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          ]
        }
      ],
      "source": [
        "pip install uagents openai textblob\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "Vx0AJA1NJ5Ia",
        "outputId": "6db43537-2d94-438a-c93b-8307dfbfdafb"
      },
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      "outputs": [
        {
          "output_type": "stream",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Testing Agents"
      ],
      "metadata": {
        "id": "AgHFdtD0NnZG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from uagents import Agent, Context\n"
      ],
      "metadata": {
        "id": "0AFAhPyX_EUb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the Agent class\n",
        "class ResourceCollector(Agent):\n",
        "    def __init__(self, name):\n",
        "        super().__init__(name)\n",
        "        self.agent_name = name  # Using a different attribute name to avoid conflict\n",
        "        self.resources = 0\n",
        "\n",
        "    def act(self):\n",
        "        self.resources += 10\n",
        "        print(f\"{self.agent_name} collected resources. Total: {self.resources}\")\n",
        "\n",
        "class Trader(Agent):\n",
        "    def __init__(self, name):\n",
        "        super().__init__(name)\n",
        "        self.agent_name = name  # Using a different attribute name to avoid conflict\n",
        "        self.resources = 0\n",
        "\n",
        "    def act(self):\n",
        "        print(f\"{self.agent_name} is ready to trade. Available resources: {self.resources}\")\n",
        "\n",
        "class Defender(Agent):\n",
        "    def __init__(self, name):\n",
        "        super().__init__(name)\n",
        "        self.agent_name = name  # Using a different attribute name to avoid conflict\n",
        "\n",
        "    def act(self):\n",
        "        print(f\"{self.agent_name} is defending resources.\")\n",
        "\n",
        "# Initialize agents\n",
        "collector = ResourceCollector(\"Collector1\")\n",
        "trader = Trader(\"Trader1\")\n",
        "defender = Defender(\"Defender1\")\n",
        "\n",
        "# Define communication function\n",
        "def communicate(agent_from, agent_to, message):\n",
        "    print(f\"{agent_from.agent_name} to {agent_to.agent_name}: {message}\")\n",
        "\n",
        "# Run the simulation\n",
        "for step in range(5):  # Number of simulation steps\n",
        "    print(f\"--- Simulation Step {step + 1} ---\")\n",
        "    collector.act()\n",
        "    trader.act()\n",
        "    defender.act()\n",
        "\n",
        "    # Example interactions\n",
        "    communicate(collector, trader, \"Collected more resources.\")\n",
        "    communicate(trader, collector, \"Need more resources for trade.\")\n",
        "    communicate(defender, trader, \"Trade secured.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LbykthykEF_L",
        "outputId": "0339860c-d220-451b-830d-8dad4b9060bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Simulation Step 1 ---\n",
            "Collector1 collected resources. Total: 10\n",
            "Trader1 is ready to trade. Available resources: 0\n",
            "Defender1 is defending resources.\n",
            "Collector1 to Trader1: Collected more resources.\n",
            "Trader1 to Collector1: Need more resources for trade.\n",
            "Defender1 to Trader1: Trade secured.\n",
            "--- Simulation Step 2 ---\n",
            "Collector1 collected resources. Total: 20\n",
            "Trader1 is ready to trade. Available resources: 0\n",
            "Defender1 is defending resources.\n",
            "Collector1 to Trader1: Collected more resources.\n",
            "Trader1 to Collector1: Need more resources for trade.\n",
            "Defender1 to Trader1: Trade secured.\n",
            "--- Simulation Step 3 ---\n",
            "Collector1 collected resources. Total: 30\n",
            "Trader1 is ready to trade. Available resources: 0\n",
            "Defender1 is defending resources.\n",
            "Collector1 to Trader1: Collected more resources.\n",
            "Trader1 to Collector1: Need more resources for trade.\n",
            "Defender1 to Trader1: Trade secured.\n",
            "--- Simulation Step 4 ---\n",
            "Collector1 collected resources. Total: 40\n",
            "Trader1 is ready to trade. Available resources: 0\n",
            "Defender1 is defending resources.\n",
