_input.lower() for word in x.replace(',', '').split()))\n",
        "    sorted_overlap_scores = overlap_scores.sort_values(by='overlap_score', ascending=False)\n",
        "    top_recommendations_indices = [idx for idx, _ in sorted_overlap_scores.head(num_recommendations).iterrows()]\n",
        "    return top_recommendations_indices\n",
        "\n",
        "\n",
        "# Function to get restaurant recommendations based on Word Embeddings similarity\n",
        "def get_embedding_recommendations(user_input, num_recommendations):\n",
        "    tokens = word_tokenize(user_input.lower())\n",
        "    embedding_similarities = {}\n",
        "\n",
        "    for i, restaurant in restaurants.iterrows():\n",
        "        restaurant_tokens = word_tokenize(restaurant['categories'].lower() + restaurant['city'].lower())\n",
        "        similarity = word2vec_model.wv.n_similarity(tokens, restaurant_tokens)\n",
        "        embedding_similarities[i] = similarity\n",
        "\n",
        "    # Sort the dictionary by similarity scores and get top recommendations\n",
        "    sorted_similarities = sorted(embedding_similarities.items(), key=lambda item: item[1], reverse=True)\n",
        "    top_recommendations_indices = [idx for idx, _ in sorted_similarities[:num_recommendations]]\n",
        "\n",
        "    return top_recommendations_indices\n",
        "\n",
        "# Function to get restaurant recommendations based on BERT embeddings similarity\n",
        "def get_bert_recommendations(user_input, num_recommendations):\n",
        "    user_embedding = bert_model.encode(user_input.lower(), convert_to_tensor=True).detach().cpu().numpy()\n",
        "    # user_embedding = np.mean(bert_model.encode(user_input.lower(), convert_to_tensor=True).detach().cpu().numpy(), axis=0)\n",
        "\n",
        "    bert_similarities = {}\n",
        "\n",
        "    for i, restaurant in restaurants.iterrows():\n",
        "        restaurant_embedding = restaurant_embeddings[i]\n",
        "        # print(restaurant_embedding, user_embedding)\n",
        "        # similarity = np.dot(user_embedding, restaurant_embedding) / (np.linalg.norm(user_embedding) * np.linalg.norm(restaurant_embedding))\n",
        "        similarity = util.cos_sim(user_embedding, restaurant_embedding)\n",
        "        bert_similarities[i] = similarity\n",
        "        # print(i, similarity)\n",
        "\n",
        "    # Sort the dictionary by similarity scores and get top recommendations\n",
        "    sorted_similarities = sorted(bert_similarities.items(), key=lambda item: item[1], reverse=True)\n",
        "    top_recommendations_indices = [idx for idx, _ in sorted_similarities[:num_recommendations]]\n",
        "\n",
        "    return top_recommendations_indices"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "gU0JhAg0EB3b"
      },
      "outputs": [],
      "source": [
        "# Function to recommend restaurants based on user input, sentiment, availability, and knowledge graph\n",
        "def recommend_restaurants(user_input, num_recommendations=5, sentiment_recursive_break=False):\n",
        "\n",
        "    sr = pd.DataFrame()\n",
        "\n",
        "    # Tokenize and perform POS tagging on the user input\n",
        "    tokens = word_tokenize(user_input)\n",
        "    pos_tags = pos_tag(tokens)\n",
        "\n",
        "    # Extract nouns and locations from POS tags\n",
        "    user_nouns = [word for word, pos in pos_tags if pos.startswith('N') or pos.startswith('J')]\n",
        "    user_locations = [word.lower().strip() for word, pos in pos_tags if pos.startswith('NNP')]  # Assume proper nouns are locations\n",
        "    # print(user_input)\n",
        "\n",
        "    # Extract named entities from user input\n",
        "    named_entities = extract_named_entities(user_input)\n",
        "    # print(\"Named Entities:\", named_entities)\n",
        "\n",
        "    # Transform user input into a TF-IDF vector\n",
        "    user_tfidf = tfidf_vectorizer.transform([user_input])\n",
        "\n",
        "    # Compute the cosine similarity between the user input and restaurant categories\n",
        "    cosine_similarities = linear_kernel(user_tfidf, tfidf_matrix).flatten()\n",
        "\n",
