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            "Requirement already satisfied: sentence-transformers in /usr/local/lib/python3.12/dist-packages (5.4.1)\n",
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            "Requirement already satisfied: annotated-doc>=0.0.2 in /usr/local/lib/python3.12/dist-packages (from typer->huggingface-hub>=0.23.0->sentence-transformers) (0.0.4)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->huggingface-hub>=0.23.0->sentence-transformers) (4.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->huggingface-hub>=0.23.0->sentence-transformers) (2.20.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich>=12.3.0->typer->huggingface-hub>=0.23.0->sentence-transformers) (0.1.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install sentence-transformers matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "import gradio as gr\n",
        "\n",
        "model = SentenceTransformer(\"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\")"
      ],
      "metadata": {
        "colab": {
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        "id": "Vp3ROniOSEy8",
        "outputId": "33eed2b1-37cc-4058-812b-c77ffcda9da0"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
            "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/229 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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              "model_id": "7b57b427473d46e8884717d9e914d1c4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/122 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5038b253d77a44f39c3460cf7ee3edf6"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md: 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": [
              "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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        {
          "output_type": "display_data",
          "data": {
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            ],
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              "version_minor": 0,
              "model_id": "962c1a4f38e644ec9a8f1a96b28f45d4"
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        {
          "output_type": "display_data",
          "data": {
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              "model.safetensors:   0%|          | 0.00/471M [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading weights:   0%|          | 0/199 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c0936770e64246f9b91d29f7596dd21b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "BertModel LOAD REPORT from: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\n",
            "Key                     | Status     |  | \n",
            "------------------------+------------+--+-\n",
            "embeddings.position_ids | UNEXPECTED |  | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4edebfb067af49a0ad65b3281c8c611e"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5b86a1ab65f94121a239330c23b4ed45"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. Definicja kategorii i ustalenie ich reprezentacji wektorowych"
      ],
      "metadata": {
        "id": "XKTqhd1yUD4w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "categories = {\n",
        "    \"pozytywna\": [\n",
        "        \"To był świetny produkt.\",\n",
        "        \"Bardzo polecam, wszystko super.\",\n",
        "        \"Jestem zadowolony.\",\n",
        "        \"Naprawdę dobra jakość.\",\n",
        "        \"Gra była bardzo przyjemna.\",\n",
        "        \"Rewelacyjna rozgrywka i grafika.\",\n",
        "        \"Bardzo satysfakcjonująca mechanika gry.\",\n",
        "        \"Czułem dużo frajdy podczas grania.\"\n",
        "    ],\n",
        "    \"negatywna\": [\n",
        "        \"Nie polecam, bardzo słaba jakość.\",\n",
        "        \"To była strata pieniędzy.\",\n",
        "        \"Jestem niezadowolony.\",\n",
        "        \"Znudziła mi się po jednym dniu.\",\n",
        "        \"Gra była nudna i frustrująca.\",\n",
        "        \"Rozgrywka była monotonna i męcząca.\",\n",
        "        \"Sterowanie działało bardzo źle.\",\n",
        "        \"Zawiodłem się całkowicie.\"\n",
        "    ]\n",
        "}\n",
        "\n",
        "category_vectors = {}\n",
        "for cat, examples in categories.items():\n",
        "  embeddings = model.encode(examples)\n",
        "  category_vectors[cat] = np.mean(embeddings, axis=0)"
      ],
      "metadata": {
        "id": "oethdHQiUJL5"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. Ocena ręcznie wpisanej opinii + gradio"
      ],
      "metadata": {
        "id": "APTIWLV5VRUg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def classify_opinion(text):\n",
        "  embedding = model.encode([text])[0]\n",
        "  sims = {cat: cosine_similarity([embedding], [vec])[0][0] for cat, vec in category_vectors.items()}\n",
        "  predicted = max(sims, key=sims.get)\n",
        "  response = f\" Opinia: {text}\\n\\n Klasyfikacja: {predicted.upper()}\\n\\n Podobieństwo:\\n\"\n",
        "  for cat, score in sims.items():\n",
        "    response += f\"-> {cat}: {score:.4f}\\n\"\n",
        "  return response"
      ],
      "metadata": {
        "id": "CD5mB_UiVVnc"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gr.Interface(\n",
        "    fn=classify_opinion,\n",
        "    inputs=gr.Textbox(label=\"Wpisz swoją opinię\"),\n",
        "    outputs=gr.Textbox(label=\"Wynik klasyfikacji\"),\n",
        "    title=\"Klasyfikator opinii\",\n",
        "    description=\"Wpisz opinię o grze lub produkcie, a model oceni, czy jest pozytywna czy negatywna.\"\n",
        ").launch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "YESD5PIOWykk",
        "outputId": "a8da324a-00c7-4f91-c52f-509372e0c548"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "It looks like you are running Gradio on a hosted Jupyter notebook, which requires `share=True`. Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "* Running on public URL: https://b942e68198ba3ba63e.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://b942e68198ba3ba63e.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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}