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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/issa-kabore/SmartSummarizer/blob/main/Sandbox_Text_Summarization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "crpsoEoflSah"
+      },
+      "source": [
+        "# 📝 Multilingual Text Summarization (French + English)\n",
+        "\n",
+        "## 📘 Context\n",
+        "\n",
+        "Text summarization is a crucial NLP task used to extract key insights from long documents. With the advancement of transformer-based architectures like BART and T5, we can now generate high-quality summaries in different languages.\n",
+        "\n",
+        "This notebook demonstrates how to perform automatic summarization in:\n",
+        "- 🇬🇧 **English**, using `facebook/bart-large-cnn`\n",
+        "- 🇫🇷 **French**, using `plguillou/t5-base-fr-sum-cnndm`\n",
+        "\n",
+        "## 🎯 Objectives\n",
+        "\n",
+        "- Load and compare language-specific summarization models\n",
+        "- Generate and display summaries for both English and French input texts\n",
+        "- Test edge cases and observe model behavior"
+      ],
+      "id": "crpsoEoflSah"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Packages"
+      ],
+      "metadata": {
+        "id": "R5HB8zbHmcG-"
+      },
+      "id": "R5HB8zbHmcG-"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PxUScsLGlSak"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install transformers sentencepiece"
+      ],
+      "id": "PxUScsLGlSak"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "tXp3V8bclSal"
+      },
+      "outputs": [],
+      "source": [
+        "# import loguru\n",
+        "\n",
+        "from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM\n",
+        "import textwrap  # Text wrapping and filling"
+      ],
+      "id": "tXp3V8bclSal"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🧠 Model Descriptions\n",
+        "\n",
+        "### 🇬🇧 `facebook/bart-large-cnn` — English Text Summarization\n",
+        "\n",
+        "**BART (Bidirectional and Auto-Regressive Transformer)** is a model developed by Facebook AI that combines the strengths of **encoder-decoder** architectures (like T5) and **auto-regressive** models (like GPT). It is **fine-tuned** on the **CNN/DailyMail dataset**, consisting of articles and summaries.\n",
+        "\n",
+        "- **Use Case**: Excellent for **journalistic**, **informal**, or **structured opinion texts**.\n",
+        "- **Type of Summary**: **Abstractive** (paraphrasing, not just extraction).\n",
+        "\n",
+        "**Architecture**:\n",
+        "- 12 layers of encoder + 12 layers of decoder\n",
+        "- Bidirectional attention for encoding, causal attention for decoding\n",
+        "- Around **406M parameters**\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### 🇫🇷 `plguillou/t5-base-fr-sum-cnndm` — French Text Summarization\n",
+        "\n",
+        "Based on **T5 (Text-to-Text Transfer Transformer)**, developed by Google. This model is **fine-tuned** for **French text summarization** on a dataset inspired by CNN/DailyMail.\n",
+        "\n",
+        "- **Use Case**: Best for **formal** or **structured** texts: **news articles**, **reports**, or **official documents**.\n",
+        "- **Type of Summary**: **Abstractive** (rephrasing the input text in its own words).\n",
+        "\n",
+        "**Architecture**:\n",
+        "- **T5-base**: Around **220M parameters**\n",
+        "- Multilingual, but fine-tuned specifically for **French**.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### 🌍 `facebook/mbart-large-50-one-to-many-mmt` — Multilingual Text Summarization\n",
+        "\n",
+        "**mBART (Multilingual BART)** is a variation of the BART model that is trained on **multiple languages**. It is designed for **translation** tasks but can also be adapted for **summarization**.\n",
+        "\n",
+        "- **Use Case**: Suitable for summarizing text in multiple languages, making it a versatile tool for multilingual applications.\n",
+        "- **Type of Summary**: **Abstractive**.\n",
+        "\n",
+        "**Architecture**:\n",
+        "- 12 layers of encoder + 12 layers of decoder\n",
+        "- Multilingual model trained on 50 languages\n",
+        "- Around **680M parameters**\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### 🔄 `google/t5-base-xxl-tlm` — T5 for Multilingual Tasks\n",
+        "\n",
