From a46e2b87ed86e48b91d4fe4480e4d2886d89a45d Mon Sep 17 00:00:00 2001 From: AlekhyaVemuri Date: Wed, 28 May 2025 05:31:45 -0700 Subject: [PATCH 1/6] Modified Text-Summarizer-Browser-Plugin Sample --- .../.python-version | 1 + Text-Summarizer-Browser-Plugin/README.md | 214 +- .../TextSummarizerPlugin.ipynb | 431 ++- .../backend/server.py | 20 + Text-Summarizer-Browser-Plugin/pyproject.toml | 16 + .../requirements.txt | 9 - Text-Summarizer-Browser-Plugin/uv.lock | 3418 +++++++++++++++++ 7 files changed, 3868 insertions(+), 241 deletions(-) create mode 100644 Text-Summarizer-Browser-Plugin/.python-version create mode 100644 Text-Summarizer-Browser-Plugin/pyproject.toml delete mode 100644 Text-Summarizer-Browser-Plugin/requirements.txt create mode 100644 Text-Summarizer-Browser-Plugin/uv.lock diff --git a/Text-Summarizer-Browser-Plugin/.python-version b/Text-Summarizer-Browser-Plugin/.python-version new file mode 100644 index 0000000..e4fba21 --- /dev/null +++ b/Text-Summarizer-Browser-Plugin/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/Text-Summarizer-Browser-Plugin/README.md b/Text-Summarizer-Browser-Plugin/README.md index 5476bb6..fbac853 100644 --- a/Text-Summarizer-Browser-Plugin/README.md +++ b/Text-Summarizer-Browser-Plugin/README.md @@ -2,89 +2,126 @@ A plug-and-play Chrome extension seamlessly integrates with Flask and leverages an OpenVINO backend for fast and efficient summarization of webpages (via URL) and PDFs (via upload). Powered by LangChain tools, it handles advanced tasks like text splitting and vectorstore management to deliver accurate and meaningful summaries. -## How it Works - -image +This Chrome extension acts as the immediate user gateway, offering a minimalist interface right in the browser's toolbar that lets users instantly trigger summarization. It's designed to efficiently capture the required input, whether it's a webpage's URL or content from an uploaded PDF, and securely send this data to the Flask server for further processing. + +## Contents +- [Sample Workflow](./Readme.md#sample-workflow) +- [Prerequisites](./Readme.md#prerequisites) +- [Sample Structure](./Readme.md#sample-structure) +- [Run the `Text Summarizer Browser Plugin` Sample](./Readme.md#run-the-text-summarizer-browser-plugin-sample) + - [Setup the environment with uv](./Readme.md#setup-the-environment-with-uv) + - [Load this `Text Summarizer` extension](./Readme.md#load-this-text=summarizer-extension) + - [Contens of `extension` directory](./Readme.md#contents-of-extension-directory) + - [Load an unpacked extension](./Readme.md#load-an-unpacked-extension) + - [Pin the extension](./Readme.md#pin-the-extension) + - [Run the sample](./Readme.md#run-the-sample) + - [Steps to follow via Jupyter Notebook](./Readme.md#steps-to-follow-via-jupyter-notebook) + - [Steps to follow via Terminal](./Readme.md#steps-to-follow-via-terminal) + - [Interact with `Text Summarizer Browser Plugin`](./Readme.md#interact-with-text-summarizer-browser-plugin) + -## Sample Structure +## Sample Workflow -The directory contains: -- **backend:** Includes `code.py` and `server.py` for processing text from webpages or PDFs and managing Flask-related operations. -- **extension:** Contains `manifest.json` for the Chrome extension along with `popup.html`, `popup.js`, and `style.css` for the user interface. +- This architecture is split into two main components: a lightweight **Chrome extension** and a powerful **Flask server**. +- The **Chrome extension** serves as the user's direct interface, enabling them to simply click a button to initiate summarization of the current webpage or to upload a PDF directly from their browser. +- Once activated, it securely sends the webpage URL or the uploaded PDF content to the Flask server. +- This **Flask server**, acting as the intelligent backend, processes the request, orchestrates the summarization using the OpenVINO backend and LangChain tools, and then sends the concise summary back to the extension for display. +- This clear separation of concerns ensures a responsive user experience within the browser while offloading heavy computational tasks to a dedicated and scalable server. + +image -## Prerequisites +## Pre-requisites | Optimized for | Description | | :------------ | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | OS | Windows 11 64-bit (22H2, 23H2) and newer or Ubuntu* 22.04 64-bit (with Linux kernel 6.6+) and newer | -| Hardware | Intel® Core™ Ultra Processors | -| Software | 1. [Intel® GPU drivers from Intel® Arc™ & Iris® Xe Graphics for Windows](https://www.intel.com/content/www/us/en/download/785597/intel-arc-iris-xe-graphics-windows.html) or [Linux GPU drivers](https://dgpu-docs.intel.com/driver/client/overview.html)
2. NPU(Optional): [Intel® NPU Driver for Windows](https://www.intel.com/content/www/us/en/download/794734/intel-npu-driver-windows.html) or [Linux NPU Driver](https://github.com/intel/linux-npu-driver/releases) | +| Hardware | 1. [Intel® GPU drivers from Intel® Arc™ & Iris® Xe Graphics for Windows](https://www.intel.com/content/www/us/en/download/785597/intel-arc-iris-xe-graphics-windows.html) or [Linux GPU drivers](https://dgpu-docs.intel.com/driver/client/overview.html)
2. NPU(Optional): [Intel® NPU Driver for Windows](https://www.intel.com/content/www/us/en/download/794734/intel-npu-driver-windows.html) or [Linux NPU Driver](https://github.com/intel/linux-npu-driver/releases)
3. Intel® Core™ Ultra Processors | +| Software | 1. [Intel® GPU drivers from Intel® Arc™ & Iris® Xe Graphics for Windows](https://www.intel.com/content/www/us/en/download/785597/intel-arc-iris-xe-graphics-windows.html),
2. [uv](https://docs.astral.sh/uv/) | | Browsers | [Google Chrome](https://www.google.com/chrome/dr/download/?brand=MRUS&ds_kid=43700079286123654&gad_source=1&gclid=EAIaIQobChMI0J3fybvSigMV5dXCBB1TDARCEAAYASAAEgL36_D_BwE&gclsrc=aw.ds) & [Microsoft Edge](https://www.microsoft.com/en-us/edge/download?form=MA13FJ) -1. **Install the below necessary tools/packages:** - - Git - - [Git for Windows](https://git-scm.com/downloads) - - Git for Linux - - For Debian/Ubuntu-based systems: - ```bash - sudo apt update && sudo apt -y install git - ``` - - For RHEL/CentOS-based systems: - ```bash - sudo dnf update && sudo dnf -y install git - ``` - - Miniforge - - [Miniforge for Windows](https://conda-forge.org/download/) - - Miniforge for Linux - Download, install the Miniconda using the below commands. - ```bash - wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh" - bash Miniforge3-$(uname)-$(uname -m).sh - ``` - Replace with your actual Miniforge bin folder path and run the cd command to go there. Initialize the conda environment and restart the terminal. - ```bash - cd - ``` - ```bash - ./conda init - ``` - -2. **Create a Conda Environment:** - - Run the command: - ```bash - conda create -n summarizer_plugin python=3.11 libuv - ``` - ```bash - conda activate summarizer_plugin - ``` +## Sample Structure -3. **Install Dependencies:** - - Execute: - ```bash - pip install -r requirements.txt - ``` +The directory contains: +- **backend:** Includes `code.py` and `server.py` for processing text from webpages or PDFs and managing Flask-related operations. +- **extension:** Contains `manifest.json` for the Chrome extension along with `popup.html`, `popup.js`, and `style.css` for the user interface. These files are used to load an unapcked extension. +- **`TextSummarizerPlugin.ipynb`:** Notebook which processes text from webpages or PDFs and manages Flask-related operations. + + +## Run the `Text Summarizer Browser Plugin` Sample: + +### Setup Environment with `uv`: +The sample uses [uv](https://docs.astral.sh/uv/) for environment management. +> Steps to install `uv` are as follows. Refer [this documentation](https://docs.astral.sh/uv/getting-started/installation/) this for more information. +>
**Windows:**
+> ```bash +> powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" +> ``` +> +>
**Linux:**
+>
Use curl to download the script and execute it with sh: +> ```bash +> curl -LsSf https://astral.sh/uv/install.sh | sh +> ``` +> +>
If your system doesn't have curl, you can use wget: +> ```bash +> wget -qO- https://astral.sh/uv/install.sh | sh +> ``` + +1. In a terminal, navigate to `Text Summarizer Browser Plugin Sample` folder: + ```bash + cd + ``` +2. Sync project dependencies: + ```bash + uv sync + ``` +> **NOTE:** Run the below command if you face any dependency issues: +> ```bash +> uv clean +> ``` +### Load this `Text Summarizer` extension + +1. **Contents of `extension` directory** + - [`extension` directory](./extension) consists of files, i.e. `manifest.json`, `popup.html`, `popup.js` and `style.css`, used to load an unpacked extension. + +2. **Load an unpacked Extension:** + - To load an unpacked extension in developer mode: + - Go to the Extensions page by entering **chrome://extensions** in a new tab. (By design chrome:// URLs are not linkable.) + - Alternatively, **click the Extensions menu puzzle button and select Manage Extensions** at the bottom of the menu. + - Or, click the Chrome menu, hover over More Tools, then select Extensions. + - Enable **Developer Mode** by clicking the toggle switch next to Developer mode. + - Click the **Load unpacked** button and select the `extension directory`. + - Refer to [Chrome’s development documentation](https://developer.chrome.com/docs/extensions/get-started/tutorial/hello-world#load-unpacked) for further details. -4. **Install an ipykernel to select the "summarizer_plugin" environment:** - ```bash - python -m ipykernel install --user --name=summarizer_plugin - ``` + image - >**Note**: Run your terminal as admin to avoid any permission issues. - -## Next Steps +3. **Pin the Extension:** + + Pin your extension to the toolbar for a quick access to your extension. + + image + + +### Run the Sample Below are the steps to run the plugin from Jupyter Notebook **OR** Terminal. -### **Steps to follow via Jupyter Notebook:** +#### **Steps to follow via Jupyter Notebook:** Once the environment is created, we can run the plugin via [TextSummarizerPlugin.ipynb](./TextSummarizerPlugin.ipynb). Please follow the below steps to open the jupyter notebook: -1. Open Jupyter Notebook & run the cells: +1. Launch Jupyter Notebook: ``` - jupyter notebook + uv run jupyter lab ``` -2. Select the **summarizer_plugin** kernel. +2. Select the default **ipykernel** kernel. +3. Run all the cells one by one. +4. The last cell at the end of the notebook launches the flask server. +5. Once the flask server is running, open Chrome Browser & click on the pinned text-summarizer + +
OR
-### **Steps to follow via Terminal:** +#### **Steps to follow via Terminal:** 1. **Download and Convert the Huggingface Model to OpenVINO IR Format:** - Log in to Huggingface: ``` @@ -95,59 +132,36 @@ Once the environment is created, we can run the plugin via [TextSummarizerPlugin ``` mkdir models cd models - optimum-cli export openvino --model meta-llama/Llama-2-7b-chat-hf --weight-format int4 ov_llama_2 - optimum-cli export openvino --model Qwen/Qwen2-7B-Instruct --weight-format int4 ov_qwen7b + uv run optimum-cli export openvino --model meta-llama/Llama-2-7b-chat-hf --weight-format int4 ov_llama_2 + uv run optimum-cli export openvino --model Qwen/Qwen2-7B-Instruct --weight-format int4 ov_qwen7b ``` >**Note**: [Raise access request](https://www.llama.com/llama-downloads) for Llama models as it is a gated repository. + - - -2. **Load the Extension:** - - To load an unpacked extension in developer mode: - - Go to the Extensions page by entering **chrome://extensions** in a new tab. (By design chrome:// URLs are not linkable.) - - Alternatively, **click the Extensions menu puzzle button and select Manage Extensions** at the bottom of the menu. - - Or, click the Chrome menu, hover over More Tools, then select Extensions. - - Enable **Developer Mode** by clicking the toggle switch next to Developer mode. - - Click the **Load unpacked** button and select the extension directory. - - Refer to [Chrome’s development documentation](https://developer.chrome.com/docs/extensions/get-started/tutorial/hello-world#load-unpacked) for further details. - - image - - - -3. **Pin the Extension:** - - Pin your extension to the toolbar to quickly access your extension. - - image - - - - -#### Steps to Run the Plugin - -1. **Start the Flask Server:** - - Navigate to the backend folder: +2. **Start the Flask Server:** + - In one terminal, navigate to the backend folder: ``` - cd ../backend - python server.py + cd ./backend + uv run server.py ``` + This step would launch a flask server in browser extension -2. **Open the Chrome Browser:** - - Activate & Pin the loaded extension. +### Interact with `Text Summarizer Browser Plugin` +1. **Open Chrome Extension in Browser:** + - Click on the pinned extension of this text summarizer plugin. - Plugin UI looks as follows: image -3. **Select an OpenVINO Model:** +2. **Select an OpenVINO Model:** - Choose an OpenVINO IR format model previously converted from Huggingface. image -4. **Interact with the UI:** +3. **Interact with the UI:** - Choose either **Web Page** or **PDF** post selecting one of the converted OV models: image @@ -174,5 +188,5 @@ Once the environment is created, we can run the plugin via [TextSummarizerPlugin image -5. **Reload the Page:** +4. **Reload the Page:** - Refresh the webpage or re-open the plugin to restart. diff --git a/Text-Summarizer-Browser-Plugin/TextSummarizerPlugin.ipynb b/Text-Summarizer-Browser-Plugin/TextSummarizerPlugin.ipynb index b66572e..e720f21 100644 --- a/Text-Summarizer-Browser-Plugin/TextSummarizerPlugin.ipynb +++ b/Text-Summarizer-Browser-Plugin/TextSummarizerPlugin.ipynb @@ -36,17 +36,6 @@ "## Pre-requisites" ] }, - { - "cell_type": "markdown", - "id": "ee13e251-d11d-4709-8068-29bdf38ed67d", - "metadata": {}, - "source": [ - "### Install the below necessary tools/packages:\n", - " - [Git on Windows](https://git-scm.com/downloads)\n", - " - [Miniforge](https://conda-forge.org/download/)\n", - " - [Google Chrome for Windows](https://www.google.com/chrome/?brand=OZZY&ds_kid=43700080794581137&gad_source=1&gclid=Cj0KCQiAoae5BhCNARIsADVLzZdwNNB5nIyjZ8OyCzg6h_cCig1eoaYquUSEd7BAigJhTzps1Kxuop8aArE6EALw_wcB&gclsrc=aw.ds)\n" - ] - }, { "cell_type": "markdown", "id": "c07f0ea0-c374-4048-b907-bf8a9fd43a9b", @@ -60,46 +49,20 @@ "id": "479ef7d9-b2d0-4b38-9829-f92224ee9998", "metadata": {}, "source": [ - "Before converting the models & running the plugin, make sure you have followed all the below listed [steps to prepare the environment](./README.md/#prerequisites)\n", - "- Setup conda environment\n", - " ```bash\n", - " conda create -n summarizer_plugin python=3.11 libuv\n", - " ```\n", - " ```bash\n", - " conda activate summarizer_plugin\n", - " ```\n", - " ```bash\n", - " python -m pip install ipykernel tqdm ipywidgets\n", - " ```\n", - " ```bash\n", - " python -m ipykernel install --user --name=summarizer_plugin\n", - " ```\n", - " Now choose the `summarizer_plugin` kernel in the notebook.