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            "Building wheels for collected packages: ffmpy\n",
            "  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=66278f5f042ec27007a137d28e44b605506059a368808f70369430d414a4e97a\n",
            "  Stored in directory: /root/.cache/pip/wheels/01/a6/d1/1c0828c304a4283b2c1639a09ad86f83d7c487ef34c6b4a1bf\n",
            "Successfully built ffmpy\n",
            "Installing collected packages: 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, 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-4.7.1 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",
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              "1   8844                      Jumanji   \n",
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              "3  31357            Waiting to Exhale   \n",
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              "\n",
              "                                         description  \\\n",
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              "1  ['Adventure', 'Fantasy', 'Family', 'boardgame'...   \n",
              "2  ['Romance', 'Comedy', 'fishing', 'bestfriend',...   \n",
              "3  ['Comedy', 'Drama', 'Romance', 'basedonnovel',...   \n",
              "4  ['Comedy', 'baby', 'midlifecrisis', 'confidenc...   \n",
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          "metadata": {},
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        "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)"
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      "outputs": [
        {
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          "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"
          ]
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              "       -9.97291654e-02, -1.95562527e-01,  3.93047780e-01, -4.50938791e-02,\n",
              "        1.54322192e-01,  2.10822508e-01, -3.79716754e-01, -1.02272816e-01,\n",
              "        7.65442103e-03, -6.05992079e-02, -6.37473345e-01,  4.43706959e-02,\n",
              "        9.98458862e-02, -7.03971758e-02, -5.69898821e-02, -2.66102254e-01,\n",
              "       -1.55915812e-01,  3.02103072e-01,  1.79690853e-01, -1.99591905e-01,\n",
              "        9.62557495e-02, -8.49790931e-01,  1.69913873e-01,  1.31870866e-01,\n",
              "       -7.43081328e-03,  1.32989913e-01,  6.16228320e-02, -3.82655174e-01,\n",
              "       -3.07241408e-03, -2.35067442e-01,  1.67000249e-01, -2.78636277e-01,\n",
              "        1.27227008e-01,  4.56134588e-01,  6.19722784e-01, -4.51611400e-01,\n",
              "       -1.90559775e-01, -3.63256574e-01,  3.61113280e-01, -1.08360164e-02,\n",
              "        4.18178022e-01,  3.21845710e-01,  2.74577618e-01, -3.31453204e-01,\n",
              "        8.42116401e-02, -4.70300317e-01,  2.08565682e-01, -7.80444860e-01,\n",
              "        2.40827382e-01,  2.38948032e-01,  1.69077516e-01, -6.44189060e-01,\n",
              "        2.84920409e-02, -1.76383585e-01,  2.23279238e-01, -3.05603873e-02,\n",
              "       -8.60854089e-02,  2.15163767e-01,  3.00386280e-01, -5.75605810e-01,\n",
              "        6.82834744e-01,  1.11185156e-01,  1.81152239e-01, -3.14303726e-01,\n",
              "        3.05607140e-01,  9.20956492e-01, -3.06648582e-01,  1.26254186e-01,\n",
              "       -3.62434238e-02,  7.98512161e-01, -2.14040756e-01, -4.42689002e-01,\n",
              "        1.03734862e-02, -1.03247754e-01, -2.87409186e-01,  2.28858307e-01,\n",
              "       -1.32991567e-01, -5.15891552e-01, -1.99588835e-01, -4.08264041e-01,\n",
              "       -1.89936012e-02, -3.67709011e-01,  1.17825747e-01, -2.02053368e-01,\n",
              "       -7.62870386e-02, -3.81076425e-01,  1.81393489e-01, -5.12889354e-03,\n",
              "        1.04082279e-01,  1.96312129e-01, -1.23248696e-01, -9.39335451e-02,\n",
              "        2.45729104e-01, -8.84499121e-03, -2.35737592e-01,  4.93178107e-02,\n",
              "        4.42034639e-02,  3.88600588e-01, -2.05273628e-01, -5.49539089e-01,\n",
              "        5.16481161e-01, -1.00423664e-01,  1.37615725e-01,  5.00032641e-02,\n",