+        "**T5** (Text-to-Text Transfer Transformer) is a model that frames all NLP tasks as a text-to-text problem, making it highly adaptable. It has been fine-tuned for multiple tasks including **summarization**.\n",
+        "\n",
+        "- **Use Case**: Works well for **multilingual summarization**, but can also be used for translation, question-answering, etc.\n",
+        "- **Type of Summary**: **Abstractive** (like all T5-based models).\n",
+        "\n",
+        "**Architecture**:\n",
+        "- **T5-base**: Around **220M parameters**\n",
+        "- **T5-XXL**: Much larger, up to **11B parameters**\n",
+        "- Fine-tuned for many multilingual tasks\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### 🚀 `google/flan-t5-xl` — Fine-tuned T5 for Better Generalization\n",
+        "\n",
+        "**FLAN-T5** is a version of T5 that is **fine-tuned on a variety of tasks** to improve generalization. It aims to perform better on a wide range of NLP tasks, including summarization, when compared to regular T5.\n",
+        "\n",
+        "- **Use Case**: Ideal for **high-quality summarization** tasks in multiple languages, with improved robustness.\n",
+        "- **Type of Summary**: **Abstractive**.\n",
+        "\n",
+        "**Architecture**:\n",
+        "- **T5-XL**: Large model with **11B parameters**.\n",
+        "- Fine-tuned on a wide variety of tasks, improving the model's ability to generalize across domains.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### 📊 Quick Comparison\n",
+        "\n",
+        "| Model                         | Language      | Architecture        | Fine-Tuning            | Type of Summary |\n",
+        "|-------------------------------|---------------|---------------------|------------------------|-----------------|\n",
+        "| `facebook/bart-large-cnn`      | English       | BART                | CNN/DailyMail          | Abstractive     |\n",
+        "| `plguillou/t5-base-fr-sum`     | French        | T5 (Base)           | CNN/DailyMail FR       | Abstractive     |\n",
+        "| `facebook/mbart-large-50`      | Multilingual  | mBART               | Multilingual (50 languages) | Abstractive |\n",
+        "| `google/t5-base-xxl-tlm`       | Multilingual  | T5 (Base or XXL)    | Multilingual           | Abstractive     |\n",
+        "| `google/flan-t5-xl`            | Multilingual  | T5 (Fine-tuned)     | Fine-tuned for better generalization | Abstractive |\n"
+      ],
+      "metadata": {
+        "id": "ZxqM3H8sm_Lc"
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+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/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": {
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+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "84794e42774b4fb990c0217c8ac98af0"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "added_tokens.json:   0%|          | 0.00/31.0 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "db6220820a4a40f29c5ae790a36ce263"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "special_tokens_map.json:   0%|          | 0.00/1.79k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "3a70d53ae38a4e10818c2130da9c9e05"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "pytorch_model.bin:   0%|          | 0.00/892M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e12581585f6d403980a271c2a545d722"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/892M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "77b6dc7272914b3d8d38f1672e202e32"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Device set to use cpu\n"
+          ]
+        }
+      ],
+      "source": [
+        "# English summarization model (BART)\n",
+        "summarizer_en = pipeline(\"summarization\", model=\"facebook/bart-large-cnn\")\n",
+        "\n",
+        "# French summarization model (T5 fine-tuned for summarization)\n",
+        "fr_model_name = \"plguillou/t5-base-fr-sum-cnndm\"\n",
+        "tokenizer_fr = AutoTokenizer.from_pretrained(fr_model_name)\n",
+        "model_fr = AutoModelForSeq2SeqLM.from_pretrained(fr_model_name)\n",
+        "summarizer_fr = pipeline(\"summarization\", model=model_fr, tokenizer=tokenizer_fr)"
+      ],
+      "id": "iiGL50dWlSal"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🧪 Application"
+      ],
+      "metadata": {
+        "id": "f25CHUCbnJMY"
+      },
+      "id": "f25CHUCbnJMY"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "FwNVa6_llSal"
+      },
+      "outputs": [],