\n", - "- Installing necessary packages" - ] - }, - { - "cell_type": "markdown", - "id": "787b4825-ad2d-4b1f-9ae2-d03da418ac29", - "metadata": {}, - "source": [ - "#### Installing necessary packages" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "25425663-cca9-47d7-a93f-5ec6239de66b", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "import subprocess\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "os.system(f\"{sys.executable} -m pip install -r requirements.txt\")" + "Before converting the models & running the plugin, make sure you have followed all the below listed [steps to prepare the environment](./Readme.md/#setup-environment-with-uv)\n", + "- Install uv by refering to [this documentation](https://docs.astral.sh/uv/getting-started/installation/)\n", + "- Sync dependencies\n", + " ```bash\n", + " uv sync\n", + " ```\n", + "Load and pin the unpacked extension as mentioned [here](./Readme.md/):\n", + "- Navigate to the Extensions page by entering chrome://extensions in a new tab. (By design chrome:// URLs are not linkable.)\n", + " - Alternatively, click the Extensions menu puzzle button and select Manage Extensions at the bottom of the menu.\n", + " - Or, click the Chrome menu, hover over More Tools, then select Extensions.\n", + "- Enable Developer Mode by clicking the toggle switch next to Developer mode.\n", + "- Click the Load unpacked button and select the extension directory.\n", + "- Refer to Chrome’s development documentation for further details.\n", + "- Pin your extension to the toolbar for a quick access." ] }, { @@ -110,17 +73,33 @@ "### Download and Convert the Huggingface Model to OpenVINO IR Format:\n", "\n", "#### Login to Huggingface:\n", - "Generate a token from Huggingface for private/gated models like Meta Llama, etc. To access such private/gated models, refer to [Huggingface documentation](https://huggingface.co/docs/hub/en/models-gated)." + "- Generate a token from Huggingface for private/gated models like Meta Llama, etc. To access such private/gated models, refer to [Huggingface documentation](https://huggingface.co/docs/hub/en/models-gated).\n", + "- If you already have access to such gated models, know how to generate an access token by referring [this](https://huggingface.co/docs/hub/en/security-tokens)." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "0c9347f7-09dc-4324-b1e2-253c4d5a9f2f", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "55c1c17acff446019b03719e22175c8a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value='
Converting LLMs to OpenVINO IR Format could take sometime.. Meanwhile, have a milkshake!🧋 " + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "29a146b7-2aad-43be-8a8a-68bb1d885a02", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from pathlib import Path\n", "import os\n", "ROOT_DIR = Path.cwd()\n", "MODEL_DIR = ROOT_DIR / 'models'\n", "MODEL_DIR.mkdir(parents=True, exist_ok=True)\n", - "os.system(f\"optimum-cli export openvino --model Qwen/Qwen2-7B-Instruct --weight-format int4 {MODEL_DIR}/ov_qwen7b\")\n", - "os.system(f\"optimum-cli export openvino --model meta-llama/Llama-2-7b-chat-hf --weight-format int4 {MODEL_DIR}/ov_llama_2\")" + "os.system(f\"uv run optimum-cli export openvino --model Qwen/Qwen2-7B-Instruct --weight-format int4 {MODEL_DIR}/ov_qwen7b\")\n", + "os.system(f\"uv run optimum-cli export openvino --model meta-llama/Llama-2-7b-chat-hf --weight-format int4 {MODEL_DIR}/ov_llama_2\")" ] }, { @@ -159,56 +157,6 @@ ">**Note**: [Raise access request](https://www.llama.com/llama-downloads) for Llama models as it is a gated repository.\n" ] }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "4b2ed7c2-e406-4423-b7a6-0a377bb2a4b5", - "metadata": {}, - "source": [ - "### Load the extension\n", - "\n", - "To load an unpacked extension in developer mode:\n", - "- Go to the Extensions page by entering **chrome://extensions** in a new tab. (By design chrome:// URLs are not linkable.)\n", - " - Alternatively, **click the Extensions menu puzzle button and select Manage Extensions** at the bottom of the menu.\n", - " - Or, click the Chrome menu, hover over More Tools, then select Extensions.\n", - "- Enable **Developer Mode** by clicking the toggle switch next to Developer mode.\n", - "- Click the **Load unpacked** button and select the extension directory.\n", - "- Refer to [Chrome’s development documentation](https://developer.chrome.com/docs/extensions/get-started/tutorial/hello-world#load-unpacked) for further details.\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "5e4c9750-8fcc-474b-87c5-ba28448b632d", - "metadata": {}, - "source": [ - "### Pin the extension\n", - "Pin your extension to the toolbar to quickly access your extension.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "1191f358-ebcb-4ca9-b0a5-c80f96ac93fd", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, - "source": [ - "### Code Sample Structure\n", - "Browser plugin code has two parts, one is backend folder & the other is extension folder.\n", - "- **Backend** - In the backend folder, we have two python files `code.py` and `server.py`\n", - " - `code.py` manages data pre-processing tasks\n", - " - `server.py` manages flask server-side operations\n", - "- **Extension** - In the extension we have the front end code required for the browser plugin (popup.html, popup.js, style.css, manifest.json)\n" - ] - }, { "cell_type": "markdown", "id": "0d060a9e-0355-41e5-a33c-c8baf35d7481", @@ -227,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "9d1333e1-1785-4a4b-b426-a7d32640236c", "metadata": {}, "outputs": [], @@ -240,7 +188,11 @@ "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "from langchain_chroma import Chroma\n", "from langchain_community.embeddings import HuggingFaceEmbeddings\n", - "from langchain_community.document_loaders import WebBaseLoader, PyPDFLoader" + "from langchain_community.document_loaders import WebBaseLoader, PyPDFLoader\n", + "import os\n", + "os.environ['USER_AGENT'] = 'myagent'\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" ] }, { @@ -254,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "de8469f9-5b72-4a2c-bc09-a4406a34a1c8", "metadata": {}, "outputs": [], @@ -290,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "dd83e322-d97f-420d-88cf-21ceba6261fe", "metadata": {}, "outputs": [], @@ -331,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "6750172c-0d22-4e70-98d5-7a5ec29f4c36", "metadata": {}, "outputs": [], @@ -383,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "edebde7e-f6df-4123-88e8-13f9f072d296", "metadata": {}, "outputs": [], @@ -437,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "c6ad4d92-c524-450b-b8af-e5630ed0b5bb", "metadata": {}, "outputs": [], @@ -490,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "83fc83ad-3e4a-4d4b-906e-01bbebd10ae3", "metadata": {}, "outputs": [], @@ -544,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "2fd5d11d-0e12-4e1e-9669-31692af50159", "metadata": {}, "outputs": [], @@ -595,7 +547,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "1434779c-1d0b-49ec-bbb9-dbbd827f61bf", "metadata": {}, "outputs": [], @@ -618,7 +570,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "54773857-d296-44c4-847e-d26da86b01f9", "metadata": {}, "outputs": [], @@ -641,7 +593,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "354d0e26-8719-4b54-888d-0264c0954416", "metadata": {}, "outputs": [], @@ -673,7 +625,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "8f664170-bf7c-41b5-b41b-45217a5c1724", "metadata": {}, "outputs": [], @@ -701,7 +653,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "36575ff6-f3ee-4ddc-8c56-689c8b6844bd", "metadata": {}, "outputs": [], @@ -734,7 +686,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "6ea9ce78-c713-4204-ad68-9b9a9e9a79f2", "metadata": {}, "outputs": [], @@ -775,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "7b4f6d9e-9007-461c-88ed-13260d5cf559", "metadata": {}, "outputs": [], @@ -807,7 +759,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "873be699-2bf8-4238-88cd-f3c36431fd85", "metadata": {}, "outputs": [], @@ -833,26 +785,72 @@ "id": "40f1be65-2d3b-4fce-bb31-baa7a6573e25", "metadata": {}, "source": [ - "### Calling the main function\n", - "This code snippet ensures that the Flask development server starts only when the application is run directly" + "### Launch Plugin via Flask Server\n", + "\n", + "#### 🎉 Flask server is Ready! 🎉\n", + "\n", + "Your application is now live and waiting for interaction!\n", + "\n", + "**🚀 Essential Step: Activate Your Browser Plugin!**\n", + "\n", + "- This application operates through its dedicated browser extension.\n", + "- To begin, please open your web browser and locate the plugin's icon, which looks like `T`, in your toolbar (it's often in the top-right corner).\n", + "- Click on the `T` icon to access the browser extension\n", + " > **NOTE**: Do not access the flask server API Endpoint as the application is running on **browser extension**. \n", + "\n", + "**Having trouble?**\n", + "- Is the plugin loaded? If you haven't already, please load it by following the Readme.md file.\n", + "- Is it enabled? Double-check your browser's extension settings to ensure the plugin isn't disabled.\n", + "- Have you pinned the extension? Pin the extension.\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "4138fa1d-5f11-4629-a636-f17cc114644d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " * Serving Flask app '__main__'\n", + " * Debug mode: off\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\n", + " * Running on http://127.0.0.1:5000\n", + "Press CTRL+C to quit\n", + "Provided model does not contain state. It may lead to sub-optimal performance.Please reexport model with updated OpenVINO version >= 2023.3.0 calling the `from_pretrained` method with original model and `export=True` parameter\n", + "Compiling the model to GPU ...\n", + "Setting OpenVINO CACHE_DIR to models\\ov_qwen7b\\model_cache\n", + "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n", + "127.0.0.1 - - [28/May/2025 03:19:51] \"POST /select-model HTTP/1.1\" 200 -\n" + ] + } + ], "source": [ "app.run(port=5000)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1a5379a7-3e4e-4dbb-acbc-145f731318e1", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "summarizer_plugin", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "summarizer_plugin" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -864,7 +862,176 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.10" + "version": "3.12.5" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": { + "0806da8e21ef43fb954d04a758b1672d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "2537e0551036498b92794ff751292dc9": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": {} + }, + "417fd8dd33434e4f83992900ab07bca3": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": {} + }, + "46b975c57854465abb7fcf9811236333": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ButtonModel", + "state": { + "description": "Login", + "layout": "IPY_MODEL_2537e0551036498b92794ff751292dc9", + "style": "IPY_MODEL_658454d94e3943fcb807696652229efc", + "tooltip": null + } + }, + "55c1c17acff446019b03719e22175c8a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "VBoxModel", + "state": { + "children": [ + "IPY_MODEL_55f83d8af4f14bcf919a7be7172fb1a1", + "IPY_MODEL_7d8e9a2182e44135b426f74599105380", + "IPY_MODEL_ecd4f83965d344e19d38b2bb8f3cad30", + "IPY_MODEL_46b975c57854465abb7fcf9811236333", + "IPY_MODEL_db1809374aaa4a07878cc6d9867f6954" + ], + "layout": "IPY_MODEL_cf95eb33c7f749afab6ae07f0094198b" + } + }, + "55f83d8af4f14bcf919a7be7172fb1a1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "layout": "IPY_MODEL_85e8f54ab64f4d60b312b854c80fc65e", + "style": "IPY_MODEL_eb1ac59dd60142febd0376c901df2314", + "value": "

Copy a token from your Hugging Face\ntokens page and paste it below.
Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.
" + } + }, + "62e1665f7a254243a615eaa510dc584b": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": {} + }, + "658454d94e3943fcb807696652229efc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ButtonStyleModel", + "state": { + "font_family": null, + "font_size": null, + "font_style": null, + "font_variant": null, + "font_weight": null, + "text_color": null, + "text_decoration": null + } + }, + "7d8e9a2182e44135b426f74599105380": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "PasswordModel", + "state": { + "description": "Token:", + "layout": "IPY_MODEL_417fd8dd33434e4f83992900ab07bca3", + "style": "IPY_MODEL_bffa098d17db45a0b7c7460ad489f915" + } + }, + "85e8f54ab64f4d60b312b854c80fc65e": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": {} + }, + "98f29deae0674348bc8be2555826295e": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": {} + }, + "bffa098d17db45a0b7c7460ad489f915": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "TextStyleModel", + "state": { + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "cf95eb33c7f749afab6ae07f0094198b": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "align_items": "center", + "display": "flex", + "flex_flow": "column", + "width": "50%" + } + }, + "db1809374aaa4a07878cc6d9867f6954": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "layout": "IPY_MODEL_62e1665f7a254243a615eaa510dc584b", + "style": "IPY_MODEL_0806da8e21ef43fb954d04a758b1672d", + "value": "\nPro Tip: If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks.
" + } + }, + "eb1ac59dd60142febd0376c901df2314": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ecd4f83965d344e19d38b2bb8f3cad30": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "CheckboxModel", + "state": { + "description": "Add token as git credential?", + "disabled": false, + "layout": "IPY_MODEL_98f29deae0674348bc8be2555826295e", + "style": "IPY_MODEL_f94049a4d5ff4b5a9ea30bb5ca24427c", + "value": true + } + }, + "f94049a4d5ff4b5a9ea30bb5ca24427c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "CheckboxStyleModel", + "state": { + "description_width": "" + } + } + }, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, diff --git a/Text-Summarizer-Browser-Plugin/backend/server.py b/Text-Summarizer-Browser-Plugin/backend/server.py index 29cbd66..5a72abb 100644 --- a/Text-Summarizer-Browser-Plugin/backend/server.py +++ b/Text-Summarizer-Browser-Plugin/backend/server.py @@ -5,6 +5,10 @@ from code import load_llm, pre_process_url_data, qa_on_url_summarized_text, pre_process_pdf_data, qa_on_pdf_summarized_text import tempfile import chromadb +import os +os.environ['USER_AGENT'] = 'myagent' +import warnings +warnings.filterwarnings("ignore") # Initializing the flask app and enabling CORS app = Flask(__name__) @@ -114,4 +118,20 @@ def url_process_query(): if __name__ == '__main__': + print(""" + 🎉 Flask server is Ready! 🎉 + Your application is now live and waiting for interaction! + + **🚀 Essential Step: Activate Your Browser Plugin!** + + - This application operates through its dedicated browser extension. + - To begin, please open your web browser and locate the plugin's icon, which looks like `T`, in your toolbar (it's often in the top-right corner). + - Click on the `T` icon to access the browser extension + + **Having trouble?** + - Is the plugin loaded? If you haven't already, please load it by following the Readme.md file. + - Is it enabled? Double-check your browser's extension settings to ensure the plugin isn't disabled. + - Have you pinned the extension? Pin the extension. + """ + ) app.run(port=5000) diff --git a/Text-Summarizer-Browser-Plugin/pyproject.toml b/Text-Summarizer-Browser-Plugin/pyproject.toml new file mode 100644 index 0000000..6e4ddd5 --- /dev/null +++ b/Text-Summarizer-Browser-Plugin/pyproject.toml @@ -0,0 +1,16 @@ +[project] +name = "text-summarizer-browser-plugin" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "flask>=3.1.1", + "flask-cors>=6.0.0", + "langchain>=0.3.25", + "langchain-chroma>=0.2.4", + "langchain-community>=0.3.24", + "optimum[nncf,openvino]>=1.25.3", + "pypdf>=5.5.0", + "sentence-transformers>=4.1.0", +] diff --git a/Text-Summarizer-Browser-Plugin/requirements.txt b/Text-Summarizer-Browser-Plugin/requirements.txt deleted file mode 100644 index 62a1529..0000000 --- a/Text-Summarizer-Browser-Plugin/requirements.txt +++ /dev/null @@ -1,9 +0,0 @@ -flask==3.1.1 -flask_cors==6.0.0 -langchain==0.3.25 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changed, 964 insertions(+) create mode 100644 Text-Summarizer-Browser-Plugin/tom.txt diff --git a/Text-Summarizer-Browser-Plugin/.gitignore b/Text-Summarizer-Browser-Plugin/.gitignore index 6e22444..f51b9ef 100644 --- a/Text-Summarizer-Browser-Plugin/.gitignore +++ b/Text-Summarizer-Browser-Plugin/.gitignore @@ -78,6 +78,7 @@ target/ # Jupyter Notebook .ipynb_checkpoints **/*.ipynb_checkpoints/ +models # IPython profile_default/ diff --git a/Text-Summarizer-Browser-Plugin/pyproject.toml b/Text-Summarizer-Browser-Plugin/pyproject.toml index 6e4ddd5..0bed0c8 100644 --- a/Text-Summarizer-Browser-Plugin/pyproject.toml +++ b/Text-Summarizer-Browser-Plugin/pyproject.toml @@ -7,6 +7,7 @@ requires-python = ">=3.12" dependencies = [ "flask>=3.1.1", "flask-cors>=6.0.0", + "jupyter>=1.1.1", "langchain>=0.3.25", "langchain-chroma>=0.2.4", "langchain-community>=0.3.24", diff --git a/Text-Summarizer-Browser-Plugin/tom.txt b/Text-Summarizer-Browser-Plugin/tom.txt new file mode 100644 index 0000000..79a9bdc --- /dev/null +++ b/Text-Summarizer-Browser-Plugin/tom.txt @@ -0,0 +1,30 @@ +Text Summarizer Plugin +1. There’s some good setup context here, but would be ideal again to be sure thing are consistent with the other readme’s. + - As there are additional steps for the browser plugin, had to make few modifications of the flow. Let me see if I can make it consistent throughout + +2. Step 2 need to be run not from the previous cmd. The user needs to opne a Miniforge console from the recently installed application. + - You are right. When users install miniforge for the first time, they should open a new terminal. As we are planning to migrate from pip to uv for environment setup, excluding this step from Readme.md file. + +3. Before installing dependencies, need to tell users to nav to the sample directory (esp since earlier we had the user nav to the minforge directory. + - Done + +4. Within the notebook, how does the hugging face login work. It seems like it’s erroring, but I can’t tell, and I obviously haven’t supply any credentials. (and after closing the notebook, I noticed messages in the output about not being able to access HF, so likely the culprit of why things aren’t working in the end). + - Huggingface login works within and outside the notebook(via terminal) too. It is also a crucial steps to access gated repositories(gated HF models) If you don't provide the access tokens, you might encounter access related errors with HF models. Refer this for more(https://huggingface.co/docs/hub/en/security-tokens) + +5. May want to provide a warning that converting the hugging face models to openVINO will take quite some time. + - Added an alert message + +6. We haven’t really talked about the extension itself, so we really need to provide some context in the load the extension section. In the description for selectin the extension, need to note that the extension is included with the sample Text-Summarizer-Browser-Plugin sample in the extension folder. + - + +7. When importing he necessary libraries, I got the following warning. +USER_AGENT environment variable not set, consider setting it to identify your requests. + - That is a warning and could be ignored. However, I have set the environment variable. + +8. Everything seemed to work in the notebook until I launched the app using the url provided in the last cell. I simply got a “Not Found’ message. + - Flask server is running in the URL mentioned(hardcoded) but the application is available to access via browser extension. I have added a detailed instructions right before the running the flask app to be more clear. Do not access the flask server API Endpoint as the application is running on browser extension + +9. Follow the cli instructions were fine, except that I got the same error, “Not Found” when trying to launch using the supplied url. + - Flask server is running in the URL mentioned(hardcoded) but the application is available to access via browser extension. I have added a detailed instructions right before the running the flask app to be more clear. 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--git a/Text-Summarizer-Browser-Plugin/README.md b/Text-Summarizer-Browser-Plugin/README.md index fbac853..cbd22be 100644 --- a/Text-Summarizer-Browser-Plugin/README.md +++ b/Text-Summarizer-Browser-Plugin/README.md @@ -1,29 +1,29 @@ # Text summarizer browser Plugin Sample -A plug-and-play Chrome extension seamlessly integrates with Flask and leverages an OpenVINO backend for fast and efficient summarization of webpages (via URL) and PDFs (via upload). Powered by LangChain tools, it handles advanced tasks like text splitting and vectorstore management to deliver accurate and meaningful summaries. +A plug-and -play Chrome extension seamlessly integrates with Flask and leverages an OpenVINO backend for fast and efficient summarization of webpages(via URL) and PDFs(via upload). Powered by LangChain tools, it handles advanced tasks like text splitting and vectorstore management to deliver accurate and meaningful summaries. This Chrome extension acts as the immediate user gateway, offering a minimalist interface right in the browser's toolbar that lets users instantly trigger summarization. It's designed to efficiently capture the required input, whether it's a webpage's URL or content from an uploaded PDF, and securely send this data to the Flask server for further processing. -## Contents -- [Sample Workflow](./Readme.md#sample-workflow) -- [Prerequisites](./Readme.md#prerequisites) -- [Sample Structure](./Readme.md#sample-structure) -- [Run the `Text Summarizer Browser Plugin` Sample](./Readme.md#run-the-text-summarizer-browser-plugin-sample) - - [Setup the environment with uv](./Readme.md#setup-the-environment-with-uv) - - [Load this `Text Summarizer` extension](./Readme.md#load-this-text=summarizer-extension) - - [Contens of `extension` directory](./Readme.md#contents-of-extension-directory) - - [Load an unpacked extension](./Readme.md#load-an-unpacked-extension) - - [Pin the extension](./Readme.md#pin-the-extension) - - [Run the sample](./Readme.md#run-the-sample) - - [Steps to follow via Jupyter Notebook](./Readme.md#steps-to-follow-via-jupyter-notebook) - - [Steps to follow via Terminal](./Readme.md#steps-to-follow-via-terminal) - - [Interact with `Text Summarizer Browser Plugin`](./Readme.md#interact-with-text-summarizer-browser-plugin) - - -## Sample Workflow - -- This architecture is split into two main components: a lightweight **Chrome extension** and a powerful **Flask server**. -- The **Chrome extension** serves as the user's direct interface, enabling them to simply click a button to initiate summarization of the current webpage or to upload a PDF directly from their browser. +# Contents +- [Sample Workflow](./Readme.md # sample-workflow) +- [Prerequisites](./Readme.md # prerequisites) +- [Sample Structure](./Readme.md # sample-structure) +- [Run the `Text Summarizer Browser Plugin` Sample](./Readme.md # run-the-text-summarizer-browser-plugin-sample) + - [Setup the environment with uv](./Readme.md # setup-the-environment-with-uv) + - [Load this `Text Summarizer` extension](./Readme.md # load-this-text=summarizer-extension) + - [Contens of `extension` directory](./Readme.md # contents-of-extension-directory) + - [Load an unpacked extension](./Readme.md # load-an-unpacked-extension) + - [Pin the extension](./Readme.md # pin-the-extension) + - [Run the sample](./Readme.md # run-the-sample) + - [Steps to follow via Jupyter Notebook](./Readme.md # steps-to-follow-via-jupyter-notebook) + - [Steps to follow via Terminal](./Readme.md # steps-to-follow-via-terminal) + - [Interact with `Text Summarizer Browser Plugin`](./Readme.md # interact-with-text-summarizer-browser-plugin) + + +# Sample Workflow + +- This architecture is split into two main components: a lightweight ** Chrome extension ** and a powerful ** Flask server**. +- The ** Chrome extension ** serves as the user's direct interface, enabling them to simply click a button to initiate summarization of the current webpage or to upload a PDF directly from their browser. - Once activated, it securely sends the webpage URL or the uploaded PDF content to the Flask server. - This **Flask server**, acting as the intelligent backend, processes the request, orchestrates the summarization using the OpenVINO backend and LangChain tools, and then sends the concise summary back to the extension for display. - This clear separation of concerns ensures a responsive user experience within the browser while offloading heavy computational tasks to a dedicated and scalable server. diff --git a/Text-Summarizer-Browser-Plugin/TextSummarizerPlugin.ipynb