              "        2.06677645e-01,  7.50004947e-02, -1.96295127e-01, -2.32280463e-01,\n",
              "        8.50900114e-02,  6.62259012e-02,  3.56951863e-01,  5.16624868e-01,\n",
              "       -3.88043344e-01,  5.61844647e-01, -2.51545727e-01, -2.14526653e-01,\n",
              "        6.58386722e-02,  4.96931553e-01, -1.06369503e-01, -2.23702297e-01,\n",
              "        3.52201402e-01, -1.96694076e-01, -5.28629541e-01,  3.23892921e-01,\n",
              "       -2.74001174e-02,  3.57092693e-02, -7.12462962e-01,  4.12782043e-01,\n",
              "        2.46262074e-01, -6.88750088e-01, -2.20990092e-01,  4.52084869e-01,\n",
              "        3.52851510e-01,  5.03242612e-01, -3.72931153e-01, -4.37055469e-01,\n",
              "       -6.69157505e-02,  3.52332473e-01, -1.47538722e-01,  3.74718219e-01,\n",
              "        4.99036126e-02, -1.73375741e-01, -2.04929277e-01, -5.49934566e-01,\n",
              "        5.73362298e-02, -2.89161682e-01,  3.90234798e-01, -9.44250971e-02,\n",
              "        1.97031423e-01,  2.40169257e-01,  3.26264575e-02,  2.14362994e-01,\n",
              "        2.60600775e-01, -1.23102255e-01,  5.97003043e-01,  3.01799595e-01,\n",
              "       -1.96088746e-01, -3.04002285e-01, -6.94885943e-03, -2.30561867e-01,\n",
              "       -1.21324368e-01, -1.77655190e-01,  4.24729675e-01, -2.57238716e-01,\n",
              "       -8.86839479e-02, -6.38331950e-01,  7.86318183e-02,  1.04086295e-01,\n",
              "       -5.60261369e-01, -2.59113729e-01,  4.76468317e-02,  7.62730315e-02,\n",
              "       -7.47193098e-01, -6.40896797e-01, -5.84631180e-03,  1.57346785e-01,\n",
              "       -3.68181825e-01, -1.42524272e-01,  1.68097585e-01, -2.12825835e-01,\n",
              "        1.69250056e-01,  9.63126495e-02,  2.14542195e-01,  1.55799970e-01,\n",
              "       -4.86067623e-01, -3.60030383e-01,  4.24702078e-01,  7.38505051e-02,\n",
              "       -2.49053821e-01, -4.09299396e-02,  1.91101968e-01, -5.62870353e-02,\n",
              "        5.05046248e-02,  5.26161551e-01, -1.23197585e-01,  6.08745217e-01,\n",
              "       -1.30760252e-01,  3.73686910e-01, -3.55867028e-01,  2.04907209e-02,\n",
              "       -3.47716838e-01, -1.44810975e-01,  1.01902775e-01, -8.46432269e-01,\n",
              "       -5.27928531e-01,  2.91747123e-01, -2.66961694e-01,  1.10958349e-02,\n",
              "       -1.29546896e-01, -2.07614154e-01,  3.06943264e-02, -7.08682165e-02,\n",
              "        1.82966232e-01, -7.53430948e-02,  3.03020835e-01,  4.28336203e-01,\n",
              "        2.26523370e-01,  6.99604228e-02, -4.20226865e-02, -4.33905721e-01,\n",
              "        2.21033439e-01, -2.03816026e-01, -3.16352248e-01,  2.67525107e-01,\n",
              "       -3.62144023e-01, -9.63813782e-01, -1.35405198e-01,  8.78465399e-02,\n",
              "        2.49296933e-01,  6.45450592e-01, -3.67236361e-02,  2.60394886e-02,\n",
              "        3.44522297e-01, -9.11867917e-02,  5.79271726e-02,  2.12892666e-01,\n",
              "        3.24503422e-01, -2.05285892e-01,  2.62803510e-02,  2.69833922e-01,\n",
              "       -7.52930790e-02, -1.51843399e-01,  2.41066199e-02,  1.88782752e-01,\n",
              "        3.54572564e-01, -2.02824045e-02, -8.54696333e-02, -1.72541142e-02,\n",
              "       -2.82748848e-01,  2.31051415e-01, -5.71999606e-03,  6.49686977e-02,\n",
              "       -2.52608895e-01,  1.94921896e-01, -4.98380549e-02, -3.03266674e-01,\n",
              "        5.08967519e-01,  5.87282240e-01, -3.35450560e-01, -4.14446503e-01,\n",
              "        1.98317021e-01,  4.51743335e-01,  1.49621874e-01, -2.16138437e-02,\n",
              "        2.30114058e-01,  1.90310895e-01,  1.98338494e-01,  2.74473488e-01,\n",
              "        3.85843843e-01, -4.48966861e-01,  3.23188156e-01, -6.55330896e-01,\n",
              "        3.58194023e-01,  3.12724471e-01, -1.00911152e+00,  1.68806136e-01,\n",
              "       -2.74739742e-01,  4.15381014e-01,  1.95109129e-01, -3.83707345e-01,\n",
              "       -1.80797100e-01,  2.20494837e-01, -3.54611039e-01, -7.58042708e-02,\n",
              "       -8.75804275e-02,  6.86799765e-01,  3.48484427e-01, -1.11455403e-01,\n",
