Created using Colaboratory

Copy_of_local_ollama_agent_.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyPaNjrQ4/vWL00DhJIGcmSl",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/almutareb/InnovationPathfinderAI/blob/agent_notebook/Copy_of_local_ollama_agent_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install -U langchain_community -q\n",
+        "!pip install -U langchain -q\n",
+        "!pip install google-search-results -q\n",
+        "!pip install langchainhub -q\n",
+        "!pip install text_generation -q\n",
+        "!pip install arxiv -q"
+      ],
+      "metadata": {
+        "id": "W125o44IGdna",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c746bf1e-0b97-4eab-c94d-a7f417980d8b"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m241.2/241.2 kB\u001b[0m \u001b[31m11.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.5/56.5 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.4/55.4 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m815.9/815.9 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Building wheel for google-search-results (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.1/81.1 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Building wheel for sgmllib3k (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "from google.colab import userdata\n",
+        "os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = userdata.get('HUGGINGFACEHUB_API_TOKEN')\n",
+        "os.environ[\"SERPAPI_API_KEY\"] = userdata.get('SERPAPI_API_KEY')"
+      ],
+      "metadata": {
+        "id": "yXd9TTjoK439"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sOrq-hGZGZ_r",
+        "outputId": "15f1040e-2e50-40dc-f1ef-bdff72097228"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " Why don't astronauts forget things in space?\n",
+            "\n",
+            "Because in space, they weightless information! 💡🚀\n",
+            "\n",
+            "(This one is an oldie but goodie!)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# LangChain supports many other chat models. Here, we're using Ollama\n",
+        "from langchain_community.chat_models import ChatOllama\n",
+        "from langchain_core.output_parsers import StrOutputParser\n",
+        "from langchain_core.prompts import ChatPromptTemplate\n",
+        "\n",
+        "# supports many more optional parameters. Hover on your `ChatOllama(...)`\n",
+        "# class to view the latest available supported parameters\n",
+        "llm = ChatOllama(\n",
+        "    model=\"mistral\",\n",
+        "    base_url=\"https://0013-35-201-206-176.ngrok-free.app\"\n",
+        "    )\n",
+        "prompt = ChatPromptTemplate.from_template(\"Tell me a short joke about {topic}\")\n",
+        "\n",
+        "# using LangChain Expressive Language chain syntax\n",
+        "# learn more about the LCEL on\n",
+        "# https://python.langchain.com/docs/expression_language/why\n",
+        "chain = prompt | llm | StrOutputParser()\n",
+        "\n",
+        "# for brevity, response is printed in terminal\n",
+        "# You can use LangServe to deploy your application for\n",
+        "# production\n",
+        "print(chain.invoke({\"topic\": \"Space travel\"}))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from langchain.tools.retriever import create_retriever_tool\n",
+        "from langchain_community.utilities import SerpAPIWrapper\n",
+        "from langchain.retrievers import ArxivRetriever\n",
+        "from langchain_core.tools import Tool\n",
+        "\n",
+        "retriever = ArxivRetriever(load_max_docs=2)\n",
+        "\n",
+        "tools = [create_retriever_tool(\n",
+        "    retriever,\n",
+        "    \"search arxiv's database for\",\n",
+        "    \"Use this to recomend the user a paper to read Unless stated please choose the most recent models\",\n",
+        "    # \"Searches and returns excerpts from the 2022 State of the Union.\",\n",
+        "),\n",
+        "\n",
+        "Tool(\n",
+        "    name=\"SerpAPI\",\n",
+        "    description=\"A low-cost Google Search API. Useful for when you need to answer questions about current events. Input should be a search query.\",\n",
+        "    func=SerpAPIWrapper().run,\n",
+        ")\n",
+        "\n",
+        "]\n",
+        "\n",
+        "# tools = [tool]"
+      ],
+      "metadata": {
+        "id": "tn5XMqCVeaQK"
+      },
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from langchain import hub\n",
+        "from langchain.agents import AgentExecutor, load_tools\n",
+        "from langchain.agents.format_scratchpad import format_log_to_str\n",
