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"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" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "colab": { - "base_uri": "https://localhost:8080/" + "base_uri": "https://localhost:8080/", + "height": 503, + "referenced_widgets": [ + "d904e1689ec44647b32c1e7e83aa33aa", + "1bc45abd855e4d858816abf7d25a3eae", + "94e4c2d81dc84529b5f03f89bc6d6388", + "2c8d28585e464dc6a6a2624f3aab4f4b", + "7b95aedb3bc3493bad30a77c8e7d8b9a", + "3035589a9a42461480e8bc96d01b5c44", + "354a695bc30540e79cb3b1381487fe47", + "0e11e7e9065a468c8bbb70e091488dfc", + "14e03043d18d4a3f8ff2b6ad61acc319", + "94263b0c69ab431ab644b2b297f97174", + "36965e092a4146d89971404bb6134a03", + "aba167a5b08d468b921765706b617508", + "448bf011eab048f2aefd2528fbf50638", + "027a9ffabebb4066b4476f0df9773400", + "53b85fa78fc6492bb57d7ce98cda395a", + "a362a028d76a46f29aedddfd61b579de", + "74a62c32ca744914a5bbe8549911d084", + 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num_recommendations):\n", " overlap_scores = pd.DataFrame()\n", @@ -247,7 +690,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "id": "gU0JhAg0EB3b" }, @@ -291,7 +734,6 @@ "\n", " # Combine recommendations from both approaches\n", " combined_recommendations = set(overlap_recommendations) | set(tfidf_recommendations) | set(embedding_similarities) |set(bert_recommendations_indices)\n", - " combined_recommendations = set(embedding_similarities)\n", " # print(\"TFIDF:\", tfidf_recommendations)\n", " # print(\"overlap:\", overlap_recommendations)\n", " # print(\"w2v:\", embedding_similarities)\n", @@ -304,9 +746,7 @@ "\n", " # Refine recommendations based on extracted information, location, and named entities\n", " for _, restaurant in recommended_restaurants.iterrows():\n", - " # if any(category in restaurant['categories'] for category in user_nouns) and any(location in restaurant['city'] for location in user_locations):\n", - " # if any(location in restaurant['city'].lower().strip() for location in user_locations):\n", - " print(f\"Recommendation: {restaurant['name'], restaurant['city'], restaurant['categories'], restaurant['stars']} \")\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", @@ -315,15 +755,9 @@ " 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", - " # comforting_places = restaurants[restaurants['categories'].str.contains('comfort food', case=False, na=False)]\n", - " # print(\"Comforting food places suggestions:\")\n", - " # print(comforting_places[['name', 'stars', 'city', 'categories']])\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", - " # exciting_places = restaurants[restaurants['categories'].str.contains('nightlife|arts & entertainment|restaurants', case=False, na=False)]\n", - " # print(\"Exciting food places suggestions:\")\n", - " # print(exciting_places[['name', 'stars', 'city', 'categories']])\n", " else:\n", " print(\"neutral sentiment\")\n", "\n", @@ -515,57 +949,9 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "background_save": true, - "base_uri": "https://localhost:8080/", - "height": 830 - }, - "id": "MNM-MiKwhN2I", - "outputId": "31abcb6c-b9bc-4096-f012-bf12ee7a1e24" + "id": "MNM-MiKwhN2I" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n", - "\n", - "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n", - "Running on public URL: https://4a62505eb450484ce0.gradio.live\n", - "\n", - "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n" - ] - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Recommendation: (\"Charlie Gitto's On the Hill\", 'St. Louis', 'Restaurants, Italian', 4.5) \n", - "Recommendation: (\"Pietro's\", 'St. Louis', 'Italian, Restaurants', 4.0) \n", - "Recommendation: ('Cluster Busters', 'St. Louis', 'Italian, Restaurants, Seafood', 4.0) \n", - "Recommendation: ('Cibare Italian Kitchen', 'St. Louis', 'Restaurants, Italian', 4.0) \n", - "Recommendation: ('Toscana Pizza, Pasta & More', 'St. Petersburg', 'Italian, Restaurants, Pizza', 4.0) \n", - "Recommendation: (\"Del Pietro's\", 'St. Louis', 'Italian, Restaurants', 4.0) \n", - "Recommendation: (\"Sophia's Cucina + Enoteca\", 'St. Petersburg', 'Italian, Restaurants', 