diff --git a/week4/community-contributions/Cosmus_Week_4_Exercise.ipynb b/week4/community-contributions/Cosmus_Week_4_Exercise.ipynb
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+++ b/week4/community-contributions/Cosmus_Week_4_Exercise.ipynb
@@ -0,0 +1,5056 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "5jxNws-rH9PW",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "31f1965a-109e-45fb-d98b-26a423715051"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/355.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m355.0/355.0 kB\u001b[0m \u001b[31m26.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q gradio anthropic transformers accelerate torch"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_F_ZosNqJXI7",
+        "outputId": "6498d792-917a-43f6-af8e-4fc31ebb5b71"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting langdetect\n",
+            "  Downloading langdetect-1.0.9.tar.gz (981 kB)\n",
+            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/981.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m981.5/981.5 kB\u001b[0m \u001b[31m56.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: six in /usr/local/lib/python3.12/dist-packages (from langdetect) (1.17.0)\n",
+            "Building wheels for collected packages: langdetect\n",
+            "  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993223 sha256=18c137bfcb690d13f02e6af464fe4f8ca86895c21396c8f9c4e239b6186496e1\n",
+            "  Stored in directory: /root/.cache/pip/wheels/c1/67/88/e844b5b022812e15a52e4eaa38a1e709e99f06f6639d7e3ba7\n",
+            "Successfully built langdetect\n",
+            "Installing collected packages: langdetect\n",
+            "Successfully installed langdetect-1.0.9\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install langdetect"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "hfCPxcIwIGMk"
+      },
+      "outputs": [],
+      "source": [
+        "# basic imports for everything\n",
+        "import os\n",
+        "import re       # for cleaning up text\n",
+        "import gradio as gr  # UI library\n",
+        "from transformers import pipeline\n",
+        "from langdetect import detect     # simple lang detection"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
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+        "id": "XO4b5423ILMx",
+        "outputId": "f76b0559-deb4-4c91-f394-5f2e3a9d3de0"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json:   0%|          | 0.00/735 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "461c5b9e87114d5eb613390c28aec02a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "`torch_dtype` is deprecated! Use `dtype` instead!\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors.index.json: 0.00B [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": [
+              "Fetching 2 files:   0%|          | 0/2 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "73d86ed0009e432fa509c2e73a9085ba"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model-00002-of-00002.safetensors:   0%|          | 0.00/564M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "093874d38f4f4e58a6d82d9add9682c6"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model-00001-of-00002.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
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+          },
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+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer_config.json: 0.00B [00:00, ?B/s]"
+            ],
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "vocab.json: 0.00B [00:00, ?B/s]"
+            ],
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+              "version_major": 2,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "merges.txt: 0.00B [00:00, ?B/s]"
+            ],
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+            }
+          },
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+        {
+          "output_type": "display_data",
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+            "text/plain": [
+              "tokenizer.json: 0.00B [00:00, ?B/s]"
+            ],
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+          },
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+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "added_tokens.json: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "special_tokens_map.json:   0%|          | 0.00/99.0 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "217f7b1cb0554493a0cf324e2fa4cb89"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Device set to use cuda:0\n"
+          ]
+        }
+      ],
+      "source": [
+        "local_model = pipeline(\n",
+        "    \"text-generation\",\n",
+        "    # any of the model, here we use this hugging face mode phi-2 but you can use phi-1.5 if you want even lighter like Langdetect installed above\n",
+        "    model=\"microsoft/phi-2\",\n",