b/Text-Summarizer-Browser-Plugin/TextSummarizerPlugin.ipynb index e720f21..5152cee 100644 --- a/Text-Summarizer-Browser-Plugin/TextSummarizerPlugin.ipynb +++ b/Text-Summarizer-Browser-Plugin/TextSummarizerPlugin.ipynb @@ -1,1039 +1,1039 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "52a9a997-e2e8-43cf-9e40-5c777b4b9723", - "metadata": {}, - "source": [ - "# Text Summarizer Plugin" - ] - }, - { - "cell_type": "markdown", - "id": "99707adf-f1be-4e00-aad4-8187f8c816cf", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "Chrome extension seamlessly integrates with Flask and leverages an OpenVINO backend for fast and efficient summarization of webpages (via URL) and PDFs (via upload). Powered by LangChain tools, it handles advanced tasks like text splitting and vectorstore management to deliver accurate and meaningful summaries." - ] - }, - { - "cell_type": "markdown", - "id": "e56b6e8b-fcda-4fd1-9f4f-baf028233ea0", - "metadata": {}, - "source": [ - "## How it Works\n", - "\n", - "\"image\"\n" - ] - }, - { - "cell_type": "markdown", - "id": "d11ee514-4487-45d3-984e-6f6e291b61f0", - "metadata": {}, - "source": [ - "## Pre-requisites" - ] - }, - { - "cell_type": "markdown", - "id": "c07f0ea0-c374-4048-b907-bf8a9fd43a9b", - "metadata": {}, - "source": [ - "### Follow the below steps to prepare the environment" - ] - }, - { - "cell_type": "markdown", - "id": "479ef7d9-b2d0-4b38-9829-f92224ee9998", - "metadata": {}, - "source": [ - "Before converting the models & running the plugin, make sure you have followed all the below listed [steps to prepare the environment](./Readme.md/#setup-environment-with-uv)\n", - "- Install uv by refering to [this documentation](https://docs.astral.sh/uv/getting-started/installation/)\n", - "- Sync dependencies\n", - " ```bash\n", - " uv sync\n", - " ```\n", - "Load and pin the unpacked extension as mentioned [here](./Readme.md/):\n", - "- Navigate to the Extensions page by entering chrome://extensions in a new tab. (By design chrome:// URLs are not linkable.)\n", - " - Alternatively, click the Extensions menu puzzle button and select Manage Extensions at the bottom of the menu.\n", - " - Or, click the Chrome menu, hover over More Tools, then select Extensions.\n", - "- Enable Developer Mode by clicking the toggle switch next to Developer mode.\n", - "- Click the Load unpacked button and select the extension directory.\n", - "- Refer to Chrome’s development documentation for further details.\n", - "- Pin your extension to the toolbar for a quick access." - ] - }, - { - "cell_type": "markdown", - "id": "dc939a97-428b-4c61-93ba-51cfce27c54f", - "metadata": {}, - "source": [ - "### Download and Convert the Huggingface Model to OpenVINO IR Format:\n", - "\n", - "#### Login to Huggingface:\n", - "- Generate a token from Huggingface for private/gated models like Meta Llama, etc. To access such private/gated models, refer to [Huggingface documentation](https://huggingface.co/docs/hub/en/models-gated).\n", - "- If you already have access to such gated models, know how to generate an access token by referring [this](https://huggingface.co/docs/hub/en/security-tokens)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "0c9347f7-09dc-4324-b1e2-253c4d5a9f2f", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "55c1c17acff446019b03719e22175c8a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(HTML(value='
Converting LLMs to OpenVINO IR Format could take sometime.. Meanwhile, have a milkshake!🧋 " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "29a146b7-2aad-43be-8a8a-68bb1d885a02", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from pathlib import Path\n", - "import os\n", - "ROOT_DIR = Path.cwd()\n", - "MODEL_DIR = ROOT_DIR / 'models'\n", - "MODEL_DIR.mkdir(parents=True, exist_ok=True)\n", - "os.system(f\"uv run optimum-cli export openvino --model Qwen/Qwen2-7B-Instruct --weight-format int4 {MODEL_DIR}/ov_qwen7b\")\n", - "os.system(f\"uv run optimum-cli export openvino --model meta-llama/Llama-2-7b-chat-hf --weight-format int4 {MODEL_DIR}/ov_llama_2\")" - ] - }, - { - "cell_type": "markdown", - "id": "8367fa4b-d96a-4447-a26f-8a50544e908a", - "metadata": {}, - "source": [ - ">**Note**: [Raise access request](https://www.llama.com/llama-downloads) for Llama models as it is a gated repository.\n" - ] - }, - { - "cell_type": "markdown", - "id": "0d060a9e-0355-41e5-a33c-c8baf35d7481", - "metadata": {}, - "source": [ - "## Backend code for Text Summarization " - ] - }, - { - "cell_type": "markdown", - "id": "de840a1b-5913-455c-bb2e-fe90f4ebb545", - "metadata": {}, - "source": [ - "### Importing the necessary libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "9d1333e1-1785-4a4b-b426-a7d32640236c", - "metadata": {}, - "outputs": [], - "source": [ - "from transformers import AutoTokenizer, pipeline\n", - "from optimum.intel import OVModelForCausalLM\n", - "from langchain_community.llms import HuggingFacePipeline\n", - "from langchain.prompts import PromptTemplate\n", - "from langchain.chains import RetrievalQA\n", - "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", - "from langchain_chroma import Chroma\n", - "from langchain_community.embeddings import HuggingFaceEmbeddings\n", - "from langchain_community.document_loaders import WebBaseLoader, PyPDFLoader\n", - "import os\n", - "os.environ['USER_AGENT'] = 'myagent'\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "markdown", - "id": "7d43d5d3-88bf-4bd6-94bd-17c8ffb9006f", - "metadata": {}, - "source": [ - "### Prompt Templates for Summarization & Question Answering Bot\n", - "Here we have declared two variables for prompt templates so that it can be called later on , one template for summarization and one for query asked in the bot" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "de8469f9-5b72-4a2c-bc09-a4406a34a1c8", - "metadata": {}, - "outputs": [], - "source": [ - "#prompt template for summarization\n", - "summary_template= \"\"\"Write a concise summary of the following: \"{context}\" CONCISE SUMMARY: \"\"\"\n", - "#prompt template for query\n", - "query_template=\"\"\"Use the following pieces of context to answer the question at the end.\n", - " If you don't know the answer, just say that you don't know, don't try to make up an answer.\n", - " Use 10 words maximum and keep the answer as concise as possible in one sentence.\n", - " Always say \"thanks for asking!\" at the end of the answer.\n", - " \n", - " {context}\n", - " \n", - " Question: {question}\n", - " \n", - " Helpful Answer:\"\"\"" - ] - }, - { - "cell_type": "markdown", - "id": "a8958c7f-7241-4784-a6a9-0d59431b0e4e", - "metadata": {}, - "source": [ - "### Pre-process the Input File\n", - "\n", - "* Through browser plugin, users post an input file for summarization. \n", - "* Document loaders, i.e. WebBaseLoader & PyPDFLoader, in RAG are used to load page content from any Webpage/PDF and preprocess the documents that will be further used for retrieval during the summarization & question answering process.\n", - "* The loaded page data would be split using Recursive Character Text Splitter & embeddings are created using HuggingFace Embeddings. Here, RecursiveCharacterTextSplitter is used to split text into smaller pieces recursively at the character level.\n", - "* In RAG, embeddings plays a crucial role in retrieval of relevant documents for a given query and Sentence Transformers helps to generate embeddings for each document in your knowledge base.\n", - "* These embeddings are further stored into ChromaDB for further retrieval usage. Chroma is a vector store and embeddings database designed from the ground-up to make it easy to build AI applications with embeddings." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "dd83e322-d97f-420d-88cf-21ceba6261fe", - "metadata": {}, - "outputs": [], - "source": [ - "def pre_processing(loader):\n", - " \"\"\"\n", - " This function does the below steps in a sequential order:\n", - " 1. Loads page content from the webpage/PDF \n", - " 2. Splits the page data using Recursive Character Text Splitter & creates embeddings using HuggingFace Embeddings\n", - " 3. This is further stored into ChromaDB for futher retrieval usage\n", - " input: loader contains page data from a Webpage/PDF\n", - " output: returns a vectorstore\n", - " \"\"\"\n", - " try:\n", - " page_data = loader.load()\n", - " text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20)\n", - " all_splits = text_splitter.split_documents(page_data)\n", - " embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\")\n", - " global vectorstore\n", - " vectorstore = Chroma.from_documents(documents=all_splits, embedding=embeddings) \n", - " return vectorstore\n", - " except Exception as e:\n", - " print(\"Error while processing Webpage/PDF page content\\n\")\n", - " raise e" - ] - }, - { - "cell_type": "markdown", - "id": "1ae0f347-df38-4c93-a77c-ab978c00cfaf", - "metadata": {}, - "source": [ - "### Load LLM models\n", - "Below module:\n", - "1. Fetches the OpenVINO converted model & compiles it on GPU\n", - "2. Generate a HuggingFace Pipeline for Text-Generation.\n", - "3. Returns the model" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "6750172c-0d22-4e70-98d5-7a5ec29f4c36", - "metadata": {}, - "outputs": [], - "source": [ - "def load_llm(model_id):\n", - " \"\"\"\n", - " Meta Llama2 & Qwen 7B models are converted to OpenVINO IR Format. This function compiles those converted models on GPU.\n", - " input: user selected model_id from plugin\n", - " output: compiled model with openvino\n", - " \"\"\"\n", - " if model_id:\n", - " try:\n", - " if model_id==\"Meta LLama 2\":\n", - " model_path='models/ov_llama_2'\n", - " elif model_id==\"Qwen 7B Instruct\":\n", - " model_path='models/ov_qwen7b'\n", - " model = OVModelForCausalLM.from_pretrained(model_path , device='GPU')\n", - " tokenizer = AutoTokenizer.from_pretrained(model_path)\n", - " pipe=pipeline(\n", - " \"text-generation\",\n", - " model=model,\n", - " tokenizer=tokenizer,\n", - " max_new_tokens=4096, \n", - " device=model.device\n", - " )\n", - " global llm_model \n", - " llm_model = HuggingFacePipeline(pipeline=pipe)\n", - " return llm_model\n", - " except Exception as e:\n", - " print(\"Failed to load the model. Please check whether the model_path is correct\\n\")\n", - " raise e" - ] - }, - { - "cell_type": "markdown", - "id": "82f8da99-d624-4ca9-aa38-f7fb6f7c3200", - "metadata": {}, - "source": [ - "### URL Summarization\n", - "For a URL Summarization, we load the web page content when end-user enters a URL into the plugin using **WebBaseLoader** which in return loads the page data and passes into the RetrievalQA chain. When a question is being asked in the retreival QA chain , we try to get a concise summary and return it . Here we are using WebBaseLoader to load the documents from the web.\n", - "* The **WebBaseLoader** in Retrieval Augmented Generation (RAG) is a type of document loader that is designed to load documents from the web.The WebBaseLoader is used when the documents for retrieval are not stored locally or in a Hugging Face dataset, but are instead located on the web.\n", - "* **RetrievalQA** is a type of question answering system that uses a retriever to fetch relevant documents given a question, and then uses a reader to extract the answer from the retrieved documents.\n", - "\n", - "Below module:\n", - "1. Loads the page data from a webpage using WebBaseLoader.\n", - "2. Pre-processed the data & stores into a vector store.\n", - "3. Passes the prompt, vectorstore & LLM model into the chain & returns the summary to the plugin" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "edebde7e-f6df-4123-88e8-13f9f072d296", - "metadata": {}, - "outputs": [], - "source": [ - "def pre_process_url_data(urls):\n", - " \"\"\"\n", - " When an end user pastes a URL into the plugin, The RAW data is passed onto the RetrievalQA chain,\n", - " and the output is returned back to the plugin.\n", - " input: Webpage URL(str).\n", - " output: Glance Summary of the fetched URL.\n", - " \"\"\"\n", - " try:\n", - " loader = WebBaseLoader(urls)\n", - " global summ_vectorstore \n", - " summ_vectorstore = pre_processing(loader) # Common Helper function for processing data.\n", - " prompt = PromptTemplate(\n", - " template=summary_template,\n", - " input_variables=[\"context\", \"question\"]\n", - " )\n", - " \n", - " qa_chain = RetrievalQA.from_chain_type(\n", - " llm=llm_model,\n", - " retriever=summ_vectorstore.as_retriever(),\n", - " chain_type=\"stuff\",\n", - " chain_type_kwargs={\"prompt\": prompt},\n", - " return_source_documents=False,\n", - " )\n", - " \n", - " question = \"Please summarize the entire book in one paragraph of 100 words\"\n", - " summary = qa_chain(question)\n", - " response = summary['result']\n", - " summary_start = response.find(\"CONCISE SUMMARY:\")\n", - " concise_summary = response[summary_start + len(\"CONCISE SUMMARY:\"):].strip()\n", - " return concise_summary\n", - " except Exception as e:\n", - " print(\"Failed to summarize webpage\\n\")\n", - " raise e" - ] - }, - { - "cell_type": "markdown", - "id": "1ffe1bad-94b8-4afd-a906-235a532affb3", - "metadata": {}, - "source": [ - "### URL Question Answering BOT\n", - "The below module:\n", - "1. Fetches any follow up questions related to the summary post URL summarization.