            "Collector1 to Trader1: Collected more resources.\n",
            "Trader1 to Collector1: Need more resources for trade.\n",
            "Defender1 to Trader1: Trade secured.\n",
            "--- Simulation Step 5 ---\n",
            "Collector1 collected resources. Total: 50\n",
            "Trader1 is ready to trade. Available resources: 0\n",
            "Defender1 is defending resources.\n",
            "Collector1 to Trader1: Collected more resources.\n",
            "Trader1 to Collector1: Need more resources for trade.\n",
            "Defender1 to Trader1: Trade secured.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "Et0aNWRPE6v5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from uagents import Agent, Context\n",
        "\n",
        "class ResourceCollector(Agent):\n",
        "    def __init__(self, name):\n",
        "        super().__init__(name)\n",
        "        self.agent_name = name\n",
        "        self.resources = 0\n",
        "\n",
        "    def act(self):\n",
        "        self.resources += 10\n",
        "        print(f\"{self.agent_name} collected resources. Total: {self.resources}\")\n",
        "\n",
        "collector = ResourceCollector(\"Collector1\")\n"
      ],
      "metadata": {
        "id": "FRLD1PxOE7NO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Trader(Agent):\n",
        "    def __init__(self, name):\n",
        "        super().__init__(name)\n",
        "        self.agent_name = name\n",
        "        self.resources = 0\n",
        "\n",
        "    def act(self):\n",
        "        print(f\"{self.agent_name} is ready to trade. Available resources: {self.resources}\")\n",
        "\n",
        "trader = Trader(\"Trader1\")\n"
      ],
      "metadata": {
        "id": "BKOqA4d6E8aw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Defender(Agent):\n",
        "    def __init__(self, name):\n",
        "        super().__init__(name)\n",
        "        self.agent_name = name\n",
        "\n",
        "    def act(self):\n",
        "        print(f\"{self.agent_name} is defending resources.\")\n",
        "\n",
        "defender = Defender(\"Defender1\")\n"
      ],
      "metadata": {
        "id": "hBjoM2ggE-w3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def communicate(agent_from, agent_to, message):\n",
        "    print(f\"{agent_from.agent_name} to {agent_to.agent_name}: {message}\")\n",
        "\n",
        "# Example communication\n",
        "communicate(collector, trader, \"I have resources to trade.\")\n",
        "communicate(trader, defender, \"Can you defend my trade?\")\n",
        "communicate(defender, collector, \"I will protect your resources.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K4hqpEJeFCpa",
        "outputId": "c98f3821-ea6a-4b0b-be8d-a475065c5796"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collector1 to Trader1: I have resources to trade.\n",
            "Trader1 to Defender1: Can you defend my trade?\n",
            "Defender1 to Collector1: I will protect your resources.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Run the simulation\n",
        "for step in range(5):  # Number of simulation steps\n",
        "    print(f\"--- Simulation Step {step + 1} ---\")\n",
        "    collector.act()\n",
        "    trader.act()\n",
        "    defender.act()\n",
        "\n",
        "    # Example interactions\n",
        "    communicate(collector, trader, \"Collected more resources.\")\n",
        "    communicate(trader, collector, \"Need more resources for trade.\")\n",
        "    communicate(defender, trader, \"Trade secured.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3PrrJxCgFD5t",
        "outputId": "f00584a4-743b-44c8-abda-eeca844fd04b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--- Simulation Step 1 ---\n",
            "Collector1 collected resources. Total: 10\n",
            "Trader1 is ready to trade. Available resources: 0\n",
            "Defender1 is defending resources.\n",
            "Collector1 to Trader1: Collected more resources.\n",
            "Trader1 to Collector1: Need more resources for trade.\n",
            "Defender1 to Trader1: Trade secured.\n",
            "--- Simulation Step 2 ---\n",
            "Collector1 collected resources. Total: 20\n",
            "Trader1 is ready to trade. Available resources: 0\n",
            "Defender1 is defending resources.\n",
            "Collector1 to Trader1: Collected more resources.\n",
