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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-4.7.1 gradio-client-0.7.0 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 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" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd" + ], + "metadata": { + "id": "EbGInAxxP8aW" + }, + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "movie_df=pd.read_csv(\"/content/hf_model_batch_18.csv\")\n", + "movie_df.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "p57QMbaKRlFl", + "outputId": "f16b1952-d371-4674-ae57-933ef37f54ec" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " id title \\\n", + "0 862 Toy Story \n", + "1 8844 Jumanji \n", + "2 15602 Grumpier Old Men \n", + "3 31357 Waiting to Exhale \n", + "4 11862 Father of the Bride Part II \n", + "\n", + " description \\\n", + "0 ['Animation', 'Comedy', 'Family', 'jealousy', ... \n", + "1 ['Adventure', 'Fantasy', 'Family', 'boardgame'... \n", + "2 ['Romance', 'Comedy', 'fishing', 'bestfriend',... \n", + "3 ['Comedy', 'Drama', 'Romance', 'basedonnovel',... \n", + "4 ['Comedy', 'baby', 'midlifecrisis', 'confidenc... \n", + "\n", + " embeddings \n", + "0 [[ 0.05446825 -0.03699033 -0.10390072 ... -0.0... \n", + "1 [[-0.21005818 -0.40819925 0.13977951 ... 0.0... \n", + "2 [[-0.12938595 -0.33542976 -0.0563906 ... -0.4... \n", + "3 [[-0.04338249 -0.10099783 -0.27223217 ... -0.0... \n", + "4 [[-6.4681962e-02 -1.0727557e-01 -3.0013630e-01... 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18844Jumanji['Adventure', 'Fantasy', 'Family', 'boardgame'...[[-0.21005818 -0.40819925 0.13977951 ... 0.0...
215602Grumpier Old Men['Romance', 'Comedy', 'fishing', 'bestfriend',...[[-0.12938595 -0.33542976 -0.0563906 ... -0.4...
331357Waiting to Exhale['Comedy', 'Drama', 'Romance', 'basedonnovel',...[[-0.04338249 -0.10099783 -0.27223217 ... -0.0...
411862Father of the Bride Part II['Comedy', 'baby', 'midlifecrisis', 'confidenc...[[-6.4681962e-02 -1.0727557e-01 -3.0013630e-01...
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\n" + ] + }, + "metadata": {}, + "execution_count": 3 + } + ] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "from transformers import AutoTokenizer, AutoModel\n", + "import pandas as pd\n", + "import numpy as np\n", + "from tqdm import tqdm\n", + "\n", + "prompt = \"Action movie with a strong female lead\"\n", + "tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\n", + "model = AutoModel.from_pretrained('bert-base-uncased')\n", + "\n", + "# Create a function to get embeddings\n", + "def get_embedding_batch(descriptions):\n", + " tokens = tokenizer(descriptions, return_tensors='pt', padding=True, truncation=True)\n", + " with torch.no_grad():\n", + " output = model(**tokens)\n", + " return output.last_hidden_state.mean(dim=1).squeeze().detach().numpy()\n", + "\n", + "embed=get_embedding_batch(prompt)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 232, + "referenced_widgets": [ + "e6cc6e8ca6e54dab804fbe200f89206e", + "87cad8cafc9b420d804270adeb3cb854", + "3dd82ec77aa84bee940c46644804b100", + "fa99874b1a584e12b3f5e81810b84894", + "6e0c40fe6b154d7b84e4c1cc26cab01d", + "c3edcaf11023440d81d4e4ee8f587361", + "9d83dd5be2a440119f1724a36b29b8f2", + "ef5ebabb56e74054acbe8969e581a9b9", + "8ed0f223bd7a4b93b55579018a99d08d", + "82e48c2d96d3495b81fb8803fb2c9693", + "ce0c66d7f3df48bdb1e812baae92acd0", + "09ca6a9f260a493eadfc816119b6acb4", + "e135189e94cf43269010962a641788b4", + "c1c289de23f14aed959f818fd418ae7a", + "ff443c5bad2447f3b6aaa93faf42162a", + "a63ba814f2354d4b8e244441d0c991cf", + "9fff1fde8ba14f3297ae057cb6523871", + "d87a9bdcd3a84f77b4f37853c7454b44", + "93dcd80eb3a44dab9ec2a56fa28fe3e8", + "9bfb7c31a9214049b8e8d2c977f7cf89", + "b8426f9cf1354797aa3ee91d12f0d323", + "eb7a55952c694195a5eb2e0c3f0d6c60", + "a3a1d81a02fe496aa5a9b39ef2836fc6", + "99ba22a9d3f44bfaa769d7ed1f6e3d73", + "2258c12308c34a009f32d7f06a736cf1", + "e49911c3c07c465593f781a588c0472e", + "9912101cc3d24cad9d2102a589c134f6", + "619ae84ba276465b8c956213f2342e82", + "627f56f67bb64e29a3adfc1c8661d723", + "e74bcdccf0b04580bac8944153e7eba2", + "a28a87eda74a400d96bcc593ed0a232d", + "d4b28dbae58542c48ed652fe571621e4", + "9d678f96b7e3486a86533a5c2711e550", + "2d6725eaf2f24f689db9be14224868a9", + "3b0bf8334e904cf59f1cb02dcd929404", + "ddf12c2a185245cb934a24df336d834f", + "42ff1a883c45420e81a45057df6e0b91", + "de2e0931bf144a508aea4afcace315da", + "0a9b58ec71684a72a3c42a8f5bbf5944", + "aa2f9fc582944121adaef29e56deeba9", + "813a78389bc1472099bd824895e8bc26", + "614e49d6a6474d03a5100d681f6511fc", + "8ae100fa677d420bafdb99979b912742", + "6338b5cc3b0f459e9585e539d77246a6", + "2b973c73f1e546d5982e99ed242887a8", + "fe9ee50c52864410ac5adfce0d02206a", + "c9448dee39fe45528f7c8cfa725e4d47", + "b964cd1b1d874966957ffab3f5a7594a", + "a1515d49b0774a69ab8db7ec11e202b1", + "6d4b8874007d40c899f8a8c242a8de71", + "582d33c9f4fd41d0a151747624ad2493", + "9a5d3a4d39034ab8910bc522e8da19f7", + "62f17581204c4278937a28c434956164", + "a768889374f94cecbf42d46abd9630b7", + "a5782ad278cb46acafa7d87ff7440e7e" + ] + }, + "id": "mFYC7i7kPXqb", + "outputId": "a0ffec1f-7f94-49d5-b8b3-cb4b3a314474" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_runtime.py:184: UserWarning: Pydantic is installed but cannot be imported. Please check your installation. `huggingface_hub` will default to not using Pydantic. Error message: '{e}'\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/28.0 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;31m# Convert string to list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mprompt_embedding_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mliteral_eval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt_embedding_string_with_commas\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;31m# Now you can convert the list to a NumPy array and use it as needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'ast' is not defined" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics.pairwise import cosine_similarity\n", + "import ast\n", + "\n", + "movie_df['embeddings'] = movie_df['embeddings'].str.replace('\\.\\.\\.', ',')\n", + "movie_df['embeddings'] = movie_df['embeddings'].apply(ast.literal_eval)\n", + "\n", + "# Convert the embeddings column to a NumPy array\n", + "movie_embeddings = np.array(movie_df['embeddings'].tolist())\n", + "\n", + "# Calculate cosine similarity between the prompt embedding and movie embeddings\n", + "prompt_embedding = get_embedding_batch(prompt)\n", + "cosine_similarities = cosine_similarity([prompt_embedding], movie_embeddings)\n", + "\n", + "top_movie_indices = np.argsort(cosine_similarities[0])[::-1][:3]\n", + "\n", + "# Display the top three movies\n", + "top_movies = movie_df.iloc[top_movie_indices][['id', 'title', 'description']]\n", + "print(\"Top Three Movies Related to the Prompt:\")\n", + "print(top_movies)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 609 + }, + "id": "HUXLFAiEPfWz", + "outputId": "93af769d-e77c-438b-a043-f8cead606286" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":4: FutureWarning: The default value of regex will change from True to False in a future version.