        "    # Get recommended restaurants from simple word overlap\n",
        "    overlap_recommendations = get_overlap_recommendations(user_input, num_recommendations)\n",
        "\n",
        "    # Get indices of restaurants with the highest similarity scores using TF-IDF\n",
        "    tfidf_recommendations = cosine_similarities.argsort()[:-num_recommendations-1:-1]\n",
        "\n",
        "    # Get recommendations using Word Embeddings\n",
        "    embedding_similarities = get_embedding_recommendations(user_input, num_recommendations)\n",
        "\n",
        "    # Get recommendations using BERT\n",
        "    bert_recommendations_indices = get_bert_recommendations(user_input, num_recommendations)\n",
        "\n",
        "    # Combine recommendations from both approaches\n",
        "    combined_recommendations = set(overlap_recommendations) | set(tfidf_recommendations) | set(embedding_similarities) |set(bert_recommendations_indices)\n",
        "    # print(\"TFIDF:\", tfidf_recommendations)\n",
        "    # print(\"overlap:\", overlap_recommendations)\n",
        "    # print(\"w2v:\", embedding_similarities)\n",
        "    # print(\"BERT:\", bert_recommendations_indices)\n",
        "\n",
        "    # Get details of recommended restaurants\n",
        "    recommended_restaurants = restaurants.iloc[list(combined_recommendations)]\n",
        "\n",
        "    # print(\"rec res:\", combined_recommendations)\n",
        "\n",
        "    # Refine recommendations based on extracted information, location, and named entities\n",
        "    for _, restaurant in recommended_restaurants.iterrows():\n",
        "      print(f\"Recommendation: {restaurant['name'], restaurant['city'], restaurant['categories'], restaurant['stars']} \")\n",
        "\n",
        "    if not sentiment_recursive_break:\n",
        "      # Perform sentiment analysis using VADER\n",
        "      sentiment_score = analyze_sentiment_vader(user_input)\n",
        "\n",
        "      if sentiment_score < -0.2:\n",
        "        print(\"Considering your negative sentiment, you might prefer comforting places.\")\n",
        "        sr = recommend_restaurants(user_input=user_input + \", \" + \", \".join(comfort_food_terms), num_recommendations=num_recommendations, sentiment_recursive_break=True)\n",
        "      elif sentiment_score > 0.2:\n",
        "        print(\"Considering your positive sentiment, you might prefer something exciting.\")\n",
        "        sr = recommend_restaurants(user_input=user_input + \", \" + \", \".join(exciting_food_terms), num_recommendations=num_recommendations, sentiment_recursive_break=True)\n",
        "      else:\n",
        "        print(\"neutral sentiment\")\n",
        "\n",
        "    # Rank the restaurants based on ratings in descending order\n",
        "    ranked_restaurants = recommended_restaurants.sort_values(by='stars', ascending=False).head(num_recommendations)\n",
        "\n",
        "    # return recommended restaurants for evaluation\n",
        "    return ranked_restaurants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aDZYCDQxfMFH"
      },
      "outputs": [],
      "source": [
        "# Example prompt\n",
        "user_prompt = input(\"Enter your restaurant preference (e.g., I want Chinese in Brooklyn): \")\n",
        "\n",
        "# Get recommendations based on the user's input, sentiment, availability, and knowledge graph\n",
        "recommend_restaurants(user_prompt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "asj5AxXvDjrY"
      },
      "outputs": [],
      "source": [
        "def evaluate_recommendations(predicted_df, filtered_df, k):\n",
        "    \"\"\"\n",
        "    Evaluate predicted restaurant recommendations against the ground truth using precision@k, recall@k, and F1@k.\n",
        "\n",
        "    Parameters:\n",
        "    - predicted_df (pd.DataFrame): DataFrame with predicted restaurant recommendations.\n",
        "    - filtered_df (pd.DataFrame): DataFrame with ground truth or actual relevant restaurants.\n",
        "    - k (int): Value of k for top-k recommendations.\n",
        "\n",
        "    Returns:\n",
        "    - precision_at_k (float): Precision@k.\n",
        "    - recall_at_k (float): Recall@k.\n",