\n", - "2. Passes the **query template** which is declared as global into the prompt, vectorstore, compiled model to the chain.\n", - "3. When a question is being passed to the retrieval QA chain, the chain loads & posts a precise answer in a single sentence.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "c6ad4d92-c524-450b-b8af-e5630ed0b5bb", - "metadata": {}, - "outputs": [], - "source": [ - "def qa_on_url_summarized_text(query):\n", - " \"\"\"\n", - " This function fetches the query asked by the users post summarization from the URL, searches an answer from the vectorstore & returns answer in less than 10 words.\n", - " input: user's follow-up question(str)\n", - " output: Answer to the conversations.\n", - " \"\"\"\n", - " try:\n", - " prompt = PromptTemplate(\n", - " template=query_template,\n", - " input_variables=[\"context\", \"question\"]\n", - " )\n", - " reduce_chain = RetrievalQA.from_chain_type(\n", - " llm=llm_model,\n", - " retriever=summ_vectorstore.as_retriever(),\n", - " chain_type=\"stuff\",\n", - " chain_type_kwargs={\"prompt\": prompt},\n", - " return_source_documents=False\n", - " )\n", - " summary = reduce_chain({'query': query})\n", - " summ_vectorstore.delete\n", - " response = summary['result']\n", - " summary_start = response.find(\"Helpful Answer:\")\n", - " concise_summary = response[summary_start + len(\"Helpful Answer:\"):].strip()\n", - " return concise_summary\n", - " except Exception as e:\n", - " print(\"Error in Webpage Summarizer QA BoT\\n\")\n", - " raise e" - ] - }, - { - "cell_type": "markdown", - "id": "401468ef-6d4c-48e2-832f-3e8886aa38c8", - "metadata": {}, - "source": [ - "### PDF Summarization\n", - "\n", - "When end-users upload any PDF file to the plugin, page data is loaded, using **PyPDFLoader** and passed into the RetrievalQA chain for generating a concise summary. When a question is being asked post summarization, a precise answer is returned. \n", - "\n", - "* **PyPDFLoader** is a document loader within the LangChain framework specifically designed to handle PDF files. It allows you to extract text from PDF documents and load them into a format suitable for language models and other text-based applications.\n", - "\n", - "Below module: \n", - "1. Loads the page data from a webpage using PyPDFLoader.\n", - "2. Pre-processed the data & stores into a vector store.\n", - "3. Passes the prompt, vectorstore & LLM model into the chain & returns the summary to the plugin" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "83fc83ad-3e4a-4d4b-906e-01bbebd10ae3", - "metadata": {}, - "outputs": [], - "source": [ - "def pre_process_pdf_data(pdf):\n", - " \"\"\"\n", - " When an end user uploads a PDF into the plugin, The RAW data is passed onto the RetrievalQA chain,\n", - " and the output is returned back to the plugin.\n", - " input: PDF path(str).\n", - " output: Glance Summary of the uploaded PDF.\n", - " \"\"\"\n", - " try:\n", - " loader = PyPDFLoader(pdf, extract_images=False)\n", - " global pdf_vectorstore\n", - " pdf_vectorstore=pre_processing(loader)\n", - " \n", - " prompt = PromptTemplate(\n", - " template=summary_template,\n", - " input_variables=[\"context\", \"question\"]\n", - " )\n", - " reduce_chain = RetrievalQA.from_chain_type(\n", - " llm=llm_model,\n", - " retriever=pdf_vectorstore.as_retriever(),\n", - " chain_type=\"stuff\",\n", - " chain_type_kwargs={\"prompt\": prompt},\n", - " return_source_documents=False,\n", - " )\n", - " question = \"Please summarize the entire book in 100 words.\"\n", - " summary = reduce_chain({'query': question})\n", - "\n", - " response = summary['result']\n", - " summary_start = response.find(\"CONCISE SUMMARY:\")\n", - " concise_summary = response[summary_start + len(\"CONCISE SUMMARY:\"):].strip()\n", - " return concise_summary\n", - " except Exception as e:\n", - " print(\"Failed to summarize PDF \\n\")\n", - " raise e\n" - ] - }, - { - "cell_type": "markdown", - "id": "129c21bf-4e16-432b-a7e1-7ca72b690020", - "metadata": {}, - "source": [ - "### PDF Question Answering BOT\n", - "The below module:\n", - "1. Posts follow up questions, asked by end-users, related to the summary PDF post PDF summarization\n", - "2. Passes the **query template** which is declared as global into the prompt, vectorstore, compiled model to the chain.\n", - "3. When a question is being passed to the retrieval QA chain, the chain loads & posts a precise answer in a single sentence." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "2fd5d11d-0e12-4e1e-9669-31692af50159", - "metadata": {}, - "outputs": [], - "source": [ - "def qa_on_pdf_summarized_text(query):\n", - " \"\"\"\n", - " This function fetches the query asked by the users post summarization from the PDF, then after it searches an answer from the vectorstore & returns answer in less than 10 words.\n", - " input: user's follow-up question(str)\n", - " output: Answer to the conversations.\n", - " \"\"\"\n", - " try:\n", - " prompt = PromptTemplate(\n", - " template=query_template,\n", - " input_variables=[\"context\", \"question\"]\n", - " )\n", - " reduce_chain = RetrievalQA.from_chain_type(\n", - " llm=llm_model,\n", - " retriever=pdf_vectorstore.as_retriever(),\n", - " chain_type=\"stuff\",\n", - " chain_type_kwargs={\"prompt\": prompt},\n", - " return_source_documents=False\n", - " )\n", - " summary = reduce_chain({'query': query})\n", - " response = summary['result']\n", - " summary_start = response.find(\"Helpful Answer:\")\n", - " concise_summary = response[summary_start + len(\"Helpful Answer:\"):].strip()\n", - " return concise_summary\n", - " except Exception as e:\n", - " print(\"Error in PDF Summarizer QA BoT\")\n", - " raise e\n" - ] - }, - { - "cell_type": "markdown", - "id": "9325c0ec-aeb7-492c-8396-0cc5a4c21cb9", - "metadata": {}, - "source": [ - "## Server-side code" - ] - }, - { - "cell_type": "markdown", - "id": "7060492e-e311-4bf1-ad3c-1c31c55bda93", - "metadata": {}, - "source": [ - "### Importing necessary packages" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "1434779c-1d0b-49ec-bbb9-dbbd827f61bf", - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "from flask import Flask, Response, request, jsonify\n", - "from flask_cors import CORS\n", - "import tempfile\n", - "import chromadb" - ] - }, - { - "cell_type": "markdown", - "id": "0dfb527c-f34f-4d1d-92f9-6d7bdad50dee", - "metadata": {}, - "source": [ - "### Initializing the flask app and enabling CORS\n", - "Here we are initializing a flask and enabling CORS which allows the flask app tobe accessed and interacted with from other domains and we are restricting the types of files that can be uploaded to the application.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "54773857-d296-44c4-847e-d26da86b01f9", - "metadata": {}, - "outputs": [], - "source": [ - "app = Flask(__name__)\n", - "CORS(app) # This will enable CORS for all routes\n", - "ALLOWED_EXTENSIONS = {'txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif'}" - ] - }, - { - "cell_type": "markdown", - "id": "7a72ba5f-b056-4601-a0fc-d963c0cc36ad", - "metadata": {}, - "source": [ - "### Model Selection\n", - "The below module:\n", - "1. Fetches the model selected by the end-user through model_id.\n", - "2. Loads the model which would further trigger the model compilation function present in the main code for summarization.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "354d0e26-8719-4b54-888d-0264c0954416", - "metadata": {}, - "outputs": [], - "source": [ - "@app.route('/select-model', methods=['POST'])\n", - "def select_model():\n", - " \"\"\"\n", - " Model selection function which would further trigger Model compilation function.\n", - " \"\"\"\n", - " try:\n", - " global current_model\n", - " data = request.get_json()\n", - " model_id = data.get('model_id')\n", - " current_model = load_llm(model_id)\n", - " return jsonify({'message': f'Model {model_id} loaded successfully.'}), 200\n", - " \n", - " except Exception:\n", - " return jsonify({'message': f'Failed to load model'}), 500" - ] - }, - { - "cell_type": "markdown", - "id": "6f69e9b5-ddf7-470f-ad93-cffea37f793a", - "metadata": {}, - "source": [ - "### Yielding Summary onto the plugin\n", - "This is a generator function which helps to Stream and yield the response content chunk by chunk" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "8f664170-bf7c-41b5-b41b-45217a5c1724", - "metadata": {}, - "outputs": [], - "source": [ - "def stream_output(process_function, *args):\n", - " \"\"\"\n", - " Generator function to stream output from a process function.\n", - " \"\"\"\n", - " try:\n", - " for chunk in process_function(*args):\n", - " if chunk is not None:\n", - " yield f\"{chunk}\"\n", - " except Exception:\n", - " yield f\"Error while streaming output\"\n" - ] - }, - { - "cell_type": "markdown", - "id": "c789173e-f5f3-4c47-b576-d167a70a21b0", - "metadata": {}, - "source": [ - "### URL processing code\n", - "This will fetch the URL from the user's input from the plugin and trigger the URL summarization function present in the main code" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "36575ff6-f3ee-4ddc-8c56-689c8b6844bd", - "metadata": {}, - "outputs": [], - "source": [ - "@app.route('/process-url', methods=['POST'])\n", - "def process_url():\n", - " \"\"\"\n", - " Fetches URL from the plugin & triggers the URL summarization function.\n", - " \"\"\"\n", - " try:\n", - " data = request.get_json()\n", - " url = data.get('url') \n", - " if not url:\n", - " return jsonify({'message': 'No URL provided'}), 400\n", - " chromadb.api.client.SharedSystemClient.clear_system_cache()\n", - " return Response(stream_output(pre_process_url_data, [url]), content_type='text/event-stream')\n", - " \n", - " except Exception: \n", - " return jsonify({'message': f'Error while processing URL'}), 400\n" - ] - }, - { - "cell_type": "markdown", - "id": "833feed5-8e31-4c1f-8bb2-598322208755", - "metadata": {}, - "source": [ - "### PDF processing code\n", - "This function takes the PDF uploaded by the user and trigger the PDF summarization function present in the main code" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "6ea9ce78-c713-4204-ad68-9b9a9e9a79f2", - "metadata": {}, - "outputs": [], - "source": [ - "@app.route('/upload-pdf', methods=['POST'])\n", - "def upload_pdf():\n", - " \"\"\"\n", - " Once the PDF's uploaded, the PDF Summarization function's triggered.\n", - " \"\"\"\n", - " \n", - " pdf_file = request.files['pdf'] \n", - " if pdf_file and pdf_file.content_type == 'application/pdf':\n", - " try:\n", - " with tempfile.NamedTemporaryFile(delete=False, suffix=\".pdf\") as temp_pdf:\n", - " pdf_file.save(temp_pdf.name)\n", - " temp_pdf_path = temp_pdf.name\n", - " print(temp_pdf_path)\n", - " \n", - " chromadb.api.client.SharedSystemClient.clear_system_cache()\n", - " return Response(stream_output(pre_process_pdf_data, temp_pdf_path), content_type='text/event-stream')\n", - " \n", - " except Exception:\n", - " return jsonify({\"message\": f\"Error processing PDF:\"}), 500\n", - " \n", - " else:\n", - " return jsonify({\"message\": \"Invalid file type. Please upload a PDF.\"}), 400\n", - " " - ] - }, - { - "cell_type": "markdown", - "id": "56256d03-048c-4dfe-ae5f-2e7b53f0aa66", - "metadata": {}, - "source": [ - "### PDF Query code for Question Answering Bot\n", - "Once the PDF content summarization is done user asks query to the Question Answering bot which gets triggered to the Query for the PDF function present in the main code" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "7b4f6d9e-9007-461c-88ed-13260d5cf559", - "metadata": {}, - "outputs": [], - "source": [ - "@app.route('/your_query_pdf', methods=['POST'])\n", - "def pdf_process_query():\n", - " \"\"\"\n", - " This function triggers the PDF Question Answering Bot\n", - " \"\"\"\n", - " try:\n", - " data = request.get_json()\n", - " query=data.get('query')\n", - " if not data:\n", - " return jsonify({'message':'no query provided'}),400\n", - " response_message=str(qa_on_pdf_summarized_text(query))\n", - " return jsonify({'message': response_message}), 200\n", - " except Exception:\n", - " return jsonify({'message': f'Error while PDF QA Bot'}), 500\n" - ] - }, - { - "cell_type": "markdown", - "id": "589d6199-fafa-4340-9c08-af63b23f15a2", - "metadata": {}, - "source": [ - "### URL Query code for Question Answering Bot\n", - "Once the URL content summarization is done user asks query to the Question Answering bot which gets triggered to the Query for the URL function present in the main code" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "873be699-2bf8-4238-88cd-f3c36431fd85", - "metadata": {}, - "outputs": [], - "source": [ - "@app.route('/your_query_url', methods=['POST'])\n", - "def url_process_query():\n", - " \"\"\"\n", - " This function triggers the URL question answering Bot\n", - " \"\"\"\n", - " try:\n", - " data = request.get_json()\n", - " query=data.get('query')\n", - " if not data:\n", - " return jsonify({'message':'no query provided'}),400\n", - " response_message=str(qa_on_url_summarized_text(query))\n", - " return jsonify({'message': response_message}), 200\n", - " except Exception:\n", - " return jsonify({'message': f'Error while URL QA Bot'}), 500\n" - ] - }, - { - "cell_type": "markdown", - "id": "40f1be65-2d3b-4fce-bb31-baa7a6573e25", - "metadata": {}, - "source": [ - "### Launch Plugin via Flask Server\n", - "\n", - "#### 🎉 Flask server is Ready! 