+      "source": [
+        "text_en = \"\"\"\n",
+        "Artificial Intelligence is revolutionizing many industries such as healthcare, finance, and transportation.\n",
+        "Machine learning techniques now enable systems to analyze vast amounts of data and make decisions with minimal human input.\n",
+        "However, these advances raise concerns over data privacy, algorithmic transparency, and job displacement.\n",
+        "\"\"\"\n",
+        "\n",
+        "text_fr = \"\"\"\n",
+        "L’intelligence artificielle transforme profondément des secteurs comme la santé, les transports et l’éducation.\n",
+        "Grâce à l’apprentissage automatique, les systèmes peuvent analyser de grandes quantités de données et prendre des décisions complexes.\n",
+        "Cependant, cela soulève des enjeux éthiques majeurs sur la transparence, l’emploi et la confidentialité.\n",
+        "\"\"\""
+      ],
+      "id": "FwNVa6_llSal"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# help(summarizer_en)"
+      ],
+      "metadata": {
+        "id": "mMoruEIRnUcf"
+      },
+      "id": "mMoruEIRnUcf",
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FpVfGp0ulSam",
+        "outputId": "e4bf1c6d-5c67-417d-831b-1972028d9c82"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Your max_length is set to 100, but your input_length is only 63. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=31)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "🔹 Original English Text:\n",
+            "\n",
+            " Artificial Intelligence is revolutionizing many industries such as healthcare, finance, and\n",
+            "transportation.  Machine learning techniques now enable systems to analyze vast amounts of data and\n",
+            "make decisions with minimal human input. However, these advances raise concerns over data privacy,\n",
+            "algorithmic transparency, and job displacement.\n",
+            "\n",
+            "✅ English Summary:\n",
+            "\n",
+            "Artificial Intelligence is revolutionizing many industries such as healthcare, finance, and\n",
+            "transportation. However, these advances raise concerns over data privacy, algorithmic transparency,\n",
+            "and job displacement.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"🔹 Original English Text:\\n\")\n",
+        "print(textwrap.fill(text_en, width=100))\n",
+        "\n",
+        "summary_en = summarizer_en(text_en, max_length=100, min_length=30 do_sample=False)\n",
+        "\n",
+        "print(\"\\n✅ English Summary:\\n\")\n",
+        "print(textwrap.fill(summary_en[0][\"summary_text\"], width=100))"
+      ],
+      "id": "FpVfGp0ulSam"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Hcr5dtB5lSam",
+        "outputId": "766c13d2-6fc4-4f64-a6e8-4a298b8b4614"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "🔹 Texte original en français:\n",
+            "\n",
+            " L’intelligence artificielle transforme profondément des secteurs comme la santé, les transports et\n",
+            "l’éducation.  Grâce à l’apprentissage automatique, les systèmes peuvent analyser de grandes\n",
+            "quantités de données et prendre des décisions complexes.  Cependant, cela soulève des enjeux\n",
+            "éthiques majeurs sur la transparence, l’emploi et la confidentialité.\n",
+            "\n",
+            "✅ Résumé en français:\n",
+            "\n",
+            "L'intelligence artificielle transforme profondément des secteurs tels que la santé, les transports\n",
+            "et l'éducation. Cependant, cela soulève des enjeux éthiques majeurs sur la transparence, l’emploi\n",
+            "eet la confidentialité. L'apprentissage automatique permet aux systèmes de prendre des décisions\n",
+            "complexes.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"🔹 Texte original en français:\\n\")\n",
+        "print(textwrap.fill(text_fr, width=100))\n",
+        "\n",
+        "summary_fr = summarizer_fr(text_fr, max_length=100, min_length=30, do_sample=False)\n",
+        "\n",
+        "print(\"\\n✅ Résumé en français:\\n\")\n",
+        "print(textwrap.fill(summary_fr[0][\"summary_text\"], width=100))"
+      ],
+      "id": "Hcr5dtB5lSam"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lcaWmvwNlSan",
+        "outputId": "1b85a579-7c91-4c62-ec4c-2a5a57d1e5db"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Your max_length is set to 50, but your input_length is only 3. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=1)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "🧪 Edge Case (Empty input):\n",