+        "from langchain.agents.output_parsers import (\n",
+        "    ReActJsonSingleInputOutputParser,\n",
+        ")\n",
+        "from langchain.tools.render import render_text_description\n",
+        "# from langchain_community.utilities import SerpAPIWrapper\n",
+        "# from langchain.retrievers import ArxivRetriever\n",
+        "\n",
+        "# setup tools\n",
+        "# tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\n",
+        "\n",
+        "# tools = load_tools([\"serpapi\", \"llm-math\",create_retriever_tool], llm=llm)\n",
+        "\n",
+        "# setup ReAct style prompt\n",
+        "prompt = hub.pull(\"hwchase17/react-json\")\n",
+        "prompt = prompt.partial(\n",
+        "    tools=render_text_description(tools),\n",
+        "    tool_names=\", \".join([t.name for t in tools]),\n",
+        ")\n",
+        "\n",
+        "chat_model = llm\n",
+        "# define the agent\n",
+        "chat_model_with_stop = chat_model.bind(stop=[\"\\nObservation\"])\n",
+        "agent = (\n",
+        "    {\n",
+        "        \"input\": lambda x: x[\"input\"],\n",
+        "        \"agent_scratchpad\": lambda x: format_log_to_str(x[\"intermediate_steps\"]),\n",
+        "    }\n",
+        "    | prompt\n",
+        "    | chat_model_with_stop\n",
+        "    | ReActJsonSingleInputOutputParser()\n",
+        ")\n",
+        "\n",
+        "# instantiate AgentExecutor\n",
+        "agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True,handle_parsing_errors=True)\n",
+        "\n",
+        "# agent_executor.invoke(\n",
+        "#     {\n",
+        "#         \"input\": \"Who is the current holder of the speed skating world record on 500 meters? What is her current age raised to the 0.43 power?\"\n",
+        "#     }\n",
+        "# )\n",
+        "\n",
+        "# agent_executor.invoke(\n",
+        "#     {\n",
+        "#         \"input\": \"what are large language models and why are they so expensive to run?\"\n",
+        "#     }\n",
+        "# )\n",
+        "\n",
+        "agent_executor.invoke(\n",
+        "    {\n",
+        "        \"input\": \"How to generate videos from images using state of the art macchine learning models\"\n",
+        "    }\n",
+        ")\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VzcIqgcDKZuZ",
+        "outputId": "99fe8de1-8539-4fe9-ae24-576aad0daee6"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "\n",
+            "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+            "\u001b[32;1m\u001b[1;3m Thought: To find the most recent papers on generating videos from images using machine learning models, I will use the \"search arxiv's database for\" tool.\n",
+            "\n",
+            "Action:\n",
+            "```json\n",
+            "{\n",
+            "  \"action\": \"search arxiv's database for\",\n",
+            "  \"action_input\": \"generating videos from images machine learning\"\n",
+            "}\n",
+            "```\u001b[0m\u001b[36;1m\u001b[1;3mDiffusion models exhibited tremendous progress in image and video generation,\n",
+            "exceeding GANs in quality and diversity. However, they are usually trained on\n",
+            "very large datasets and are not naturally adapted to manipulate a given input\n",
+            "image or video. In this paper we show how this can be resolved by training a\n",
+            "diffusion model on a single input image or video. Our image/video-specific\n",
+            "diffusion model (SinFusion) learns the appearance and dynamics of the single\n",
+            "image or video, while utilizing the conditioning capabilities of diffusion\n",
+            "models. It can solve a wide array of image/video-specific manipulation tasks.\n",
+            "In particular, our model can learn from few frames the motion and dynamics of a\n",
+            "single input video. It can then generate diverse new video samples of the same\n",
+            "dynamic scene, extrapolate short videos into long ones (both forward and\n",
+            "backward in time) and perform video upsampling. Most of these tasks are not\n",
+            "realizable by current video-specific generation methods.\n",
+            "\n",
+            "In this work we deal with the problem of high-level event detection in video.\n",
+            "Specifically, we study the challenging problems of i) learning to detect video\n",
+            "events from solely a textual description of the event, without using any\n",
+            "positive video examples, and ii) additionally exploiting very few positive\n",