🎉\n", - "\n", - "Your application is now live and waiting for interaction!\n", - "\n", - "**🚀 Essential Step: Activate Your Browser Plugin!**\n", - "\n", - "- This application operates through its dedicated browser extension.\n", - "- To begin, please open your web browser and locate the plugin's icon, which looks like `T`, in your toolbar (it's often in the top-right corner).\n", - "- Click on the `T` icon to access the browser extension\n", - " > **NOTE**: Do not access the flask server API Endpoint as the application is running on **browser extension**. \n", - "\n", - "**Having trouble?**\n", - "- Is the plugin loaded? If you haven't already, please load it by following the Readme.md file.\n", - "- Is it enabled? Double-check your browser's extension settings to ensure the plugin isn't disabled.\n", - "- Have you pinned the extension? Pin the extension.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "4138fa1d-5f11-4629-a636-f17cc114644d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " * Serving Flask app '__main__'\n", - " * Debug mode: off\n" - ] + "cells": [ + { + "cell_type": "markdown", + "id": "52a9a997-e2e8-43cf-9e40-5c777b4b9723", + "metadata": {}, + "source": [ + "# Text Summarizer Plugin" + ] + }, + { + "cell_type": "markdown", + "id": "99707adf-f1be-4e00-aad4-8187f8c816cf", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Chrome extension seamlessly integrates with Flask and leverages an OpenVINO backend for fast and efficient summarization of webpages (via URL) and PDFs (via upload). Powered by LangChain tools, it handles advanced tasks like text splitting and vectorstore management to deliver accurate and meaningful summaries." + ] + }, + { + "cell_type": "markdown", + "id": "e56b6e8b-fcda-4fd1-9f4f-baf028233ea0", + "metadata": {}, + "source": [ + "## How it Works\n", + "\n", + "\"image\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "d11ee514-4487-45d3-984e-6f6e291b61f0", + "metadata": {}, + "source": [ + "## Pre-requisites" + ] + }, + { + "cell_type": "markdown", + "id": "c07f0ea0-c374-4048-b907-bf8a9fd43a9b", + "metadata": {}, + "source": [ + "### Follow the below steps to prepare the environment" + ] + }, + { + "cell_type": "markdown", + "id": "479ef7d9-b2d0-4b38-9829-f92224ee9998", + "metadata": {}, + "source": [ + "Before converting the models & running the plugin, make sure you have followed all the below listed [steps to prepare the environment](./Readme.md/#setup-environment-with-uv)\n", + "- Install uv by refering to [this documentation](https://docs.astral.sh/uv/getting-started/installation/)\n", + "- Sync dependencies\n", + " ```bash\n", + " uv sync\n", + " ```\n", + "Load and pin the unpacked extension as mentioned [here](./Readme.md/):\n", + "- Navigate to the Extensions page by entering chrome://extensions in a new tab. (By design chrome:// URLs are not linkable.)\n", + " - Alternatively, click the Extensions menu puzzle button and select Manage Extensions at the bottom of the menu.\n", + " - Or, click the Chrome menu, hover over More Tools, then select Extensions.\n", + "- Enable Developer Mode by clicking the toggle switch next to Developer mode.\n", + "- Click the Load unpacked button and select the extension directory.\n", + "- Refer to Chrome’s development documentation for further details.\n", + "- Pin your extension to the toolbar for a quick access." + ] + }, + { + "cell_type": "markdown", + "id": "dc939a97-428b-4c61-93ba-51cfce27c54f", + "metadata": {}, + "source": [ + "### Download and Convert the Huggingface Model to OpenVINO IR Format:\n", + "\n", + "#### Login to Huggingface:\n", + "- Generate a token from Huggingface for private/gated models like Meta Llama, etc. To access such private/gated models, refer to [Huggingface documentation](https://huggingface.co/docs/hub/en/models-gated).\n", + "- If you already have access to such gated models, know how to generate an access token by referring [this](https://huggingface.co/docs/hub/en/security-tokens)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0c9347f7-09dc-4324-b1e2-253c4d5a9f2f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "55c1c17acff446019b03719e22175c8a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value='
Converting LLMs to OpenVINO IR Format could take sometime.. Meanwhile, have a milkshake!🧋 " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "29a146b7-2aad-43be-8a8a-68bb1d885a02", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pathlib import Path\n", + "import os\n", + "ROOT_DIR = Path.cwd()\n", + "MODEL_DIR = ROOT_DIR / 'models'\n", + "MODEL_DIR.mkdir(parents=True, exist_ok=True)\n", + "os.system(f\"uv run optimum-cli export openvino --model Qwen/Qwen2-7B-Instruct --weight-format int4 {MODEL_DIR}/ov_qwen7b\")\n", + "os.system(f\"uv run optimum-cli export openvino --model meta-llama/Llama-2-7b-chat-hf --weight-format int4 {MODEL_DIR}/ov_llama_2\")" + ] + }, + { + "cell_type": "markdown", + "id": "8367fa4b-d96a-4447-a26f-8a50544e908a", + "metadata": {}, + "source": [ + ">**Note**: [Raise access request](https://www.llama.com/llama-downloads) for Llama models as it is a gated repository.\n" + ] + }, + { + "cell_type": "markdown", + "id": "0d060a9e-0355-41e5-a33c-c8baf35d7481", + "metadata": {}, + "source": [ + "## Backend code for Text Summarization " + ] + }, + { + "cell_type": "markdown", + "id": "de840a1b-5913-455c-bb2e-fe90f4ebb545", + "metadata": {}, + "source": [ + "### Importing the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "9d1333e1-1785-4a4b-b426-a7d32640236c", + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import AutoTokenizer, pipeline\n", + "from optimum.intel import OVModelForCausalLM\n", + "from langchain_community.llms import HuggingFacePipeline\n", + "from langchain.prompts import PromptTemplate\n", + "from langchain.chains import RetrievalQA\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", + "from langchain_chroma import Chroma\n", + "from langchain_community.embeddings import HuggingFaceEmbeddings\n", + "from langchain_community.document_loaders import WebBaseLoader, PyPDFLoader\n", + "import os\n", + "os.environ['USER_AGENT'] = 'myagent'\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "id": "7d43d5d3-88bf-4bd6-94bd-17c8ffb9006f", + "metadata": {}, + "source": [ + "### Prompt Templates for Summarization & Question Answering Bot\n", + "Here we have declared two variables for prompt templates so that it can be called later on , one template for summarization and one for query asked in the bot" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "de8469f9-5b72-4a2c-bc09-a4406a34a1c8", + "metadata": {}, + "outputs": [], + "source": [ + "#prompt template for summarization\n", + "summary_template= \"\"\"Write a concise summary of the following: \"{context}\" CONCISE SUMMARY: \"\"\"\n", + "#prompt template for query\n", + "query_template=\"\"\"Use the following pieces of context to answer the question at the end.\n", + " If you don't know the answer, just say that you don't know, don't try to make up an answer.\n", + " Use 10 words maximum and keep the answer as concise as possible in one sentence.\n", + " Always say \"thanks for asking!\" at the end of the answer.\n", + " \n", + " {context}\n", + " \n", + " Question: {question}\n", + " \n", + " Helpful Answer:\"\"\"" + ] + }, + { + "cell_type": "markdown", + "id": "a8958c7f-7241-4784-a6a9-0d59431b0e4e", + "metadata": {}, + "source": [ + "### Pre-process the Input File\n", + "\n", + "* Through browser plugin, users post an input file for summarization. \n", + "* Document loaders, i.e. WebBaseLoader & PyPDFLoader, in RAG are used to load page content from any Webpage/PDF and preprocess the documents that will be further used for retrieval during the summarization & question answering process.\n", + "* The loaded page data would be split using Recursive Character Text Splitter & embeddings are created using HuggingFace Embeddings. Here, RecursiveCharacterTextSplitter is used to split text into smaller pieces recursively at the character level.\n", + "* In RAG, embeddings plays a crucial role in retrieval of relevant documents for a given query and Sentence Transformers helps to generate embeddings for each document in your knowledge base.\n", + "* These embeddings are further stored into ChromaDB for further retrieval usage. Chroma is a vector store and embeddings database designed from the ground-up to make it easy to build AI applications with embeddings." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "dd83e322-d97f-420d-88cf-21ceba6261fe", + "metadata": {}, + "outputs": [], + "source": [ + "def pre_processing(loader):\n", + " \"\"\"\n", + " This function does the below steps in a sequential order:\n", + " 1. Loads page content from the webpage/PDF \n", + " 2. Splits the page data using Recursive Character Text Splitter & creates embeddings using HuggingFace Embeddings\n", + " 3. This is further stored into ChromaDB for futher retrieval usage\n", + " input: loader contains page data from a Webpage/PDF\n", + " output: returns a vectorstore\n", + " \"\"\"\n", + " try:\n", + " page_data = loader.load()\n", + " text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20)\n", + " all_splits = text_splitter.split_documents(page_data)\n", + " embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\")\n", + " global vectorstore\n", + " vectorstore = Chroma.from_documents(documents=all_splits, embedding=embeddings) \n", + " return vectorstore\n", + " except Exception as e:\n", + " print(\"Error while processing Webpage/PDF page content\\n\")\n", + " raise e" + ] + }, + { + "cell_type": "markdown", + "id": "1ae0f347-df38-4c93-a77c-ab978c00cfaf", + "metadata": {}, + "source": [ + "### Load LLM models\n", + "Below module:\n", + "1. Fetches the OpenVINO converted model & compiles it on GPU\n", + "2. Generate a HuggingFace Pipeline for Text-Generation.\n", + "3. Returns the model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6750172c-0d22-4e70-98d5-7a5ec29f4c36", + "metadata": {}, + "outputs": [], + "source": [ + "def load_llm(model_id):\n", + " \"\"\"\n", + " Meta Llama2 & Qwen 7B models are converted to OpenVINO IR Format. This function compiles those converted models on GPU.\n", + " input: user selected model_id from plugin\n", + " output: compiled model with openvino\n", + " \"\"\"\n", + " if model_id:\n", + " try:\n", + " if model_id==\"Meta LLama 2\":\n", + " model_path='models/ov_llama_2'\n", + " elif model_id==\"Qwen 7B Instruct\":\n", + " model_path='models/ov_qwen7b'\n", + " model = OVModelForCausalLM.from_pretrained(model_path , device='GPU')\n", + " tokenizer = AutoTokenizer.from_pretrained(model_path)\n", + " pipe=pipeline(\n", + " \"text-generation\",\n", + " model=model,\n", + " tokenizer=tokenizer,\n", + " max_new_tokens=4096, \n", + " device=model.device\n", + " )\n", + " global llm_model \n", + " llm_model = HuggingFacePipeline(pipeline=pipe)\n", + " return llm_model\n", + " except Exception as e:\n", + " print(\"Failed to load the model. Please check whether the model_path is correct\\n\")\n", + " raise e" + ] + }, + { + "cell_type": "markdown", + "id": "82f8da99-d624-4ca9-aa38-f7fb6f7c3200", + "metadata": {}, + "source": [ + "### URL Summarization\n", + "For a URL Summarization, we load the web page content when end-user enters a URL into the plugin using **WebBaseLoader** which in return loads the page data and passes into the RetrievalQA chain. When a question is being asked in the retreival QA chain , we try to get a concise summary and return it . Here we are using WebBaseLoader to load the documents from the web.\n", + "* The **WebBaseLoader** in Retrieval Augmented Generation (RAG) is a type of document loader that is designed to load documents from the web.The WebBaseLoader is used when the documents for retrieval are not stored locally or in a Hugging Face dataset, but are instead located on the web.\n", + "* **RetrievalQA** is a type of question answering system that uses a retriever to fetch relevant documents given a question, and then uses a reader to extract the answer from the retrieved documents.\n", + "\n", + "Below module:\n", + "1. Loads the page data from a webpage using WebBaseLoader.\n", + "2. Pre-processed the data & stores into a vector store.\n", + "3. Passes the prompt, vectorstore & LLM model into the chain & returns the summary to the plugin" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "edebde7e-f6df-4123-88e8-13f9f072d296", + "metadata": {}, + "outputs": [], + "source": [ + "def pre_process_url_data(urls):\n", + " \"\"\"\n", + " When an end user pastes a URL into the plugin, The RAW data is passed onto the RetrievalQA chain,\n", + " and the output is returned back to the plugin.\n", + " input: Webpage URL(str).\n", + " output: Glance Summary of the fetched URL.\n", + " \"\"\"\n", + " try:\n", + " loader = WebBaseLoader(urls)\n", + " global summ_vectorstore \n", + " summ_vectorstore = pre_processing(loader) # Common Helper function for processing data.