\n", + " movie_df['embeddings'] = movie_df['embeddings'].str.replace('\\.\\.\\.', ',')\n" + ] + }, + { + "output_type": "error", + "ename": "SyntaxError", + "evalue": "ignored", + "traceback": [ + "Traceback \u001b[0;36m(most recent call last)\u001b[0m:\n", + " File \u001b[1;32m\"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\"\u001b[0m, line \u001b[1;32m3553\u001b[0m, in \u001b[1;35mrun_code\u001b[0m\n exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \u001b[1;32m\"\"\u001b[0m, line \u001b[1;32m5\u001b[0m, in \u001b[1;35m\u001b[0m\n movie_df['embeddings'] = movie_df['embeddings'].apply(ast.literal_eval)\n", + " File \u001b[1;32m\"/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\"\u001b[0m, line \u001b[1;32m4771\u001b[0m, in \u001b[1;35mapply\u001b[0m\n return SeriesApply(self, func, convert_dtype, args, kwargs).apply()\n", + " File \u001b[1;32m\"/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\"\u001b[0m, line \u001b[1;32m1123\u001b[0m, in \u001b[1;35mapply\u001b[0m\n return self.apply_standard()\n", + " File \u001b[1;32m\"/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\"\u001b[0m, line \u001b[1;32m1174\u001b[0m, in \u001b[1;35mapply_standard\u001b[0m\n mapped = lib.map_infer(\n", + " File \u001b[1;32m\"pandas/_libs/lib.pyx\"\u001b[0m, line \u001b[1;32m2924\u001b[0m, in \u001b[1;35mpandas._libs.lib.map_infer\u001b[0m\n", + " File \u001b[1;32m\"/usr/lib/python3.10/ast.py\"\u001b[0m, line \u001b[1;32m64\u001b[0m, in \u001b[1;35mliteral_eval\u001b[0m\n node_or_string = parse(node_or_string.lstrip(\" \\t\"), mode='eval')\n", + "\u001b[0;36m File \u001b[0;32m\"/usr/lib/python3.10/ast.py\"\u001b[0;36m, line \u001b[0;32m50\u001b[0;36m, in \u001b[0;35mparse\u001b[0;36m\u001b[0m\n\u001b[0;31m return compile(source, filename, mode, flags,\u001b[0m\n", + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m [[ 0.05446825 -0.03699033 -0.10390072 , -0.09778773 0.04674733\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax. Perhaps you forgot a comma?\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "GRADIO CODE" + ], + "metadata": { + "id": "7eAEDqfMRGzV" + } + }, + { + "cell_type": "code", + "source": [ + "import gradio as gr\n", + "\n", + "def get_top_movies(prompt):\n", + "\n", + " movie_df['embeddings'] = movie_df['embeddings'].str.replace('\\.\\.\\.', ',')\n", + " movie_df['embeddings'] = movie_df['embeddings'].apply(ast.literal_eval)\n", + "\n", + " movie_embeddings = np.array(movie_df['embeddings'].tolist())\n", + "\n", + " prompt_embedding = get_embedding_batch(prompt)\n", + " cosine_similarities = cosine_similarity([prompt_embedding], movie_embeddings)\n", + "\n", + " top_movie_indices = np.argsort(cosine_similarities[0])[::-1][:3]\n", + "\n", + " top_movies = movie_df.iloc[top_movie_indices][['id', 'title', 'description']]\n", + " return top_movies.to_dict(orient='records')\n", + "\n", + "# Gradio Interface\n", + "iface = gr.Interface(\n", + " fn=get_top_movies,\n", + " inputs=\"text\",\n", + " outputs=gr.Table(columns=[\"id\", \"title\", \"description\"]),\n", + " live=True,\n", + " interpretation=\"default\"\n", + ")\n", + "\n", + "iface.launch()" + ], + "metadata": { + "id": "mJ_r3aptRIvg" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file