            "Trader1 to Collector1: Need more resources for trade.\n",
            "Defender1 to Trader1: Trade secured.\n",
            "--- Simulation Step 3 ---\n",
            "Collector1 collected resources. Total: 30\n",
            "Trader1 is ready to trade. Available resources: 0\n",
            "Defender1 is defending resources.\n",
            "Collector1 to Trader1: Collected more resources.\n",
            "Trader1 to Collector1: Need more resources for trade.\n",
            "Defender1 to Trader1: Trade secured.\n",
            "--- Simulation Step 4 ---\n",
            "Collector1 collected resources. Total: 40\n",
            "Trader1 is ready to trade. Available resources: 0\n",
            "Defender1 is defending resources.\n",
            "Collector1 to Trader1: Collected more resources.\n",
            "Trader1 to Collector1: Need more resources for trade.\n",
            "Defender1 to Trader1: Trade secured.\n",
            "--- Simulation Step 5 ---\n",
            "Collector1 collected resources. Total: 50\n",
            "Trader1 is ready to trade. Available resources: 0\n",
            "Defender1 is defending resources.\n",
            "Collector1 to Trader1: Collected more resources.\n",
            "Trader1 to Collector1: Need more resources for trade.\n",
            "Defender1 to Trader1: Trade secured.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class DialogueAgent(Agent):\n",
        "    def __init__(self, name):\n",
        "        super().__init__(name)\n",
        "        self.agent_name = name\n",
        "\n",
        "    def start_dialogue(self, context: Context):\n",
        "        print(f\"{self.agent_name} is starting a dialogue.\")\n",
        "        # Dialogue logic here\n",
        "\n",
        "    def respond_dialogue(self, context: Context):\n",
        "        print(f\"{self.agent_name} is responding to a dialogue.\")\n",
        "        # Response logic here\n",
        "\n",
        "# Example dialogue\n",
        "collector = DialogueAgent(\"Collector1\")\n",
        "trader = DialogueAgent(\"Trader1\")\n",
        "\n",
        "collector.start_dialogue(None)\n",
        "trader.respond_dialogue(None)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jl8s-FfYFMAf",
        "outputId": "d3158613-3608-4f2c-d996-995573ff6bbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collector1 is starting a dialogue.\n",
            "Trader1 is responding to a dialogue.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "ltDv_B3lJKCe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Shopping Agent"
      ],
      "metadata": {
        "id": "5reg9lOUJJ_P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install uagents transformers textblob ipywidgets sentence-transformers scikit-learn pytrends requests beautifulsoup4\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LfPTOkE0N0Ve",
        "outputId": "46e0f324-b684-4687-d9f0-925bdf672421",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pytrends\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ERhPgIO8OJEs",
        "outputId": "d434be44-7ca4-4fa2-c673-5f470222da08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pytrends\n",
            "  Downloading pytrends-4.9.2-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: requests>=2.0 in /usr/local/lib/python3.10/dist-packages (from pytrends) (2.31.0)\n",
            "Requirement already satisfied: pandas>=0.25 in /usr/local/lib/python3.10/dist-packages (from pytrends) (2.0.3)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from pytrends) (4.9.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.25->pytrends) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.25->pytrends) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.25->pytrends) (2024.1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.25->pytrends) (1.25.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.0->pytrends) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.0->pytrends) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.0->pytrends) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.0->pytrends) (2024.6.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas>=0.25->pytrends) (1.16.0)\n",
            "Installing collected packages: pytrends\n",
            "Successfully installed pytrends-4.9.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "# Define intents and example queries\n",
        "intents = [\"book_ticket\", \"get_offers\", \"search_product\", \"get_price_trend\", \"general_query\"]\n",
