Search_Engine_Analytics_Vidhya.ipynb

{
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        "outputId": "22b58f2b-adc8-42e5-f531-90ff3c75ce32"
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          "text": [
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            "Installing collected packages: pydub, uvicorn, tomlkit, semantic-version, ruff, python-multipart, markupsafe, ffmpy, faiss-cpu, aiofiles, starlette, safehttpx, gradio-client, fastapi, gradio\n",
            "  Attempting uninstall: markupsafe\n",
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          ]
        }
      ],
      "source": [
        "# Install Required Libraries\n",
        "!pip install transformers sentence-transformers faiss-cpu gradio pandas\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
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        "id": "fDAfgCaW6Q-E",
        "outputId": "3fb3b795-c24f-4f38-ce74-6bcd28422164"
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              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving free_courses_analytics_vidhya.csv to free_courses_analytics_vidhya.csv\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "\n",
        "# Upload File\n",
        "uploaded = files.upload()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "Kglol_077FPV",
        "outputId": "0af7fba5-11a2-4188-b7b6-8394ee59d0c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9 entries, 0 to 8\n",
            "Data columns (total 3 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   Course Title  9 non-null      object\n",
            " 1   Description   9 non-null      object\n",
            " 2   Curriculum    9 non-null      object\n",
            "dtypes: object(3)\n",
            "memory usage: 344.0+ bytes\n",
            "None\n",
            "                                        Course Title  \\\n",
            "0           Frameworks for Effective Problem Solving   \n",
            "1           Anyone can Build AI Agents - Free Course   \n",
            "2  A Comprehensive Learning Path to Become a Data...   \n",
            "3  Reimagining GenAI: Common Mistakes and Best Pr...   \n",
            "4  Coding a ChatGPT-style Language Model from Scr...   \n",
            "\n",
            "                                         Description   Curriculum  \n",
            "0  This course provides structured approaches to ...   18 lessons  \n",
            "1  An introductory course designed to empower ind...    5 lessons  \n",
            "2  A detailed program outlining the essential ski...  298 lessons  \n",
            "3  This course delves into the common pitfalls in...    6 lessons  \n",
            "4  A hands-on course guiding learners through the...    7 lessons  \n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the uploaded file\n",
        "df = pd.read_csv('free_courses_analytics_vidhya.csv')\n",
        "\n",
        "# Display basic info and sample rows\n",
        "print(df.info())\n",
        "print(df.head())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "t2G3HOiU8ahg",
        "outputId": "7f30309a-c1c9-4d3b-974e-091f850f106d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cleaned dataset saved as 'cleaned_courses.csv'\n"
          ]
        }
      ],
      "source": [
        "import re\n",
        "\n",
        "# Text Preprocessing\n",
        "def preprocess(text):\n",
        "    text = str(text).lower()\n",
        "    text = re.sub(r'\\s+', ' ', text)  # Remove extra spaces\n",
        "    text = re.sub(r'[^\\w\\s]', '', text)  # Remove punctuation\n",
        "    return text.strip()\n",
        "\n",
        "# Apply preprocessing\n",
        "df['Title_clean'] = df['Course Title'].apply(preprocess)\n",
        "df['Description_clean'] = df['Description'].apply(preprocess)\n",
        "\n",
        "# Save cleaned data\n",
        "df.to_csv('cleaned_courses.csv', index=False)\n",
        "print(\"Cleaned dataset saved as 'cleaned_courses.csv'\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
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        "colab": {
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        },
        "id": "09ZVhv0F-Cu6",