\n", + " prompt = PromptTemplate(\n", + " template=summary_template,\n", + " input_variables=[\"context\", \"question\"]\n", + " )\n", + " \n", + " qa_chain = RetrievalQA.from_chain_type(\n", + " llm=llm_model,\n", + " retriever=summ_vectorstore.as_retriever(),\n", + " chain_type=\"stuff\",\n", + " chain_type_kwargs={\"prompt\": prompt},\n", + " return_source_documents=False,\n", + " )\n", + " \n", + " question = \"Please summarize the entire book in one paragraph of 100 words\"\n", + " summary = qa_chain(question)\n", + " response = summary['result']\n", + " summary_start = response.find(\"CONCISE SUMMARY:\")\n", + " concise_summary = response[summary_start + len(\"CONCISE SUMMARY:\"):].strip()\n", + " return concise_summary\n", + " except Exception as e:\n", + " print(\"Failed to summarize webpage\\n\")\n", + " raise e" + ] + }, + { + "cell_type": "markdown", + "id": "1ffe1bad-94b8-4afd-a906-235a532affb3", + "metadata": {}, + "source": [ + "### URL Question Answering BOT\n", + "The below module:\n", + "1. Fetches any follow up questions related to the summary post URL summarization.\n", + "2. Passes the **query template** which is declared as global into the prompt, vectorstore, compiled model to the chain.\n", + "3. When a question is being passed to the retrieval QA chain, the chain loads & posts a precise answer in a single sentence.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c6ad4d92-c524-450b-b8af-e5630ed0b5bb", + "metadata": {}, + "outputs": [], + "source": [ + "def qa_on_url_summarized_text(query):\n", + " \"\"\"\n", + " This function fetches the query asked by the users post summarization from the URL, searches an answer from the vectorstore & returns answer in less than 10 words.\n", + " input: user's follow-up question(str)\n", + " output: Answer to the conversations.\n", + " \"\"\"\n", + " try:\n", + " prompt = PromptTemplate(\n", + " template=query_template,\n", + " input_variables=[\"context\", \"question\"]\n", + " )\n", + " reduce_chain = RetrievalQA.from_chain_type(\n", + " llm=llm_model,\n", + " retriever=summ_vectorstore.as_retriever(),\n", + " chain_type=\"stuff\",\n", + " chain_type_kwargs={\"prompt\": prompt},\n", + " return_source_documents=False\n", + " )\n", + " summary = reduce_chain({'query': query})\n", + " summ_vectorstore.delete\n", + " response = summary['result']\n", + " summary_start = response.find(\"Helpful Answer:\")\n", + " concise_summary = response[summary_start + len(\"Helpful Answer:\"):].strip()\n", + " return concise_summary\n", + " except Exception as e:\n", + " print(\"Error in Webpage Summarizer QA BoT\\n\")\n", + " raise e" + ] + }, + { + "cell_type": "markdown", + "id": "401468ef-6d4c-48e2-832f-3e8886aa38c8", + "metadata": {}, + "source": [ + "### PDF Summarization\n", + "\n", + "When end-users upload any PDF file to the plugin, page data is loaded, using **PyPDFLoader** and passed into the RetrievalQA chain for generating a concise summary. When a question is being asked post summarization, a precise answer is returned. \n", + "\n", + "* **PyPDFLoader** is a document loader within the LangChain framework specifically designed to handle PDF files. It allows you to extract text from PDF documents and load them into a format suitable for language models and other text-based applications.\n", + "\n", + "Below module: \n", + "1. Loads the page data from a webpage using PyPDFLoader.\n", + "2. Pre-processed the data & stores into a vector store.\n", + "3. Passes the prompt, vectorstore & LLM model into the chain & returns the summary to the plugin" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "83fc83ad-3e4a-4d4b-906e-01bbebd10ae3", + "metadata": {}, + "outputs": [], + "source": [ + "def pre_process_pdf_data(pdf):\n", + " \"\"\"\n", + " When an end user uploads a PDF into the plugin, The RAW data is passed onto the RetrievalQA chain,\n", + " and the output is returned back to the plugin.\n", + " input: PDF path(str).\n", + " output: Glance Summary of the uploaded PDF.\n", + " \"\"\"\n", + " try:\n", + " loader = PyPDFLoader(pdf, extract_images=False)\n", + " global pdf_vectorstore\n", + " pdf_vectorstore=pre_processing(loader)\n", + " \n", + " prompt = PromptTemplate(\n", + " template=summary_template,\n", + " input_variables=[\"context\", \"question\"]\n", + " )\n", + " reduce_chain = RetrievalQA.from_chain_type(\n", + " llm=llm_model,\n", + " retriever=pdf_vectorstore.as_retriever(),\n", + " chain_type=\"stuff\",\n", + " chain_type_kwargs={\"prompt\": prompt},\n", + " return_source_documents=False,\n", + " )\n", + " question = \"Please summarize the entire book in 100 words.\"\n", + " summary = reduce_chain({'query': question})\n", + "\n", + " response = summary['result']\n", + " summary_start = response.find(\"CONCISE SUMMARY:\")\n", + " concise_summary = response[summary_start + len(\"CONCISE SUMMARY:\"):].strip()\n", + " return concise_summary\n", + " except Exception as e:\n", + " print(\"Failed to summarize PDF \\n\")\n", + " raise e\n" + ] + }, + { + "cell_type": "markdown", + "id": "129c21bf-4e16-432b-a7e1-7ca72b690020", + "metadata": {}, + "source": [ + "### PDF Question Answering BOT\n", + "The below module:\n", + "1. Posts follow up questions, asked by end-users, related to the summary PDF post PDF summarization\n", + "2. Passes the **query template** which is declared as global into the prompt, vectorstore, compiled model to the chain.\n", + "3. When a question is being passed to the retrieval QA chain, the chain loads & posts a precise answer in a single sentence." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "2fd5d11d-0e12-4e1e-9669-31692af50159", + "metadata": {}, + "outputs": [], + "source": [ + "def qa_on_pdf_summarized_text(query):\n", + " \"\"\"\n", + " This function fetches the query asked by the users post summarization from the PDF, then after it searches an answer from the vectorstore & returns answer in less than 10 words.\n", + " input: user's follow-up question(str)\n", + " output: Answer to the conversations.\n", + " \"\"\"\n", + " try:\n", + " prompt = PromptTemplate(\n", + " template=query_template,\n", + " input_variables=[\"context\", \"question\"]\n", + " )\n", + " reduce_chain = RetrievalQA.from_chain_type(\n", + " llm=llm_model,\n", + " retriever=pdf_vectorstore.as_retriever(),\n", + " chain_type=\"stuff\",\n", + " chain_type_kwargs={\"prompt\": prompt},\n", + " return_source_documents=False\n", + " )\n", + " summary = reduce_chain({'query': query})\n", + " response = summary['result']\n", + " summary_start = response.find(\"Helpful Answer:\")\n", + " concise_summary = response[summary_start + len(\"Helpful Answer:\"):].strip()\n", + " return concise_summary\n", + " except Exception as e:\n", + " print(\"Error in PDF Summarizer QA BoT\")\n", + " raise e\n" + ] + }, + { + "cell_type": "markdown", + "id": "9325c0ec-aeb7-492c-8396-0cc5a4c21cb9", + "metadata": {}, + "source": [ + "## Server-side code" + ] + }, + { + "cell_type": "markdown", + "id": "7060492e-e311-4bf1-ad3c-1c31c55bda93", + "metadata": {}, + "source": [ + "### Importing necessary packages" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1434779c-1d0b-49ec-bbb9-dbbd827f61bf", + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "from flask import Flask, Response, request, jsonify\n", + "from flask_cors import CORS\n", + "import tempfile\n", + "import chromadb" + ] + }, + { + "cell_type": "markdown", + "id": "0dfb527c-f34f-4d1d-92f9-6d7bdad50dee", + "metadata": {}, + "source": [ + "### Initializing the flask app and enabling CORS\n", + "Here we are initializing a flask and enabling CORS which allows the flask app tobe accessed and interacted with from other domains and we are restricting the types of files that can be uploaded to the application.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "54773857-d296-44c4-847e-d26da86b01f9", + "metadata": {}, + "outputs": [], + "source": [ + "app = Flask(__name__)\n", + "CORS(app) # This will enable CORS for all routes\n", + "ALLOWED_EXTENSIONS = {'txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif'}" + ] + }, + { + "cell_type": "markdown", + "id": "7a72ba5f-b056-4601-a0fc-d963c0cc36ad", + "metadata": {}, + "source": [ + "### Model Selection\n", + "The below module:\n", + "1. Fetches the model selected by the end-user through model_id.\n", + "2. Loads the model which would further trigger the model compilation function present in the main code for summarization.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "354d0e26-8719-4b54-888d-0264c0954416", + "metadata": {}, + "outputs": [], + "source": [ + "@app.route('/select-model', methods=['POST'])\n", + "def select_model():\n", + " \"\"\"\n", + " Model selection function which would further trigger Model compilation function.\n", + " \"\"\"\n", + " try:\n", + " global current_model\n", + " data = request.get_json()\n", + " model_id = data.get('model_id')\n", + " current_model = load_llm(model_id)\n", + " return jsonify({'message': f'Model {model_id} loaded successfully.'}), 200\n", + " \n", + " except Exception:\n", + " return jsonify({'message': f'Failed to load model'}), 500" + ] + }, + { + "cell_type": "markdown", + "id": "6f69e9b5-ddf7-470f-ad93-cffea37f793a", + "metadata": {}, + "source": [ + "### Yielding Summary onto the plugin\n", + "This is a generator function which helps to Stream and yield the response content chunk by chunk" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8f664170-bf7c-41b5-b41b-45217a5c1724", + "metadata": {}, + "outputs": [], + "source": [ + "def stream_output(process_function, *args):\n", + " \"\"\"\n", + " Generator function to stream output from a process function.\n", + " \"\"\"\n", + " try:\n", + " for chunk in process_function(*args):\n", + " if chunk is not None:\n", + " yield f\"{chunk}\"\n", + " except Exception:\n", + " yield f\"Error while streaming output\"\n" + ] + }, + { + "cell_type": "markdown", + "id": "c789173e-f5f3-4c47-b576-d167a70a21b0", + "metadata": {}, + "source": [ + "### URL processing code\n", + "This will fetch the URL from the user's input from the plugin and trigger the URL summarization function present in the main code" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "36575ff6-f3ee-4ddc-8c56-689c8b6844bd", + "metadata": {}, + "outputs": [], + "source": [ + "@app.route('/process-url', methods=['POST'])\n", + "def process_url():\n", + " \"\"\"\n", + " Fetches URL from the plugin & triggers the URL summarization function.\n", + " \"\"\"\n", + " try:\n", + " data = request.get_json()\n", + " url = data.get('url') \n", + " if not url:\n", + " return jsonify({'message': 'No URL provided'}), 400\n", + " chromadb.api.client.SharedSystemClient.clear_system_cache()\n", + " return Response(stream_output(pre_process_url_data, [url]), content_type='text/event-stream')\n", + " \n", + " except Exception: \n", + " return jsonify({'message': f'Error while processing URL'}), 400\n" + ] + }, + { + "cell_type": "markdown", + "id": "833feed5-8e31-4c1f-8bb2-598322208755", + "metadata": {}, + "source": [ + "### PDF processing code\n", + "This function takes the PDF uploaded by the user and trigger the PDF summarization function present in the main code" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "6ea9ce78-c713-4204-ad68-9b9a9e9a79f2", + "metadata": {}, + "outputs": [], + "source": [ + "@app.route('/upload-pdf', methods=['POST'])\n", + "def upload_pdf():\n", + " \"\"\"\n", + " Once the PDF's uploaded, the PDF Summarization function's triggered.\n", + " \"\"\"\n", + " \n", + " pdf_file = request.files['pdf'] \n", + " if pdf_file and pdf_file.content_type == 'application/pdf':\n", + " try:\n", + " with tempfile.NamedTemporaryFile(delete=False, suffix=\".pdf\") as temp_pdf:\n", + " pdf_file.save(temp_pdf.name)\n", + " temp_pdf_path = temp_pdf.name\n", + " print(temp_pdf_path)\n", + " \n", + " chromadb.api.client.SharedSystemClient.clear_system_cache()\n", + " return Response(stream_output(pre_process_pdf_data, temp_pdf_path), content_type='text/event-stream')\n", + " \n", + " except Exception:\n", + " return jsonify({\"message\": f\"Error processing PDF:\"}), 500\n", + " \n", + " else:\n", + " return jsonify({\"message\": \"Invalid file type. Please upload a PDF.