        "examples = [\n",
        "    \"book a ticket for a movie\", \"what offers are available on smartphones\",\n",
        "    \"search for the cheapest laptop\", \"what's the price trend for bitcoin\",\n",
        "    \"tell me a joke\"\n",
        "]\n",
        "\n",
        "# Use Sentence Transformers for encoding the examples\n",
        "from sentence_transformers import SentenceTransformer\n",
        "model = SentenceTransformer('paraphrase-MiniLM-L6-v2')\n",
        "example_embeddings = model.encode(examples)\n",
        "\n",
        "# Function to classify the intent\n",
        "def classify_intent(query):\n",
        "    query_embedding = model.encode([query])\n",
        "    similarities = cosine_similarity(query_embedding, example_embeddings)\n",
        "    intent_index = np.argmax(similarities)\n",
        "    return intents[intent_index]\n"
      ],
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            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "FzHuTvFHQw0l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "AQbNYMRAKFA7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class BookingAgent(Agent):\n",
        "    def __init__(self, name):\n",
        "        super().__init__(name)\n",
        "        self.agent_name = name\n",
        "\n",
        "    def book_ticket(self, event):\n",
        "        print(f\"{self.agent_name} is booking ticket for: {event}\")\n",
        "        return f\"Booked ticket for {event}\"\n",
        "\n",
        "    def act(self, query):\n",
        "        event = query.split(\"book\")[1].strip() if \"book\" in query else \"unknown event\"\n",
        "        result = self.book_ticket(event)\n",
        "        print(f\"{self.agent_name} booked: {result}\")\n",
        "        return result\n",
        "\n",
        "booking_agent = BookingAgent(\"BookingAgent\")\n"
      ],
      "metadata": {
        "id": "wKvokFS-KFba"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "LNEhPp9wKP4n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class OfferAgent(Agent):\n",
        "    def __init__(self, name):\n",
        "        super().__init__(name)\n",
        "        self.agent_name = name\n",
        "\n",
        "    def get_offers(self, product):\n",
        "        print(f\"{self.agent_name} is fetching offers for: {product}\")\n",
        "        return f\"20% off on {product}\"\n",
        "\n",
        "    def act(self, query):\n",
        "        product = query.split(\"offers on\")[1].strip() if \"offers on\" in query else \"unknown product\"\n",
        "        result = self.get_offers(product)\n",
        "        print(f\"{self.agent_name} offers: {result}\")\n",
        "        return result\n",
        "\n",
        "offer_agent = OfferAgent(\"OfferAgent\")\n"
      ],
      "metadata": {
        "id": "GZerYqOvKQRD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "VzoSbADlLbIT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pytrends.request import TrendReq\n",
        "\n",
        "class PriceTrendAgent(Agent):\n",
        "    def __init__(self, name):\n",
        "        super().__init__(name)\n",
        "        self.agent_name = name\n",
        "        self.pytrends = TrendReq()\n",
        "\n",
        "    def get_price_trend(self, product):\n",
        "        self.pytrends.build_payload([product], cat=0, timeframe='today 12-m')\n",
        "        interest_over_time_df = self.pytrends.interest_over_time()\n",
        "        trend = \"increasing\" if interest_over_time_df[product].iloc[-1] > interest_over_time_df[product].mean() else \"decreasing\"\n",
        "        print(f\"{self.agent_name} found the price trend for {product}: {trend}\")\n",
        "        return f\"The price trend for {product} is {trend}\"\n",
        "\n",
        "    def act(self, query):\n",
        "        product = query.split(\"price trend for\")[1].strip() if \"price trend for\" in query else \"unknown product\"\n",
        "        result = self.get_price_trend(product)\n",
        "        print(f\"{self.agent_name} trend: {result}\")\n",
        "        return result\n",
        "\n",
        "price_trend_agent = PriceTrendAgent(\"PriceTrendAgent\")\n"
      ],
      "metadata": {
        "id": "LGq5nTGHLarN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "69aHzDEXLk2N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def ai_engine(query):\n",
        "    intent = classify_intent(query)\n",
        "    if intent == \"book_ticket\":\n",