              "        2.31406048e-01, -2.35931814e-01,  1.54536992e-01,  5.50654471e-01,\n",
              "        7.03026772e-01, -3.32810253e-01,  2.61391282e-01, -5.46195321e-02,\n",
              "        2.16451839e-01,  9.95179564e-02,  1.71807349e-01,  2.45050058e-01,\n",
              "        6.47257924e-01,  2.27008879e-01,  2.06983566e-01, -2.88225114e-01,\n",
              "        1.16693288e-01,  3.31514180e-01,  4.20706213e-01, -3.88130218e-01,\n",
              "        2.49405541e-02, -2.04542503e-01,  5.09207666e-01, -2.96194434e-01,\n",
              "        4.13545489e-01,  4.50500220e-01,  2.38020584e-01, -2.73393899e-01,\n",
              "       -4.99605864e-01, -3.82879615e-01, -1.03559852e-01, -3.15386392e-02,\n",
              "       -4.43671495e-01,  8.86142328e-02, -1.57858841e-02, -3.49003598e-02,\n",
              "       -2.59252787e-01, -1.62809685e-01, -4.76247430e-01, -2.91014135e-01,\n",
              "        4.11918998e-01,  2.72746027e-01, -5.20366907e-01, -9.65323150e-02,\n",
              "        2.12256253e-01, -6.92025661e-01, -2.49767542e-01, -2.02792026e-02,\n",
              "        2.63318837e-01, -5.35638094e-01,  1.84465602e-01, -3.02353175e-03,\n",
              "       -6.26779906e-03, -3.73644233e-01, -7.46308714e-02, -1.96713597e-01,\n",
              "        5.74655175e-01,  5.75465083e-01,  1.96377784e-01,  6.54747725e-01,\n",
              "        5.31009883e-02,  2.75098123e-02, -3.98811072e-01,  1.77533567e-01,\n",
              "        2.44624779e-01, -1.22765571e-01, -3.53191346e-01,  4.12638158e-01,\n",
              "       -4.25908238e-01, -6.32878184e-01,  2.00456247e-01,  2.26450205e-01,\n",
              "       -7.82289267e-01,  2.41746292e-01,  2.47303694e-01,  1.44023791e-01,\n",
              "        5.22288978e-01,  9.38877761e-02,  1.78719535e-02,  6.13665767e-02,\n",
              "       -6.06968552e-02, -1.52218372e-01,  3.04150358e-02, -1.13224080e-02,\n",
              "       -7.23096907e-01, -1.48629650e-01, -1.98480949e-01,  5.65028787e-01,\n",
              "        5.10084093e-01, -9.79003906e-01, -7.16118589e-02,  2.59715915e-01,\n",
              "        3.34665179e-01,  1.65671602e-01,  1.20420724e-01, -8.68218318e-02,\n",
              "       -2.78593153e-01,  4.13182288e-01, -6.26657486e-01, -3.20610106e-01,\n",
              "        1.19245440e-01, -1.12262607e-01,  1.10299230e-01,  2.48476923e-01,\n",
              "       -1.42650330e+00, -3.55258465e-01,  2.24189699e-01,  1.00760097e-02,\n",
              "        7.29218662e-01, -3.61272722e-01,  2.99315691e-01, -2.30474308e-01,\n",
              "       -8.15051198e-02, -1.08105883e-01, -2.83472747e-01,  2.55942076e-01,\n",
              "        3.30552846e-01, -4.58603173e-01,  2.41501808e-01, -4.07792628e-01],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We were getting an error in comparing the similarities between the prompts due to some mismatch in the type of embeddings of movies description and prompt."
      ],
      "metadata": {
        "id": "duJdl3bqUBJF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "# String representation of prompt embedding without commas\n",
        "prompt_embedding_string = movie_df[\"embeddings\"][0]\n",
        "# Add commas between numbers\n",
        "prompt_embedding_string_with_commas = re.sub(r'(?<=\\d)\\s(?=-?\\d)', ',', prompt_embedding_string)\n",
        "\n",
        "# Convert string to list\n",
        "prompt_embedding_list = ast.literal_eval(prompt_embedding_string_with_commas)\n",
        "\n",
        "# Now you can convert the list to a NumPy array and use it as needed\n",
        "prompt_embedding = np.array(prompt_embedding_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "id": "fk-Dmm1dP1Gn",
        "outputId": "051bea03-5aca-454b-e80f-b1213da3dad0"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-7aef1b3ee898>\u001b[0m in \u001b[0;36m<cell line: 9>\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": [
            "<ipython-input-8-2db79a622765>: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\"<ipython-input-8-2db79a622765>\"\u001b[0m, line \u001b[1;32m5\u001b[0m, in \u001b[1;35m<cell line: 5>\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\"<unknown>\"\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": []
    }
  ]
}