4.0) \n", - "Recommendation: (\"Moscato's Bella Cucina\", 'St. Petersburg', 'Italian, Restaurants', 3.5) \n", - "Recommendation: ('Bici Trattoria', 'St. Petersburg', 'Italian, Restaurants', 4.0) \n", - "Recommendation: ('Goodcents Deli Fresh Subs', 'St. Louis', 'Restaurants, Sandwiches', 4.0) \n", - "neutral sentiment\n" - ] - } - ], + "outputs": [], "source": [ "# rr = recommend_restaurants(prompt, number_of_recommendations = 5)\n", "\n", @@ -574,112 +960,6 @@ "if __name__ == \"__main__\":\n", " demo.launch(show_api=False, debug=True)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Dx0SmXB8qGln" - }, - "outputs": [], - "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", - "# import nltk\n", - "# from nltk import pos_tag\n", - "# from nltk.tokenize import word_tokenize\n", - "# from datetime import datetime\n", - "\n", - "# # Download NLTK resources (if not already downloaded)\n", - "# nltk.download('punkt')\n", - "# nltk.download('averaged_perceptron_tagger')\n", - "\n", - "# # Load Yelp dataset (replace 'yelp_dataset.csv' with your actual dataset file)\n", - "# yelp_data = pd.read_csv('yelp_dataset.csv')\n", - "\n", - "# # Load existing knowledge graph (replace 'knowledge_graph.gexf' with your actual graph file)\n", - "# knowledge_graph = nx.read_gexf('knowledge_graph.gexf')\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", - "# # 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'].fillna(''))\n", - "\n", - "# # Function to check if a restaurant is open at the current time\n", - "# def is_restaurant_open(hours, current_time):\n", - "# for day, hours_range in hours.items():\n", - "# start_time, end_time = hours_range.split('-')\n", - "# if start_time <= current_time <= end_time:\n", - "# return True\n", - "# return False\n", - "\n", - "# # Function to get similar foods from the knowledge graph\n", - "# def get_similar_foods(category, knowledge_graph):\n", - "# similar_foods = set()\n", - "\n", - "# if category in knowledge_graph.nodes:\n", - "# neighbors = list(knowledge_graph.neighbors(category))\n", - "# similar_foods.update(neighbors)\n", - "\n", - "# return similar_foods\n", - "\n", - "# # Function to recommend restaurants based on content-based filtering and availability\n", - "# def recommend_restaurants(user_input, num_recommendations=5):\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 for word, pos in pos_tags if pos.startswith('NNP')] # Assume proper nouns are locations\n", - "\n", - "# # Filter restaurants based on the user's location\n", - "# location_filtered_restaurants = restaurants[restaurants['city'].isin(user_locations)]\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 indices of restaurants with highest similarity scores\n", - "# restaurant_indices = cosine_similarities.argsort()[:-num_recommendations-1:-1]\n", - "\n", - "# # Get recommended restaurants\n", - "# recommended_restaurants = restaurants.iloc[restaurant_indices]\n", - "\n", - "# # Refine recommendations based on extracted information and location\n", - "# for _, restaurant in recommended_restaurants.iterrows():\n", - "# if any(category in restaurant['categories'] for category in user_nouns) and any(location in restaurant['city'] for location in user_locations):\n", - "# print(f\"Refined recommendation: {restaurant['name']} based on type of food and location.\")\n", - "\n", - "# # Get similar foods from the knowledge graph\n", - "# additional_categories = set()\n", - "# for user_noun in user_nouns:\n", - "# similar_foods = get_similar_foods(user_noun, knowledge_graph)\n", - "# additional_categories.update(similar_foods)\n", - "\n", - "# # Update the categories column with similar foods\n", - "# updated_categories = ', '.join(set(restaurant['categories'].split(', ') + list(additional_categories)))\n", - "# print(f\"Updated Categories: {updated_categories}\")\n", - "\n", - "# # Check restaurant availability based on current time\n", - "# current_time = datetime.now().strftime(\"%H:%M\")\n", - "# if is_restaurant_open(restaurant['hours'], current_time):\n", - "# print(f\"{restaurant['name']} is open right 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