\"}), 400\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "56256d03-048c-4dfe-ae5f-2e7b53f0aa66", + "metadata": {}, + "source": [ + "### PDF Query code for Question Answering Bot\n", + "Once the PDF content summarization is done user asks query to the Question Answering bot which gets triggered to the Query for the PDF function present in the main code" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "7b4f6d9e-9007-461c-88ed-13260d5cf559", + "metadata": {}, + "outputs": [], + "source": [ + "@app.route('/your_query_pdf', methods=['POST'])\n", + "def pdf_process_query():\n", + " \"\"\"\n", + " This function triggers the PDF Question Answering Bot\n", + " \"\"\"\n", + " try:\n", + " data = request.get_json()\n", + " query=data.get('query')\n", + " if not data:\n", + " return jsonify({'message':'no query provided'}),400\n", + " response_message=str(qa_on_pdf_summarized_text(query))\n", + " return jsonify({'message': response_message}), 200\n", + " except Exception:\n", + " return jsonify({'message': f'Error while PDF QA Bot'}), 500\n" + ] + }, + { + "cell_type": "markdown", + "id": "589d6199-fafa-4340-9c08-af63b23f15a2", + "metadata": {}, + "source": [ + "### URL Query code for Question Answering Bot\n", + "Once the URL content summarization is done user asks query to the Question Answering bot which gets triggered to the Query for the URL function present in the main code" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "873be699-2bf8-4238-88cd-f3c36431fd85", + "metadata": {}, + "outputs": [], + "source": [ + "@app.route('/your_query_url', methods=['POST'])\n", + "def url_process_query():\n", + " \"\"\"\n", + " This function triggers the URL question answering Bot\n", + " \"\"\"\n", + " try:\n", + " data = request.get_json()\n", + " query=data.get('query')\n", + " if not data:\n", + " return jsonify({'message':'no query provided'}),400\n", + " response_message=str(qa_on_url_summarized_text(query))\n", + " return jsonify({'message': response_message}), 200\n", + " except Exception:\n", + " return jsonify({'message': f'Error while URL QA Bot'}), 500\n" + ] + }, + { + "cell_type": "markdown", + "id": "40f1be65-2d3b-4fce-bb31-baa7a6573e25", + "metadata": {}, + "source": [ + "### Launch Plugin via Flask Server\n", + "\n", + "#### 🎉 Flask server is Ready! 🎉\n", + "\n", + "Your application is now live and waiting for interaction!\n", + "\n", + "**🚀 Essential Step: Activate Your Browser Plugin!**\n", + "\n", + "- This application operates through its dedicated browser extension.\n", + "- To begin, please open your web browser and locate the plugin's icon, which looks like `T`, in your toolbar (it's often in the top-right corner).\n", + "- Click on the `T` icon to access the browser extension\n", + " > **NOTE**: Do not access the flask server API Endpoint as the application is running on **browser extension**. \n", + "\n", + "**Having trouble?**\n", + "- Is the plugin loaded? If you haven't already, please load it by following the Readme.md file.\n", + "- Is it enabled? Double-check your browser's extension settings to ensure the plugin isn't disabled.\n", + "- Have you pinned the extension? Pin the extension.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4138fa1d-5f11-4629-a636-f17cc114644d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " * Serving Flask app '__main__'\n", + " * Debug mode: off\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\n", + " * Running on http://127.0.0.1:5000\n", + "Press CTRL+C to quit\n", + "Provided model does not contain state. It may lead to sub-optimal performance.Please reexport model with updated OpenVINO version >= 2023.3.0 calling the `from_pretrained` method with original model and `export=True` parameter\n", + "Compiling the model to GPU ...\n", + "Setting OpenVINO CACHE_DIR to models\\ov_qwen7b\\model_cache\n", + "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n", + "127.0.0.1 - - [28/May/2025 03:19:51] \"POST /select-model HTTP/1.1\" 200 -\n" + ] + } + ], + "source": [ + "app.run(port=5000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1a5379a7-3e4e-4dbb-acbc-145f731318e1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": { + "0806da8e21ef43fb954d04a758b1672d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "2537e0551036498b92794ff751292dc9": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": {} + }, + "417fd8dd33434e4f83992900ab07bca3": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": {} + }, + "46b975c57854465abb7fcf9811236333": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ButtonModel", + "state": { + "description": "Login", + "layout": "IPY_MODEL_2537e0551036498b92794ff751292dc9", + "style": "IPY_MODEL_658454d94e3943fcb807696652229efc", + "tooltip": null + } + }, + "55c1c17acff446019b03719e22175c8a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "VBoxModel", + "state": { + "children": [ + "IPY_MODEL_55f83d8af4f14bcf919a7be7172fb1a1", + "IPY_MODEL_7d8e9a2182e44135b426f74599105380", + "IPY_MODEL_ecd4f83965d344e19d38b2bb8f3cad30", + "IPY_MODEL_46b975c57854465abb7fcf9811236333", + "IPY_MODEL_db1809374aaa4a07878cc6d9867f6954" + ], + "layout": "IPY_MODEL_cf95eb33c7f749afab6ae07f0094198b" + } + }, + "55f83d8af4f14bcf919a7be7172fb1a1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "layout": "IPY_MODEL_85e8f54ab64f4d60b312b854c80fc65e", + "style": "IPY_MODEL_eb1ac59dd60142febd0376c901df2314", + "value": "

Copy a token from your Hugging Face\ntokens page and paste it below.
Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.
" + } + }, + "62e1665f7a254243a615eaa510dc584b": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": {} + }, + "658454d94e3943fcb807696652229efc": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "ButtonStyleModel", + "state": { + "font_family": null, + "font_size": null, + "font_style": null, + "font_variant": null, + "font_weight": null, + "text_color": null, + "text_decoration": null + } + }, + "7d8e9a2182e44135b426f74599105380": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "PasswordModel", + "state": { + "description": "Token:", + "layout": "IPY_MODEL_417fd8dd33434e4f83992900ab07bca3", + "style": "IPY_MODEL_bffa098d17db45a0b7c7460ad489f915" + } + }, + "85e8f54ab64f4d60b312b854c80fc65e": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": {} + }, + "98f29deae0674348bc8be2555826295e": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": {} + }, + "bffa098d17db45a0b7c7460ad489f915": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "TextStyleModel", + "state": { + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "cf95eb33c7f749afab6ae07f0094198b": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "2.0.0", + "model_name": "LayoutModel", + "state": { + "align_items": "center", + "display": "flex", + "flex_flow": "column", + "width": "50%" + } + }, + "db1809374aaa4a07878cc6d9867f6954": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLModel", + "state": { + "layout": "IPY_MODEL_62e1665f7a254243a615eaa510dc584b", + "style": "IPY_MODEL_0806da8e21ef43fb954d04a758b1672d", + "value": "\nPro Tip: If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks.
" + } + }, + "eb1ac59dd60142febd0376c901df2314": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "HTMLStyleModel", + "state": { + "description_width": "", + "font_size": null, + "text_color": null + } + }, + "ecd4f83965d344e19d38b2bb8f3cad30": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "CheckboxModel", + "state": { + "description": "Add token as git credential?", + "disabled": false, + "layout": "IPY_MODEL_98f29deae0674348bc8be2555826295e", + "style": "IPY_MODEL_f94049a4d5ff4b5a9ea30bb5ca24427c", + "value": true + } + }, + "f94049a4d5ff4b5a9ea30bb5ca24427c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "2.0.0", + "model_name": "CheckboxStyleModel", + "state": { + "description_width": "" + } + } + }, + "version_major": 2, + "version_minor": 0 + } + } }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\n", - " * Running on http://127.0.0.1:5000\n", - "Press CTRL+C to quit\n", - "Provided model does not contain state. It may lead to sub-optimal performance.Please reexport model with updated OpenVINO version >= 2023.3.0 calling the `from_pretrained` method with original model and `export=True` parameter\n", - "Compiling the model to GPU ...\n", - "Setting OpenVINO CACHE_DIR to models\\ov_qwen7b\\model_cache\n", - "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n", - "127.0.0.1 - - [28/May/2025 03:19:51] \"POST /select-model HTTP/1.1\" 200 -\n" - ] - } - ], - "source": [ - "app.run(port=5000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1a5379a7-3e4e-4dbb-acbc-145f731318e1", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.5" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": { - "0806da8e21ef43fb954d04a758b1672d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "2537e0551036498b92794ff751292dc9": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": {} - }, - "417fd8dd33434e4f83992900ab07bca3": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": {} - }, - "46b975c57854465abb7fcf9811236333": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ButtonModel", - "state": { - "description": "Login", - "layout": "IPY_MODEL_2537e0551036498b92794ff751292dc9", - "style": "IPY_MODEL_658454d94e3943fcb807696652229efc", - "tooltip": null - } - }, - "55c1c17acff446019b03719e22175c8a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "VBoxModel", - "state": { - "children": [ - "IPY_MODEL_55f83d8af4f14bcf919a7be7172fb1a1", - "IPY_MODEL_7d8e9a2182e44135b426f74599105380", - "IPY_MODEL_ecd4f83965d344e19d38b2bb8f3cad30", - "IPY_MODEL_46b975c57854465abb7fcf9811236333", - "IPY_MODEL_db1809374aaa4a07878cc6d9867f6954" - ], - "layout": "IPY_MODEL_cf95eb33c7f749afab6ae07f0094198b" - } - }, - "55f83d8af4f14bcf919a7be7172fb1a1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "layout": "IPY_MODEL_85e8f54ab64f4d60b312b854c80fc65e", - "style": "IPY_MODEL_eb1ac59dd60142febd0376c901df2314", - "value": "

Copy a token from your Hugging Face\ntokens page and paste it below.
Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.
" - } - }, - "62e1665f7a254243a615eaa510dc584b": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": {} - }, - "658454d94e3943fcb807696652229efc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "ButtonStyleModel", - "state": { - "font_family": null, - "font_size": null, - "font_style": null, - "font_variant": null, - "font_weight": null, - "text_color": null, - "text_decoration": null - } - }, - "7d8e9a2182e44135b426f74599105380": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "PasswordModel", - "state": { - "description": "Token:", - "layout": "IPY_MODEL_417fd8dd33434e4f83992900ab07bca3", - "style": "IPY_MODEL_bffa098d17db45a0b7c7460ad489f915" - } - }, - "85e8f54ab64f4d60b312b854c80fc65e": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": {} - }, - "98f29deae0674348bc8be2555826295e": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": {} - }, - "bffa098d17db45a0b7c7460ad489f915": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "TextStyleModel", - "state": { - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "cf95eb33c7f749afab6ae07f0094198b": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "2.0.0", - "model_name": "LayoutModel", - "state": { - "align_items": "center", - "display": "flex", - "flex_flow": "column", - "width": "50%" - } - }, - "db1809374aaa4a07878cc6d9867f6954": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLModel", - "state": { - "layout": "IPY_MODEL_62e1665f7a254243a615eaa510dc584b", - "style": "IPY_MODEL_0806da8e21ef43fb954d04a758b1672d", - "value": "\nPro Tip: If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks.
" - } - }, - "eb1ac59dd60142febd0376c901df2314": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "HTMLStyleModel", - "state": { - "description_width": "", - "font_size": null, - "text_color": null - } - }, - "ecd4f83965d344e19d38b2bb8f3cad30": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "CheckboxModel", - "state": { - "description": "Add token as git credential?", - "disabled": false, - "layout": "IPY_MODEL_98f29deae0674348bc8be2555826295e", - "style": "IPY_MODEL_f94049a4d5ff4b5a9ea30bb5ca24427c", - "value": true - } - }, - "f94049a4d5ff4b5a9ea30bb5ca24427c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "2.0.0", - "model_name": "CheckboxStyleModel", - "state": { - "description_width": "" - } - } - }, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/Text-Summarizer-Browser-Plugin/backend/server.py b/Text-Summarizer-Browser-Plugin/backend/server.py index 5a72abb..6e48d0c 100644 --- a/Text-Summarizer-Browser-Plugin/backend/server.py +++ b/Text-Summarizer-Browser-Plugin/backend/server.py @@ -1,4 +1,5 @@ # Importing necessary Libraries +import warnings import time from flask import Flask, Response, request, jsonify from flask_cors import CORS @@ -7,7 +8,6 @@ import chromadb import os os.environ['USER_AGENT'] = 'myagent' -import warnings warnings.filterwarnings("ignore") # Initializing the flask app and enabling CORS @@ -133,5 +133,5 @@ def url_process_query(): - Is it enabled? Double-check your browser's extension settings to ensure the plugin isn't disabled. - Have you pinned the extension? Pin the extension. """ - ) + ) app.run(port=5000) diff --git a/Text-Summarizer-Browser-Plugin/extension/manifest.json b/Text-Summarizer-Browser-Plugin/extension/manifest.json index 049083a..b62ea10 100644 --- a/Text-Summarizer-Browser-Plugin/extension/manifest.json +++ b/Text-Summarizer-Browser-Plugin/extension/manifest.json @@ -6,6 +6,6 @@ "permissions": ["activeTab"], "host_permissions": ["http://localhost:5000/"], "action": { - "default_popup": "popup.html" + "default_popup": "popup.html" } - } +} diff --git a/Text-Summarizer-Browser-Plugin/extension/popup.html b/Text-Summarizer-Browser-Plugin/extension/popup.html index 59258b9..8333e80 100644 --- a/Text-Summarizer-Browser-Plugin/extension/popup.html +++ b/Text-Summarizer-Browser-Plugin/extension/popup.html @@ -1,85 +1,83 @@ - - - - - - Web & PDF Summarizer - - - -
-

Summarizer

- - -
- -
- -
- -
- - -