diff --git "a/Module02_Session03_Task_MozahAlismail_(1) (1).ipynb" "b/Module02_Session03_Task_MozahAlismail_(1) (1).ipynb"
deleted file mode 100644--- "a/Module02_Session03_Task_MozahAlismail_(1) (1).ipynb"
+++ /dev/null
@@ -1,3999 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "id": "9360a8ca",
-      "metadata": {
-        "id": "9360a8ca"
-      },
-      "source": [
-        "##### Tuwaiq Course: llama based on Gen AI\n",
-        "##### Module 02 Session 03 Task\n",
-        "##### Name: Mozah Alismail"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "!pip install transformers gradio gtts"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "jeyWeS4xc-zZ",
-        "outputId": "777a8895-31ca-4c5f-b4bb-07980789ddfc"
-      },
-      "id": "jeyWeS4xc-zZ",
-      "execution_count": 1,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (4.52.4)\n",
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-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "id": "3df6291a",
-      "metadata": {
-        "id": "3df6291a"
-      },
-      "outputs": [],
-      "source": [
-        "import gradio as gr\n",
-        "from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM\n",
-        "from google.colab import userdata\n",
-        "import gtts"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "id": "3323343d",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 163,
-          "referenced_widgets": [
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-        },
-        "id": "3323343d",
-        "outputId": "3f00e7f3-640f-4324-cddb-32ac6e75d51a"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "64999c6d407c4b84bc3d29f5c9625f50"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "7345d05d849349fbba7f762d3235f6e8"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "d86468a93ab243938b2e9863bc48fb05"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "vocab.txt: 0.00B [00:00, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "f7d9c8f8083a45f7b55273ce6b02471f"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "Device set to use cpu\n"
-          ]
-        }
-      ],
-      "source": [
-        "# === Load Pipeline ===\n",
-        "sentiment_pipeline = pipeline(\"sentiment-analysis\", verbose = 0, model=\"distilbert/distilbert-base-uncased-finetuned-sst-2-english\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "id": "26a8b534",
-      "metadata": {
-        "id": "26a8b534"
-      },
-      "outputs": [],
-      "source": [
-        "# === Sentiment Analysis ===\n",
-        "def analyze_sentiment(text):\n",
-        "    result = sentiment_pipeline(text)[0]\n",
-        "    return f\"{result['label']} (Score: {result['score']:.2f})\""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "id": "447ab305",
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 227,
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-        "id": "447ab305",
-        "outputId": "8b2d5ab6-af2b-439a-cd89-b9a42bf0c662"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
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-            "Device set to use cpu\n"
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-        "# Initialize the summarization pipeline, tokenizer, and model\n",
-        "model_name = \"sshleifer/distilbart-cnn-12-6\"\n",
-        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
-        "model = AutoModelForSeq2SeqLM.from_pretrained(model_name)\n",
-        "summary_pipe = pipeline(\"summarization\", model=model, tokenizer=tokenizer)"
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-      "execution_count": 6,
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-        "# Function to chunk text with an overlap\n",
-        "def chunk_text_with_overlap(tokens, max_length, overlap):\n",
-        "    chunks = []\n",
-        "    for i in range(0, len(tokens), max_length - overlap):\n",
-        "        chunk = tokens[i:i + max_length]\n",
-        "        chunks.append(chunk)\n",
-        "    return chunks\n",
-        "\n",
-        "\n",
-        "# === Summarization ===\n",
-        "def summarize_text(text):\n",
-        "\n",
-        "    # Get the maximum length from the model configuration\n",
-        "    max_length = model.config.max_position_embeddings\n",
-        "    print('max_length:', max_length)\n",
-        "\n",
-        "    # Define the overlap\n",
-        "    overlap = 50  # Adjust overlap as needed\n",
-        "\n",
-        "    # Tokenize the text\n",
-        "    tokens = tokenizer(text, return_tensors='pt', truncation=False)['input_ids'][0]\n",
-        "\n",
-        "    # Chunk the tokens with overlap\n",
-        "    chunks = chunk_text_with_overlap(tokens, max_length, overlap)\n",
-        "\n",
-        "    # Summarize each chunk\n",
-        "    summaries = []\n",
-        "    for chunk in chunks:\n",
-        "        input_ids = chunk.unsqueeze(0)  # Add batch dimension\n",
-        "        summary_ids = model.generate(input_ids, max_length=max_length, num_beams=4, length_penalty=2.0, early_stopping=True)\n",
-        "        summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)\n",
-        "        summaries.append(summary)\n",
-        "\n",
-        "    # Combine the summaries into a final summary\n",
-        "    final_summary = ' '.join(summaries)\n",
-        "\n",
-        "    return final_summary"
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-      "source": [
-        "# === Text-to-Speech ===\n",
-        "def text_to_speech(text):\n",
-        "    tts = gtts.gTTS(text)\n",
-        "    tts.save(\"output.mp3\")\n",
-        "    return \"output.mp3\""
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-            "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://dc78be8d4c9ea63986.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"
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