+            "CNN.com will feature iReporter photos in a weekly Travel Snapshots gallery. Please submit your best shots of New York for next week. Visit CNN.com/Travel next Wednesday for a new gallery of snapshots.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"🧪 Edge Case (Empty input):\")\n",
+        "\n",
+        "# handle with clause if else"
+      ],
+      "id": "lcaWmvwNlSan"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ipja6tLnlSan",
+        "outputId": "5c205a71-1e51-4738-8d08-535b9abb1036"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Your max_length is set to 60, but your input_length is only 24. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=12)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "🔁 Résumés français (batch):\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Your max_length is set to 60, but your input_length is only 32. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=16)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "📌 Texte: Le réchauffement climatique provoque des événements météorologiques extrêmes dans le monde entier.\n",
+            "➡️ Résumé: Le réchauffement climatique provoque des événements météorologiques extrêmes dans le monde entier.\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Your max_length is set to 60, but your input_length is only 33. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=16)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "📌 Texte: La France accueille chaque année des millions de touristes attirés par sa culture et sa gastronomie.\n",
+            "➡️ Résumé: La France accueille chaque année des millions de touristes attirés par la culture et la gastronomie. La culture française est l'une des principales attractions touristiques françaises.\n",
+            "\n",
+            "📌 Texte: Les véhicules autonomes utilisent des capteurs et de l'IA pour se déplacer sans conducteur humain.\n",
+            "➡️ Résumé: Les véhicules autonomes utilisent des capteurs et de l'IA pour se déplacer sans conducteur humain.\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "texts_fr = [\n",
+        "    \"Le réchauffement climatique provoque des événements météorologiques extrêmes dans le monde entier.\",\n",
+        "    \"La France accueille chaque année des millions de touristes attirés par sa culture et sa gastronomie.\",\n",
+        "    \"Les véhicules autonomes utilisent des capteurs et de l'IA pour se déplacer sans conducteur humain.\"\n",
+        "]\n",
+        "\n",
+        "print(\"🔁 Résumés français (batch):\\n\")\n",
+        "for t in texts_fr:\n",
+        "    s = summarizer_fr(t, max_length=60, min_length=20, do_sample=False)\n",
+        "    print(f\"📌 Texte: {t}\\n➡️ Résumé: {s[0]['summary_text']}\\n\")"
+      ],
+      "id": "ipja6tLnlSan"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        " ## 🔍 Auto detect text language"
+      ],
+      "metadata": {
+        "id": "0cA3DEJtq7J2"
+      },
+      "id": "0cA3DEJtq7J2"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install langdetect"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "poBwmUOUrPxB",
+        "outputId": "6d44963f-1157-4a54-f1f3-39b94a18b40e"
+      },
+      "id": "poBwmUOUrPxB",
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: langdetect in /usr/local/lib/python3.11/dist-packages (1.0.9)\n",
+            "Requirement already satisfied: six in /usr/local/lib/python3.11/dist-packages (from langdetect) (1.17.0)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from langdetect import detect\n",
+        "\n",
+        "texts = [\n",
+        "    \"Bonjour, comment allez-vous ?\",           # French\n",
+        "    \"Hello, how are you doing?\",               # English\n",
+        "    \"Hola, ¿cómo estás?\",                      # Spanish\n",
+        "    \"Guten Tag, wie geht's Ihnen?\",            # German\n",
+        "    \"\",                                        # Empty\n",
+        "    \"こんにちは、お元気ですか？\",                # Japanese\n",
+        "    \"1234567890 $$$ ???\",                      # Gibberish\n",
+        "]\n",
+        "\n",
+        "for text in texts:\n",
+        "    try:\n",
+        "        lang = detect(text)\n",
+        "        print(f\"📝 Text: {text}\\n➡️ Language detected: {lang}\\n\")\n",