+            "training samples together with a small number of ``related'' videos. For\n",
+            "learning only from an event's textual description, we first identify a general\n",
+            "learning framework and then study the impact of different design choices for\n",
+            "various stages of this framework. For additionally learning from example\n",
+            "videos, when true positive training samples are scarce, we employ an extension\n",
+            "of the Support Vector Machine that allows us to exploit ``related'' event\n",
+            "videos by automatically introducing different weights for subsets of the videos\n",
+            "in the overall training set. Experimental evaluations performed on the\n",
+            "large-scale TRECVID MED 2014 video dataset provide insight on the effectiveness\n",
+            "of the proposed methods.\n",
+            "\n",
+            "We study the problem of video-to-video synthesis, whose goal is to learn a\n",
+            "mapping function from an input source video (e.g., a sequence of semantic\n",
+            "segmentation masks) to an output photorealistic video that precisely depicts\n",
+            "the content of the source video. While its image counterpart, the\n",
+            "image-to-image synthesis problem, is a popular topic, the video-to-video\n",
+            "synthesis problem is less explored in the literature. Without understanding\n",
+            "temporal dynamics, directly applying existing image synthesis approaches to an\n",
+            "input video often results in temporally incoherent videos of low visual\n",
+            "quality. In this paper, we propose a novel video-to-video synthesis approach\n",
+            "under the generative adversarial learning framework. Through carefully-designed\n",
+            "generator and discriminator architectures, coupled with a spatio-temporal\n",
+            "adversarial objective, we achieve high-resolution, photorealistic, temporally\n",
+            "coherent video results on a diverse set of input formats including segmentation\n",
+            "masks, sketches, and poses. Experiments on multiple benchmarks show the\n",
+            "advantage of our method compared to strong baselines. In particular, our model\n",
+            "is capable of synthesizing 2K resolution videos of street scenes up to 30\n",
+            "seconds long, which significantly advances the state-of-the-art of video\n",
+            "synthesis. Finally, we apply our approach to future video prediction,\n",
+            "outperforming several state-of-the-art competing systems.\u001b[0m\u001b[32;1m\u001b[1;3m This paper discusses several recent studies on generating videos from images using machine learning models. One study introduces SinFusion, a diffusion model that learns the appearance and dynamics of a single image or video for various manipulation tasks. Another study deals with high-level event detection in videos, learning from textual descriptions and exploiting related videos. The third study proposes a novel video-to-video synthesis approach under the generative adversarial learning framework to achieve photorealistic, temporally coherent videos.\n",
+            "\n",
+            "Final Answer: There are recent studies that discuss generating videos from images using machine learning models. One study introduces SinFusion, a diffusion model for image/video-specific manipulation tasks. Another study deals with high-level event detection in videos using textual descriptions and related videos. The third study proposes a novel video-to-video synthesis approach under the generative adversarial learning framework to achieve photorealistic, temporally coherent videos.\u001b[0m\n",
+            "\n",
+            "\u001b[1m> Finished chain.\u001b[0m\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'input': 'How to generate videos from images using state of the art macchine learning models',\n",
+              " 'output': 'There are recent studies that discuss generating videos from images using machine learning models. One study introduces SinFusion, a diffusion model for image/video-specific manipulation tasks. Another study deals with high-level event detection in videos using textual descriptions and related videos. The third study proposes a novel video-to-video synthesis approach under the generative adversarial learning framework to achieve photorealistic, temporally coherent videos.'}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    }
+  ]
+}