+        "    torch_dtype=\"auto\",\n",
+        "    device_map=\"auto\",\n",
+        "    max_new_tokens=250,\n",
+        "    do_sample=True,\n",
+        "    temperature=0.3\n",
+        ")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "LxZN8q6LIXI9"
+      },
+      "outputs": [],
+      "source": [
+        "# detecting the language\n",
+        "def detect_language(code):\n",
+        "    # common detection using keywords for each language\n",
+        "    if any(k in code for k in [\"def \", \"import \", \"print(\", \"lambda\"]):\n",
+        "        return \"python\"\n",
+        "    elif any(k in code for k in [\"#include\", \"int main\", \"cout\"]):\n",
+        "        return \"cpp\"\n",
+        "    elif any(k in code for k in [\"function \", \"console.log\"]):\n",
+        "        return \"javascript\"\n",
+        "    else:\n",
+        "        return \"unknown\"\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "TuwECHJnIdMd"
+      },
+      "outputs": [],
+      "source": [
+        "def add_comments_to_code(code):\n",
+        "    # if input is empty\n",
+        "    if not code.strip():\n",
+        "        return \"Please paste some code to add comments.\"\n",
+        "\n",
+        "    # gt the languagee\n",
+        "    language = detect_language(code)\n",
+        "    if language == \"unknown\":\n",
+        "        return \"Could not detect programming language. Output may be limited.\"\n",
+        "\n",
+        "    # Tell the model/system what we want and how\n",
+        "    prompt = (\n",
+        "        f\"You are a coding assistant. \"\n",
+        "        f\"Add inline comments to this Python code.\"\n",
+        "        f\"Explain all assignments, exec calls, and dictionary manipulations.\"\n",
+        "        f\"Do not change logic, only add comments.\"\n",
+        "        f\"Add concise inline comments and docstrings to this {language} code. \"\n",
+        "        f\"Do NOT change the logic, only add comments to make it understandable.\\n\\n\"\n",
+        "        f\"Code:\\n{code}\\n\\nImproved code with comments:\\n\"\n",
+        "    )\n",
+        "\n",
+        "    # run model\n",
+        "    result = local_model(\n",
+        "        prompt,\n",
+        "        max_new_tokens=400,\n",
+        "        do_sample=True,\n",
+        "        temperature=0.3\n",
+        "    )[0][\"generated_text\"]\n",
+        "\n",
+        "    # remove the echoed prompt (from the first attemp most models had a lot of echo, thus the need to do away with the repetition)\n",
+        "    cleaned = result.replace(prompt, \"\").strip()\n",
+        "\n",
+        "    # remove duplicate lines\n",
+        "    lines = cleaned.splitlines()\n",
+        "    deduped = []\n",
+        "    for line in lines:\n",
+        "        if not deduped or line.strip() != deduped[-1].strip():\n",
+        "            deduped.append(line)\n",
+        "\n",
+        "    return \"\\n\".join(deduped)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 610
+        },
+        "id": "v_fWQtsZIhGR",
+        "outputId": "00fea56a-5d9f-42ca-bf0f-c5061763200a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
+            "* Running on public URL: https://8e7f36c8560f1b5cb6.gradio.live\n",
+            "\n",
+            "This share link expires in 1 week. 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://8e7f36c8560f1b5cb6.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": 8
+        }
+      ],
+      "source": [
+        "# Our grado UX\n",
+        "with gr.Blocks(theme=\"soft\") as demo:\n",
+        "    gr.Markdown(\"## Code Commenter & Interpreter\")\n",
+        "    gr.Markdown(\"Paste your code below, click **Analyze**, and view the rewritten code with human-like comments.\")\n",
+        "\n",
+        "    # input\n",
+        "    code_input = gr.Textbox(\n",
+        "        label=\"Paste your code here\",\n",
+        "        lines=12,\n",
+        "        placeholder=\"Write or paste any code snippet...\",\n",
+        "        elem_id=\"code_box\"\n",
+        "    )\n",
+        "\n",
+        "    # button to click\n",
+        "    analyze_btn = gr.Button(\"Analyze Code\", variant=\"primary\")\n",
+        "\n",
+        "    # output area\n",
+        "    rewritten_out = gr.Code(\n",
+        "        label=\"Rewritten Code with Human-Like Comments\",\n",
+        "        language=\"python\",\n",
+        "        lines=14\n",
+        "    )\n",
+        "\n",
+        "    # fn to link the button with\n",
+        "    analyze_btn.click(fn=add_comments_to_code, inputs=code_input, outputs=rewritten_out)\n",
+        "\n",
+        "# launch app\n",
+        "demo.launch(share=True)\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
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+    "kernelspec": {
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