+        "    except:\n",
+        "        print(f\"📝 Text: {text}\\n❌ Could not detect language\\n\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "s90NKwiNrLeF",
+        "outputId": "57c6b9f0-ab2f-4d42-9f0d-7acdd225a082"
+      },
+      "id": "s90NKwiNrLeF",
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "📝 Text: Bonjour, comment allez-vous ?\n",
+            "➡️ Language detected: fr\n",
+            "\n",
+            "📝 Text: Hello, how are you doing?\n",
+            "➡️ Language detected: en\n",
+            "\n",
+            "📝 Text: Hola, ¿cómo estás?\n",
+            "➡️ Language detected: es\n",
+            "\n",
+            "📝 Text: Guten Tag, wie geht's Ihnen?\n",
+            "➡️ Language detected: de\n",
+            "\n",
+            "📝 Text: \n",
+            "❌ Could not detect language\n",
+            "\n",
+            "📝 Text: こんにちは、お元気ですか？\n",
+            "➡️ Language detected: ja\n",
+            "\n",
+            "📝 Text: 1234567890 $$$ ???\n",
+            "❌ Could not detect language\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Functions"
+      ],
+      "metadata": {
+        "id": "dpCSrgL5r0tG"
+      },
+      "id": "dpCSrgL5r0tG"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from transformers import pipeline\n",
+        "from langdetect import detect\n",
+        "\n",
+        "# Load English and French summarizers\n",
+        "summarizer_en = pipeline(\"summarization\",\n",
+        "                         model=\"facebook/bart-large-cnn\")\n",
+        "fr_model_name = \"plguillou/t5-base-fr-sum-cnndm\"\n",
+        "tokenizer_fr = AutoTokenizer.from_pretrained(fr_model_name)\n",
+        "model_fr = AutoModelForSeq2SeqLM.from_pretrained(fr_model_name)\n",
+        "summarizer_fr = pipeline(\"summarization\",\n",
+        "                         model=model_fr, tokenizer=tokenizer_fr)\n",
+        "\n",
+        "\n",
+        "def detect_language(text):\n",
+        "    try:\n",
+        "        return detect(text)\n",
+        "    except:\n",
+        "        return \"unknown\"\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SKb4od1Irehc",
+        "outputId": "a29a7a3f-ed10-42c9-8faf-84de06118bd5"
+      },
+      "id": "SKb4od1Irehc",
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Device set to use cpu\n",
+            "Device set to use cpu\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def summarize_text(text):\n",
+        "    if not text or text.strip() == \"\":\n",
+        "        return \"⚠️ No input text provided.\"\n",
+        "\n",
+        "    if len(text.split()) < 5:\n",
+        "        return f\"⚠️ Text too short to summarize. Returning input:\\n{text}\"\n",
+        "\n",
+        "    lang = detect_language(text)\n",
+        "\n",
+        "    max_len = max(130, len(text.split()))\n",
+        "    min_len = min(30, len(text.split()))\n",
+        "\n",
+        "    if lang == \"en\":\n",
+        "        summary = summarizer_en(text, max_length=max_len, min_length=min_len, do_sample=False)\n",
+        "        return f\"🇬🇧 English Summary:\\n{summary[0]['summary_text']}\"\n",
+        "\n",
+        "    elif lang == \"fr\":\n",
+        "        summary = summarizer_fr(text, max_length=max_len, min_length=min_len, do_sample=False)\n",
+        "        return f\"🇫🇷 Résumé Français :\\n{summary[0]['summary_text']}\"\n",
+        "\n",
+        "    else:\n",
+        "        return f\"❌ Unsupported or undetected language: '{lang}'\"\n",
+        "\n",
+        "\n",
+        "text = \"Bonjour, voici un exemple de texte que nous allons résumer automatiquement.\"\n",
+        "print(summarize_text(text))\n",
+        "\n",
+        "text = \"This is an example of a paragraph that we want to summarize using LLMs.\"\n",
+        "print(summarize_text(text))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "R6ChZ4pGr6eP",
+        "outputId": "aee3098c-4c65-4fc4-a553-cb164137c62d"
+      },
+      "id": "R6ChZ4pGr6eP",
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Your max_length is set to 130, but your input_length is only 19. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=9)\n",
+            "Your max_length is set to 130, but your input_length is only 18. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=9)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "🇫🇷 Résumé Français :\n",
+            "Voici un exemple de texte que nous allons résumer automatiquement.\n",
