Upload Untitled0.ipynb

Untitled0.ipynb

@@ -0,0 +1,187 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_qYQS0tiDKEL"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q langchain openai duckduckgo-search gradio transformers\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install -q langchain-community langchainhub langchain-core"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zsmgG_1LD4hj",
+        "outputId": "8cc047a0-6963-491c-a80f-bb46732a9689"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m36.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m467.2/467.2 kB\u001b[0m \u001b[31m21.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m38.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.5/65.5 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.7/64.7 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.9/50.9 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "google-colab 1.0.0 requires requests==2.32.4, but you have requests 2.32.5 which is incompatible.\n",
+            "langchain 0.3.27 requires langchain-core<1.0.0,>=0.3.72, but you have langchain-core 1.0.0 which is incompatible.\n",
+            "langchain 0.3.27 requires langchain-text-splitters<1.0.0,>=0.3.9, but you have langchain-text-splitters 1.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0m"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install -q ddgs"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1xhRZvQ7Eh9p",
+        "outputId": "ebe48afb-2fcd-4698-9930-83bca327e58b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/41.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.6/41.6 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/5.3 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m5.3/5.3 MB\u001b[0m \u001b[31m194.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.3/5.3 MB\u001b[0m \u001b[31m112.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from transformers import pipeline\n",
+        "from langchain.llms import HuggingFacePipeline\n",
+        "from langchain.tools import DuckDuckGoSearchRun\n",
+        "import gradio as gr\n",
+        "\n",
+        "# 🔹 Create search tool\n",
+        "search_tool = DuckDuckGoSearchRun()\n",
+        "\n",
+        "# 🔹 Create local LLM\n",
+        "hf = pipeline(\"text2text-generation\", model=\"google/flan-t5-base\", max_new_tokens=256)\n",
+        "llm = HuggingFacePipeline(pipeline=hf)\n",
+        "\n",
+        "# 🔹 Combined function: first search, then summarize\n",
+        "def agent_chat(query):\n",
+        "    try:\n",
+        "        # Step 1: Get web search results\n",
+        "        search_results = search_tool.run(query)\n",
+        "\n",
+        "        # Step 2: Ask model to summarize results\n",
+        "        prompt = f\"Summarize the following information and answer clearly:\\n\\n{search_results}\"\n",
+        "        response = llm(prompt)\n",
+        "\n",
+        "        return response\n",
+        "    except Exception as e:\n",
+        "        return f\"❌ Error: {str(e)}\"\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "y1LePpZ4DOWy",
+        "outputId": "7a40d787-acff-4156-fffe-99f6bc4655eb"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Device set to use cuda:0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "# 🔹 Gradio Interface\n",
+        "demo = gr.Interface(\n",
+        "    fn=agent_chat,\n",
+        "    inputs=gr.Textbox(label=\"Ask the AI anything:\"),\n",
+        "    outputs=\"text\",\n",
+        "    title=\"AI Assistant\",\n",
+        "    description=\"This simple agent searches the web and summarizes results using FLAN-T5.\"\n",
+        ")\n",
+        "\n",
+        "demo.launch(debug=True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 646
+        },
+        "id": "QwDaTh7ODe1B",
+        "outputId": "b6597c18-f6ad-446f-9e06-85f4ce8ecd18"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "It looks like you are running Gradio on a hosted Jupyter notebook, which requires `share=True`. Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
+            "\n",
+            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
+            "* Running on public URL: https://a884027dcc1d0b4670.gradio.live\n",
+            "\n",
+            "This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "<div><iframe src=\"https://a884027dcc1d0b4670.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}