{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 1) Import Required Libraries \n", "\n", "import requests\n", "import gradio as gr\n", "\n", "# Deepseek only uses abstract summarization\n", "# This tool use DeepSeek API Endpoint\n", "\n", "# 2) Define the DeepSeek API Endpoint\n", "\n", "OLLAMA_URL = \"http://localhost:11434/api/generate\"\n", "\n", "# 3) Define the Summarization Function which can retrieve Information\n", "\n", "def summarize_text(text):\n", " payload = {\n", " \"model\": \"deepseek-r1\", #Here you can load whatever the model you have in your ollama(ex:deepseek-r1:1.5b,7b,8b,14b) I used 7b model here \n", " \"prompt\": f\"Summarize the following text in **5 bullet points**:\\n\\n{text}\", #The prompt is here for tell commands for the llm to act \n", " \"stream\": False # Ensures the response is returned as a whole, not streamed\n", " }\n", "\n", " response = requests.post(OLLAMA_URL, json=payload) #Send Requests to deepseekAPI\n", "\n", " if response.status_code == 200: #if server run correctly it return the result or it will give error\n", " return response.json().get(\"response\", \"No summary generated.\")\n", " else:\n", " return f\"Error: {response.text}\"\n", "\n", "# 4) Create Gradio interface to design \n", "interface = gr.Interface(\n", " fn=summarize_text,\n", " inputs=gr.Textbox(lines=10, placeholder=\"Enter text to summarize\"),\n", " outputs=gr.Textbox(label=\"Summarized Text\"),\n", " #theme='NoCrypt/miku', #Theme for the Interface I used Hatsune Miku from HF \n", " title=\"AI-Powered Text Summarizer\",\n", " description=\"Enter a long text and DeepSeek AI will generate a concise summary.\"\n", ")\n", "\n", "# Launch the web app\n", "if __name__ == \"__main__\":\n", " interface.launch()\n", "\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "base", "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": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 2 }