+            "🇬🇧 English Summary:\n",
+            "This is an example of a paragraph that we want to summarize using LLMs.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "text = \"Bonjour, voici un exemple de texte que nous allons résumer automatiquement.\"\n",
+        "detect_language(text)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 36
+        },
+        "id": "DuSZxnQUuBv6",
+        "outputId": "b82bcd65-adfc-4717-820c-1141d0fc6747"
+      },
+      "id": "DuSZxnQUuBv6",
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'fr'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "3duRlP58sKws"
+      },
+      "id": "3duRlP58sKws",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install gradio"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BHXVI4XjsKtJ",
+        "outputId": "30306664-4464-4554-98d7-326b1be7c1c6"
+      },
+      "id": "BHXVI4XjsKtJ",
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting gradio\n",
+            "  Downloading gradio-5.29.0-py3-none-any.whl.metadata (16 kB)\n",
+            "Collecting aiofiles<25.0,>=22.0 (from gradio)\n",
+            "  Downloading aiofiles-24.1.0-py3-none-any.whl.metadata (10 kB)\n",
+            "Requirement already satisfied: anyio<5.0,>=3.0 in /usr/local/lib/python3.11/dist-packages (from gradio) (4.9.0)\n",
+            "Collecting fastapi<1.0,>=0.115.2 (from gradio)\n",
+            "  Downloading fastapi-0.115.12-py3-none-any.whl.metadata (27 kB)\n",
+            "Collecting ffmpy (from gradio)\n",
+            "  Downloading ffmpy-0.5.0-py3-none-any.whl.metadata (3.0 kB)\n",
+            "Collecting gradio-client==1.10.0 (from gradio)\n",
+            "  Downloading gradio_client-1.10.0-py3-none-any.whl.metadata (7.1 kB)\n",
+            "Collecting groovy~=0.1 (from gradio)\n",
+            "  Downloading groovy-0.1.2-py3-none-any.whl.metadata (6.1 kB)\n",
+            "Requirement already satisfied: httpx>=0.24.1 in /usr/local/lib/python3.11/dist-packages (from gradio) (0.28.1)\n",
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+            "Requirement already satisfied: pydantic<2.12,>=2.0 in /usr/local/lib/python3.11/dist-packages (from gradio) (2.11.4)\n",
+            "Collecting pydub (from gradio)\n",
+            "  Downloading pydub-0.25.1-py2.py3-none-any.whl.metadata (1.4 kB)\n",
+            "Collecting python-multipart>=0.0.18 (from gradio)\n",
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+            "Requirement already satisfied: pyyaml<7.0,>=5.0 in /usr/local/lib/python3.11/dist-packages (from gradio) (6.0.2)\n",
+            "Collecting ruff>=0.9.3 (from gradio)\n",
+            "  Downloading ruff-0.11.8-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (25 kB)\n",
+            "Collecting safehttpx<0.2.0,>=0.1.6 (from gradio)\n",
+            "  Downloading safehttpx-0.1.6-py3-none-any.whl.metadata (4.2 kB)\n",
+            "Collecting semantic-version~=2.0 (from gradio)\n",
+            "  Downloading semantic_version-2.10.0-py2.py3-none-any.whl.metadata (9.7 kB)\n",
+            "Collecting starlette<1.0,>=0.40.0 (from gradio)\n",
+            "  Downloading starlette-0.46.2-py3-none-any.whl.metadata (6.2 kB)\n",
+            "Collecting tomlkit<0.14.0,>=0.12.0 (from gradio)\n",
+            "  Downloading tomlkit-0.13.2-py3-none-any.whl.metadata (2.7 kB)\n",
+            "Requirement already satisfied: typer<1.0,>=0.12 in /usr/local/lib/python3.11/dist-packages (from gradio) (0.15.3)\n",
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+            "Collecting uvicorn>=0.14.0 (from gradio)\n",
+            "  Downloading uvicorn-0.34.2-py3-none-any.whl.metadata (6.5 kB)\n",
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+            "Requirement already satisfied: typing-inspection>=0.4.0 in /usr/local/lib/python3.11/dist-packages (from pydantic<2.12,>=2.0->gradio) (0.4.0)\n",
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+            "Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.11/dist-packages (from typer<1.0,>=0.12->gradio) (1.5.4)\n",
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+            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.11/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/python3.11/dist-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio) (2.19.1)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.28.1->gradio) (3.4.1)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.28.1->gradio) (2.4.0)\n",