        "outputId": "73ecb81f-882c-4871-cf51-79eeadd656b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "README.md:   0%|          | 0.00/10.7k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5966d20bc2f542d1a7f612334f1cbcce"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3a4ee566140f49db92008300edd573d2"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/350 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b487ed1746344d7c9fe27355606440e8"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bb591007fec040a98499c040b6ba419c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6952fbb4e8fd4b9b9edcfb4481b5af09"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2341a95e61ec4aca986c9c92e796a597"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "daab6fdfcb70433ca9a9c42735f936c6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8260c79f4ae243f1a65d32122c4e238b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Embeddings saved as 'course_embeddings.npy'\n"
          ]
        }
      ],
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "import numpy as np\n",
        "\n",
        "# Load SentenceTransformer Model\n",
        "model = SentenceTransformer('all-MiniLM-L6-v2')\n",
        "\n",
        "# Generate Embeddings\n",
        "embeddings = model.encode(df['Title_clean'] + ' ' + df['Description_clean'], show_progress_bar=True)\n",
        "\n",
        "# Save Embeddings\n",
        "np.save('course_embeddings.npy', embeddings)\n",
        "print(\"Embeddings saved as 'course_embeddings.npy'\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EhzTjsUw0sn-",
        "outputId": "323416ad-ffed-47da-9a2f-2a566c6098c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting faiss-cpu\n",
            "  Downloading faiss_cpu-1.9.0.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.4 kB)\n",
            "Requirement already satisfied: numpy<3.0,>=1.25.0 in /usr/local/lib/python3.10/dist-packages (from faiss-cpu) (1.26.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from faiss-cpu) (24.2)\n",
            "Downloading faiss_cpu-1.9.0.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (27.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m27.5/27.5 MB\u001b[0m \u001b[31m61.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: faiss-cpu\n",
            "Successfully installed faiss-cpu-1.9.0.post1\n",
            "FAISS index saved as 'courses_index.faiss'\n"
          ]
        }
      ],
      "source": [
        "!pip install faiss-cpu\n",
        "import faiss\n",
        "\n",
        "# Create FAISS Index\n",
        "dimension = embeddings.shape[1]\n",
        "index = faiss.IndexFlatL2(dimension)\n",
        "index.add(embeddings)\n",
        "\n",
        "# Save FAISS Index\n",
        "faiss.write_index(index, 'courses_index.faiss')\n",
        "print(\"FAISS index saved as 'courses_index.faiss'\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fdayrimJ02cL",
        "outputId": "e82a97dd-b960-440f-b1c1-7da4ccff5366"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Course Title': 'Reimagining GenAI: Common Mistakes and Best Practices for Success', 'Description': 'This course delves into the common pitfalls in Generative AI and offers best practices to achieve success in the field.'}\n",
            "{'Course Title': 'Mastering Multilingual GenAI Open-Weights for Indic Languages', 'Description': 'This course focuses on mastering Generative AI models tailored for Indic languages, emphasizing open-weight models.'}\n",
            "{'Course Title': 'GenAI Applied to Quantitative Finance: For Control Implementation', 'Description': 'This course applies Generative AI techniques to quantitative finance, focusing on control implementation strategies.'}\n",
            "{'Course Title': 'Learning Autonomous Driving Behaviors with LLMs & RL', 'Description': 'An advanced course exploring the integration of Large Language Models and Reinforcement Learning in autonomous driving.'}\n",
            "{'Course Title': 'Navigating LLM Tradeoffs: Techniques for Speed, Cost, Scale & Accuracy', 'Description': 'A course that examines the trade-offs in Large Language Models, providing techniques to balance speed, cost, scale, and accuracy.'}\n"
          ]
        }
      ],
      "source": [
        "def search_courses(query, top_k=5):\n",
        "    query_embedding = model.encode([preprocess(query)])\n",
        "    distances, indices = index.search(np.array(query_embedding).astype('float32'), top_k)\n",
        "\n",
        "    results = []\n",
        "    for idx in indices[0]:\n",
        "        if idx < len(df):\n",
        "            results.append({\n",
        "                'Course Title': df.iloc[idx]['Course Title'],\n",
        "                'Description': df.iloc[idx]['Description'],\n",
        "\n",
        "            })\n",
        "    return results\n",
        "\n",
        "# Test Search\n",
        "query = \"machine learning\"\n",
        "results = search_courses(query)\n",