        "    - f1_at_k (float): F1@k.\n",
        "    \"\"\"\n",
        "\n",
        "    # Extract the top-k predicted restaurants\n",
        "    top_k_predicted = predicted_df.head(k)\n",
        "\n",
        "    # Evaluate precision@k, recall@k, and F1@k\n",
        "    intersection = pd.merge(top_k_predicted, filtered_df, on=['name', 'stars', 'city', 'categories', 'hours'], how='inner')\n",
        "\n",
        "    precision_at_k = len(intersection) / k\n",
        "    recall_at_k = len(intersection) / len(filtered_df)\n",
        "    f1_at_k = 2 * (precision_at_k * recall_at_k) / (precision_at_k + recall_at_k) if (precision_at_k + recall_at_k) > 0 else 0\n",
        "\n",
        "    return precision_at_k, recall_at_k, f1_at_k"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n1Xrcarvzw9P"
      },
      "outputs": [],
      "source": [
        "cajun_in_neworleans = restaurants[\n",
        "    (restaurants['city'] == 'New Orleans') &\n",
        "    (restaurants['categories'].str.contains('cajun', case=False))\n",
        "]\n",
        "steakhouses_in_indiana = restaurants[\n",
        "    (restaurants['city'] == 'Indianapolis') &\n",
        "    (restaurants['categories'].str.contains('steakhouse', case=False))\n",
        "]\n",
        "chinese_in_philadelphia = restaurants[\n",
        "    (restaurants['city'] == 'Philadelphia') &\n",
        "    (restaurants['categories'].str.contains('chinese', case=False))\n",
        "]\n",
        "seafood_in_tampa = restaurants[\n",
        "    (restaurants['city'] == 'Tampa') &\n",
        "    (restaurants['categories'].str.contains('seafood', case=False))\n",
        "]\n",
        "italian_in_stlouis = restaurants[\n",
        "    (restaurants['city'] == 'Saint Louis') &\n",
        "    (restaurants['categories'].str.contains('italian', case=False))\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XEG8Eyh55lV7"
      },
      "outputs": [],
      "source": [
        "cino = recommend_restaurants(input())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Iarl1OLg6CW8"
      },
      "outputs": [],
      "source": [
        "print(evaluate_recommendations(cino, cajun_in_neworleans, 1))\n",
        "print(evaluate_recommendations(cino, cajun_in_neworleans, 5))\n",
        "print(evaluate_recommendations(cino, cajun_in_neworleans, 10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jQI7iAQeEDGr"
      },
      "outputs": [],
      "source": [
        "sit = recommend_restaurants(input())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G2QAD-o8E6HZ"
      },
      "outputs": [],
      "source": [
        "print(evaluate_recommendations(sit, seafood_in_tampa, 1))\n",
        "print(evaluate_recommendations(sit, seafood_in_tampa, 5))\n",
        "print(evaluate_recommendations(sit, seafood_in_tampa, 10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v7co493SGeWY"
      },
      "outputs": [],
      "source": [
        "sii = recommend_restaurants(input())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Te-YQGBzGl70"
      },
      "outputs": [],
      "source": [
        "print(evaluate_recommendations(sii, steakhouses_in_indiana, 1))\n",
        "print(evaluate_recommendations(sii, steakhouses_in_indiana, 5))\n",
        "print(evaluate_recommendations(sii, steakhouses_in_indiana, 10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ILgW8nsLHuwd"
      },
      "outputs": [],
      "source": [
        "iisl = recommend_restaurants(input())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nGi-VJfcH3tH"
      },
      "outputs": [],
      "source": [
        "print(evaluate_recommendations(iisl, italian_in_stlouis, 1))\n",
        "print(evaluate_recommendations(iisl, italian_in_stlouis, 5))\n",
        "print(evaluate_recommendations(iisl, italian_in_stlouis, 10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MNM-MiKwhN2I"
      },
      "outputs": [],
      "source": [
        "# rr = recommend_restaurants(prompt, number_of_recommendations = 5)\n",
        "\n",
        "demo = gr.Interface(fn=recommend_restaurants, inputs=[\"text\", gr.Number(value=10, precision=0, minimum=1)], outputs=\"dataframe\")\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    demo.launch(show_api=False, debug=True)"
      ]
    }
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