+            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.11/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0,>=0.12->gradio) (0.1.2)\n",
+            "Downloading gradio-5.29.0-py3-none-any.whl (54.1 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.1/54.1 MB\u001b[0m \u001b[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading gradio_client-1.10.0-py3-none-any.whl (322 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m322.9/322.9 kB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading aiofiles-24.1.0-py3-none-any.whl (15 kB)\n",
+            "Downloading fastapi-0.115.12-py3-none-any.whl (95 kB)\n",
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+            "\u001b[?25hDownloading groovy-0.1.2-py3-none-any.whl (14 kB)\n",
+            "Downloading python_multipart-0.0.20-py3-none-any.whl (24 kB)\n",
+            "Downloading ruff-0.11.8-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.5 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.5/11.5 MB\u001b[0m \u001b[31m79.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading safehttpx-0.1.6-py3-none-any.whl (8.7 kB)\n",
+            "Downloading semantic_version-2.10.0-py2.py3-none-any.whl (15 kB)\n",
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+            "\u001b[?25hDownloading tomlkit-0.13.2-py3-none-any.whl (37 kB)\n",
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+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.5/62.5 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading ffmpy-0.5.0-py3-none-any.whl (6.0 kB)\n",
+            "Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n",
+            "Installing collected packages: pydub, uvicorn, tomlkit, semantic-version, ruff, python-multipart, groovy, ffmpy, aiofiles, starlette, safehttpx, gradio-client, fastapi, gradio\n",
+            "Successfully installed aiofiles-24.1.0 fastapi-0.115.12 ffmpy-0.5.0 gradio-5.29.0 gradio-client-1.10.0 groovy-0.1.2 pydub-0.25.1 python-multipart-0.0.20 ruff-0.11.8 safehttpx-0.1.6 semantic-version-2.10.0 starlette-0.46.2 tomlkit-0.13.2 uvicorn-0.34.2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import gradio as gr"
+      ],
+      "metadata": {
+        "id": "2x1M-ZzZvQQS"
+      },
+      "id": "2x1M-ZzZvQQS",
+      "execution_count": 44,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "def summarize_interface(text):\n",
+        "    return summarize_text(text)\n",
+        "\n",
+        "# Gradio interface\n",
+        "demo = gr.Interface(\n",
+        "    fn=summarize_interface,\n",
+        "    inputs=gr.Textbox(lines=10, placeholder=\"Paste your email, article or paragraph here...\", label=\"📝 Input Text\"),\n",
+        "    outputs=gr.Textbox(label=\"🧾 Summary\"),\n",
+        "    title=\"Multilingual Text Summarizer (EN/FR)\",\n",
+        "    description=\"Paste any English or French text below and get an automatic summary. Language is detected automatically.\",\n",
+        "    examples=[\n",
+        "        [\"Bonjour, ceci est un exemple de mail professionnel à résumer pour un usage interne.\"],\n",
+        "        [\"This is a long English article that explains how machine learning models are trained using large datasets.\"]\n",
+        "    ]\n",
+        ")\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "    demo.launch()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 650
+        },
+        "id": "RjSZ3LBgvOJO",
+        "outputId": "31aa9b2b-1f91-40a0-ba94-52d64587125a"
+      },
+      "id": "RjSZ3LBgvOJO",
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "It looks like you are running Gradio on a hosted a Jupyter notebook. For the Gradio app to work, sharing must be enabled. 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://ffa1a05b16f061c165.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://ffa1a05b16f061c165.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "demo.close()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3QCyh5aYvOFw",
+        "outputId": "0607c523-1a7a-469c-c936-0c817f9b6854"
+      },
+      "id": "3QCyh5aYvOFw",
+      "execution_count": 46,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Closing server running on port: 7860\n"
+          ]
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.x"
+    },
+    "colab": {
+      "provenance": [],
+      "collapsed_sections": [
+        "R5HB8zbHmcG-"
+      ],
+      "toc_visible": true,
+      "include_colab_link": true
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