        "for res in results:\n",
        "    print(res)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OlNmN-gK05UF",
        "outputId": "e812906c-d610-4d68-e2b8-d40e0d4e406f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio) (2.18.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.25.1->gradio) (3.4.0)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.25.1->gradio) (2.2.3)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0,>=0.12->gradio) (0.1.2)\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "* Running on public URL: https://355202bb7ec46edd2f.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://355202bb7ec46edd2f.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "!pip install gradio\n",
        "import gradio as gr\n",
        "\n",
        "def search_interface(query):\n",
        "    results = search_courses(query)\n",
        "    if not results:\n",
        "        return \"No relevant courses found. Try another keyword.\"\n",
        "\n",
        "    formatted_results = \"\\n\\n\".join(\n",
        "        [f\"**{res['Course Title']}**\\n{res['Description']}\\n[Link]({res.get('Link', '#')})\" for res in results]\n",
        "    )\n",
        "    return formatted_results\n",
        "\n",
        "# Launch Gradio Interface\n",
        "iface = gr.Interface(\n",
        "    fn=search_interface,\n",
        "    inputs=\"text\",\n",
        "    outputs=\"markdown\",\n",
        "    title=\"Smart Course Search\",\n",
        "    description=\"Enter a query to find relevant free courses on Analytics Vidhya.\"\n",
        ")\n",
        "\n",
        "iface.launch(share=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4FsC3zhg9is5",
        "outputId": "606a471b-7a49-4086-9f42-5123a9aed19c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distances: [[1.4496169 1.8257225 1.8651056 1.8797674 1.8815625]]\n",
            "Indices: [[0 6 3 1 2]]\n"
          ]
        }
      ],
      "source": [
        "query_embedding = model.encode([preprocess(\"solving\")])\n",
        "distances, indices = index.search(np.array(query_embedding).astype('float32'), 5)\n",
        "\n",
        "print(\"Distances:\", distances)\n",
        "print(\"Indices:\", indices)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RYiI6S1y13TS",
        "outputId": "d1cc84be-c0d4-4742-d636-758508506c8b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Course Title': 'Frameworks for Effective Problem Solving', 'Description': 'This course provides structured approaches to problem-solving, equipping learners with frameworks to tackle complex challenges effectively.'}\n",
            "{'Course Title': 'Learning Autonomous Driving Behaviors with LLMs & RL', 'Description': 'An advanced course exploring the integration of Large Language Models and Reinforcement Learning in autonomous driving.'}\n",
            "{'Course Title': 'Reimagining GenAI: Common Mistakes and Best Practices for Success', 'Description': 'This course delves into the common pitfalls in Generative AI and offers best practices to achieve success in the field.'}\n",
            "{'Course Title': 'Anyone can Build AI Agents - Free Course', 'Description': 'An introductory course designed to empower individuals to create AI agents, regardless of their prior experience.'}\n",
            "{'Course Title': 'A Comprehensive Learning Path to Become a Data Analyst in 2025', 'Description': 'A detailed program outlining the essential skills and knowledge required to pursue a career as a data analyst by 2025.'}\n"
          ]
        }
      ],
      "source": [
        "query = \"solving\"\n",
        "results = search_courses(query)\n",
        "for res in results:\n",
        "    print(res)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "C7JGkM6g1VF8",
        "outputId": "29d745c4-bcb2-44d2-b593-26f0ce336469"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": [
              "download(\"download_31e14124-2dc5-4389-94bd-692151b5bc45\", \"cleaned_courses.csv\", 3365)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": [
              "download(\"download_cdfd491f-1dfe-4754-8049-73b71b6f2010\", \"course_embeddings.npy\", 13952)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": [
              "download(\"download_01c80f9d-9818-4f4e-8aef-7c8cc4f58add\", \"courses_index.faiss\", 13869)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from google.colab import files\n",
        "\n",
        "files.download('cleaned_courses.csv')\n",
        "files.download('course_embeddings.npy')\n",
        "files.download('courses_index.faiss')\n"
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