        "        print(\"Booking Agent is handling the request...\")\n",
        "        response = booking_agent.act(query)\n",
        "    elif intent == \"get_offers\":\n",
        "        print(\"Offer Agent is handling the request...\")\n",
        "        response = offer_agent.act(query)\n",
        "    elif intent == \"search_product\":\n",
        "        print(\"Shopping Agent is handling the request...\")\n",
        "        response = shopping_agent.act(query)\n",
        "    elif intent == \"get_price_trend\":\n",
        "        print(\"Price Trend Agent is handling the request...\")\n",
        "        response = price_trend_agent.act(query)\n",
        "    else:\n",
        "        print(\"LLM Agent is handling the request...\")\n",
        "        response, sentiment = llm_agent.act(query)\n",
        "    return response\n",
        "\n"
      ],
      "metadata": {
        "id": "fff4c7QYLkSv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Shopping AI Environment"
      ],
      "metadata": {
        "id": "UNIZWbowRcSA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "from transformers import pipeline\n",
        "from textblob import TextBlob\n",
        "from uagents import Agent, Context\n",
        "import ipywidgets as widgets\n",
        "from IPython.display import display\n",
        "import random\n",
        "from pytrends.request import TrendReq\n",
        "from sentence_transformers import SentenceTransformer\n",
        "import numpy as np\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "import requests\n",
        "from bs4 import BeautifulSoup"
      ],
      "metadata": {
        "id": "x-fDlR7WRicn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the LLM Agent (using Hugging Face's GPT-2)\n",
        "class LLMAgent(Agent):\n",
        "    def __init__(self, name):\n",
        "        super().__init__(name)\n",
        "        self.agent_name = name\n",
        "        self.generator = pipeline('text-generation', model='gpt2')\n",
        "\n",
        "    def query_llm(self, prompt):\n",
        "        \"\"\"Generate a response using GPT-2 model.\"\"\"\n",
        "        response = self.generator(prompt, max_length=150, num_return_sequences=1)\n",
        "        return response[0]['generated_text']\n",
        "\n",
        "    def analyze_sentiment(self, text):\n",
        "        \"\"\"Analyze the sentiment of the provided text.\"\"\"\n",
        "        analysis = TextBlob(text)\n",
        "        return analysis.sentiment.polarity, analysis.sentiment.subjectivity\n",
        "\n",
        "    def act(self, query):\n",
        "        \"\"\"Process the query and return the response and sentiment analysis.\"\"\"\n",
        "        response = self.query_llm(query)\n",
        "        sentiment = self.analyze_sentiment(query)\n",
        "        print(f\"{self.agent_name} received response: {response}\")\n",
        "        print(f\"Sentiment Analysis - Polarity: {sentiment[0]}, Subjectivity: {sentiment[1]}\")\n",
        "        return response, sentiment\n",
        "\n",
        "llm_agent = LLMAgent(\"LLMAgent\")"
      ],
      "metadata": {
        "id": "J5hoG505RjKg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "from bs4 import BeautifulSoup\n",
        "import random\n",
        "import time\n",
        "\n",
        "class ShoppingAgent(Agent):\n",
        "    def __init__(self, name):\n",
        "        super().__init__(name)\n",
        "        self.agent_name = name\n",
        "        self.proxies = [\"http://proxy1\", \"http://proxy2\", \"http://proxy3\"]  # Replace with actual proxy URLs\n",
        "\n",
        "    def get_random_headers(self):\n",
        "        user_agents = [\n",
        "            \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36\",\n",
        "            \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36\",\n",
        "            \"Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:89.0) Gecko/20100101 Firefox/89.0\",\n",
        "        ]\n",
        "        return {\n",
        "            \"User-Agent\": random.choice(user_agents),\n",
        "            \"Accept-Language\": \"en-US,en;q=0.9\",\n",
        "        }\n",
        "\n",
        "    def search_product(self, product):\n",
        "        print(f\"{self.agent_name} is searching for product: {product} on Amazon\")\n",
        "        url = f\"https://www.amazon.com/s?k={product.replace(' ', '+')}\"\n",
        "        headers = self.get_random_headers()\n",
        "        proxy = {\"http\": random.choice(self.proxies)}\n",
        "        response = requests.get(url, headers=headers, proxies=proxy)\n",
        "        time.sleep(random.uniform(1, 3))  # Sleep to avoid detection as a bot\n",
        "        soup = BeautifulSoup(response.content, 'html.parser')\n",
        "        item = soup.find('span', {'class': 'a-size-medium a-color-base a-text-normal'})\n",
        "        if item:\n",
        "            item_name = item.text.strip()\n",
        "            item_link = item.find_parent('a')['href']\n",
        "            return f\"Found {item_name}. [Purchase here](https://www.amazon.in{item_link})\"\n",
        "        else:\n",
        "            return f\"No results found for {product} on Amazon.\"\n",
        "\n",
        "    def act(self, query):\n",
        "        product = query.split(\"for\")[1].strip() if \"for\" in query else \"unknown product\"\n",
        "        result = self.search_product(product)\n",
        "        print(f\"{self.agent_name} found: {result}\")\n",
        "        return result\n",
        "\n",
        "shopping_agent = ShoppingAgent(\"ShoppingAgent\")\n"
      ],
      "metadata": {
        "id": "tEX108UZRrNq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the Booking Agent\n",
        "class BookingAgent(Agent):\n",
        "    def __init__(self, name):\n",
        "        super().__init__(name)\n",
        "        self.agent_name = name\n",
        "\n",
        "    def book_ticket(self, event):\n",
        "        \"\"\"Book a ticket for the specified event.\"\"\"\n",
        "        print(f\"{self.agent_name} is booking ticket for: {event}\")\n",
        "        return f\"Booked ticket for {event}\"\n",
        "\n",
        "    def act(self, query):\n",
        "        \"\"\"Process the query and return the booking result.\"\"\"\n",
        "        event = query.split(\"book\")[1].strip() if \"book\" in query else \"unknown event\"\n",
        "        result = self.book_ticket(event)\n",
        "        print(f\"{self.agent_name} booked: {result}\")\n",
        "        return result\n",
        "\n",
        "booking_agent = BookingAgent(\"BookingAgent\")"
      ],
      "metadata": {
        "id": "kNxMudqIRuPx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the Offer Agent\n",
        "class OfferAgent(Agent):\n",
        "    def __init__(self, name):\n",
        "        super().__init__(name)\n",
        "        self.agent_name = name\n",
        "\n",
        "    def get_offers(self, product):\n",
        "        \"\"\"Fetch offers for the specified product.\"\"\"\n",
        "        print(f\"{self.agent_name} is fetching offers for: {product}\")\n",
        "        return f\"20% off on {product}\"\n",
        "\n",
        "    def act(self, query):\n",
        "        \"\"\"Process the query and return the offers.\"\"\"\n",
        "        product = query.split(\"offers on\")[1].strip() if \"offers on\" in query else \"unknown product\"\n",
        "        result = self.get_offers(product)\n",
        "        print(f\"{self.agent_name} offers: {result}\")\n",
        "        return result\n",
        "\n",
        "offer_agent = OfferAgent(\"OfferAgent\")"
      ],
      "metadata": {
        "id": "8gp1k7s3RxON"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the Price Trend Agent with Google Trends integration\n",
        "class PriceTrendAgent(Agent):\n",
        "    def __init__(self, name):\n",
        "        super().__init__(name)\n",
        "        self.agent_name = name\n",
        "        self.pytrends = TrendReq()\n",
        "\n",
        "    def get_price_trend(self, product):\n",
        "        \"\"\"Get the price trend for the specified product using Google Trends.\"\"\"\n",
        "        self.pytrends.build_payload([product], cat=0, timeframe='today 12-m')\n",
        "        interest_over_time_df = self.pytrends.interest_over_time()\n",
        "        trend = \"increasing\" if interest_over_time_df[product].iloc[-1] > interest_over_time_df[product].mean() else \"decreasing\"\n",
        "        print(f\"{self.agent_name} found the price trend for {product}: {trend}\")\n",
        "        return f\"The price trend for {product} is {trend}\"\n",
        "\n",
        "    def act(self, query):\n",
        "        \"\"\"Process the query and return the price trend.\"\"\"\n",
        "        product = query.split(\"price trend for\")[1].strip() if \"price trend for\" in query else \"unknown product\"\n",
        "        result = self.get_price_trend(product)\n",
        "        print(f\"{self.agent_name} trend: {result}\")\n",
        "        return result\n",
        "\n",
        "price_trend_agent = PriceTrendAgent(\"PriceTrendAgent\")"
      ],
      "metadata": {
        "id": "f73soEuBR0Oj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define intents and example queries\n",
        "intents = [\"book_ticket\", \"get_offers\", \"search_product\", \"get_price_trend\", \"general_query\"]\n",
        "examples = [\n",
        "    \"book a ticket for a movie\", \"what offers are available on smartphones\",\n",
        "    \"search for the cheapest laptop\", \"what's the price trend for bitcoin\",\n",
        "    \"tell me a joke\"\n",
        "]\n",
        "\n",
        "# Use Sentence Transformers for encoding the examples\n",
        "model = SentenceTransformer('paraphrase-MiniLM-L6-v2')\n",
        "example_embeddings = model.encode(examples)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G7xFTAkxR5D9",
        "outputId": "2021a99b-7e77-445d-8673-29671da7450f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to classify the intent\n",
        "def classify_intent(query):\n",
        "    \"\"\"Classify the intent of the query.\"\"\"\n",
        "    query_embedding = model.encode([query])\n",
        "    similarities = cosine_similarity(query_embedding, example_embeddings)\n",
        "    intent_index = np.argmax(similarities)\n",
        "    return intents[intent_index]"
      ],
      "metadata": {
        "id": "MG3QFfveR_N8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def ai_engine(query):\n",
        "    intent = classify_intent(query)\n",
        "    if intent == \"book_ticket\":\n",
        "        print(\"Booking Agent is handling the request...\")\n",
        "        response = booking_agent.act(query)\n",
        "    elif intent == \"get_offers\":\n",
        "        print(\"Offer Agent is handling the request...\")\n",
        "        response = offer_agent.act(query)\n",
        "    elif intent == \"search_product\":\n",
        "        print(\"Shopping Agent is handling the request...\")\n",
        "        response = shopping_agent.act(query)\n",
        "    elif intent == \"get_price_trend\":\n",
        "        print(\"Price Trend Agent is handling the request...\")\n",
        "        response = price_trend_agent.act(query)\n",
        "    else:\n",
        "        print(\"LLM Agent is handling the request...\")\n",
        "        response, sentiment = llm_agent.act(query)\n",
        "    return response\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "EBMVzMimSBwr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create text input widget\n",
        "input_text = widgets.Text(\n",
        "    value='',\n",
        "    placeholder='Type your query here',\n",
        "    description='User:',\n",
        "    disabled=False\n",
        ")\n",
        "\n",
        "# Create output widget\n",
        "output = widgets.Output()\n",
        "\n",
        "# Define the callback function for the text input\n",
        "def on_submit(query):\n",
        "    with output:\n",
        "        output.clear_output()\n",
        "        response = ai_engine(query.value)\n",
        "        print(f\"AI Engine Response: {response}\")\n",
        "\n",
        "# Link the callback function to the text input\n",
        "input_text.on_submit(on_submit)\n",
        "\n",
        "# Display the widgets\n",
        "display(input_text, output)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118,
          "referenced_widgets": [
            "3bfc2d790c51443abe30e98f083249ec",
            "d617fe4cc8ab4e4fa5ff5afcb65e170e",
            "b74be7f6445f4626aa10a27ac9401944",
            "ee72ebde3b8f45d3a3761777c7c244b5",
            "8a65ebef585843dd84ba67587e8a216d"
          ]
        },
        "id": "pxdfjziURgdS",
        "outputId": "c0aba8e1-59e0-43f3-c7c9-f68f053ce111"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Text(value='', description='User:', placeholder='Type your query here')"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3bfc2d790c51443abe30e98f083249ec"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Output()"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee72ebde3b8f45d3a3761777c7c244b5"
            }
          },
          "metadata": {}
        }
      ]
    }
  ]
}