chatbot_ModelIA.ipynb

{
  "cells": [
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      "cell_type": "markdown",
      "source": [
        "#**Aula 5: Criando um Chatbot**"
      ],
      "metadata": {
        "id": "uVbdsqQJuc_X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Selecionando o modelo**"
      ],
      "metadata": {
        "id": "lJtjaYB6ug3U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Modelo: [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium)"
      ],
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        "id": "rC1nb0dOujRR"
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            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
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      ],
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "import torch\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/DialoGPT-medium\")\n",
        "model = AutoModelForCausalLM.from_pretrained(\"microsoft/DialoGPT-medium\")"
      ]
    },
    {
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      "execution_count": null,
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      "outputs": [
        {
          "name": "stdout",
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          "text": [
            ">> User:Hello!\n",
            "DialoGPT: Hello! :D\n",
            ">> User:How are you doing?\n",
            "DialoGPT: I'm doing well! How are you?\n",
            ">> User:I'm fine\n",
            "DialoGPT: That's good!\n",
            ">> User:Have a good day\n",
            "DialoGPT: You too! :D\n",
            ">> User:bye\n",
            "DialoGPT: Bye! :D\n"
          ]
        }
      ],
      "source": [
        "# Let's chat for 5 lines\n",
        "for step in range(5):\n",
        "    # encode the new user input, add the eos_token and return a tensor in Pytorch\n",
        "    new_user_input_ids = tokenizer.encode(input(\">> User:\") + tokenizer.eos_token, return_tensors='pt')\n",
        "\n",
        "    # append the new user input tokens to the chat history\n",
        "    bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids\n",
        "\n",
        "    # generated a response while limiting the total chat history to 1000 tokens,\n",
        "    chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)\n",
        "\n",
        "    # pretty print last ouput tokens from bot\n",
        "    print(\"DialoGPT: {}\".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Criando o chatbot**"
      ],
      "metadata": {
        "id": "4CsN2TAuu_OL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o3V2jgo-ZBuI"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C5AxRNamaq_S"
      },
      "outputs": [],
      "source": [
        "dados_pedidos = {\n",
        "    \"numero_pedido\": [\"12345\", \"67890\", \"11121\", \"22232\"],\n",
        "    \"status\": [\"Shipped\", \"Processing\", \"Delivered\", \"Cancelled\"]\n",
        "}\n",
        "df_status_pedidos = pd.DataFrame(dados_pedidos)"
      ]
    },
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              "\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_8661072e-c8f1-4b16-87b0-432d8155f827\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
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              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
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              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_status_pedidos')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_status_pedidos');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "  numero_pedido      status\n",
              "0         12345     Shipped\n",
              "1         67890  Processing\n",
              "2         11121   Delivered\n",
              "3         22232   Cancelled"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_status_pedidos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_g03gvePcow5"
      },
      "outputs": [],
      "source": [
        "def verificar_status_pedido(numero_pedido):\n",
        "  try:\n",
        "    status = df_status_pedidos[df_status_pedidos['numero_pedido'] == numero_pedido]['status'].iloc[0]\n",
        "\n",
        "    return f'The status of your order {numero_pedido} is: {status}'\n",
        "\n",
        "  except:\n",
        "    return 'Order number not found. Please check and try again'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ruXV3CgId0od"
      },
      "outputs": [],
      "source": [
        "palavras_chave_status = ['order', 'order status', 'status of my order', 'check my order', 'track my order', 'order update']"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Interagindo com o chatbot**"
      ],
      "metadata": {
        "id": "MGYuIuaQvCtW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fsNYV1OAeSlV",
        "outputId": "dcd981f4-3eaf-4047-c139-5b5749126a34"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "You: Hello!!\n",
            "Bot: Hello! :D\n",
            "You: How are you doing?\n",
            "Bot: I'm doing well! How are you?\n",
            "You: I'm great\n",
            "Bot: That's good!\n",
            "You: I would like to check the status of my order\n",
            "Could you please enter you order number?12345\n",
            "Bot: The status of your order 12345 is: Shipped\n",
            "You: Thanks a lot\n",
            "Bot: No problem :D\n",
            "You: stop\n",
            "Bot: Goodbye!\n"
          ]
        }
      ],
      "source": [
        "ids_historico_chat = None\n",
        "\n",
        "while True:\n",
        "  input_usuario = input('You: ')\n",
        "\n",
        "  if input_usuario.lower() in ['exit', 'quit', 'stop']:\n",
        "    print('Bot: Goodbye!')\n",
        "    break\n",
        "\n",
        "  if any(keyword in input_usuario.lower() for keyword in palavras_chave_status):\n",
        "    numero_pedido = input('Could you please enter you order number?')\n",
        "    resposta = verificar_status_pedido(numero_pedido)\n",
        "  else:\n",
        "    novo_usuario_input_ids = tokenizer.encode(input_usuario + tokenizer.eos_token, return_tensors='pt')\n",
        "\n",
        "    if ids_historico_chat is not None:\n",
        "      bot_input_ids = torch.cat([ids_historico_chat, novo_usuario_input_ids ], dim=-1)\n",
        "\n",
        "    else:\n",
        "      bot_input_ids = novo_usuario_input_ids\n",
        "\n",
        "    ids_historico_chat = model.generate(\n",
        "        bot_input_ids,\n",
        "        max_length=1000,\n",
        "        pad_token_id=tokenizer.eos_token_id\n",
        "    )\n",
        "    resposta = tokenizer.decode(ids_historico_chat[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)\n",
        "\n",
        "  print(f'Bot: {resposta}')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Aula 6: Criando a interface do Chatbot**"
      ],
      "metadata": {
        "id": "bVxDMVNrvKmy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PWTxQwqMjC2F",
        "outputId": "30b95b3d-ab9f-48d4-c407-6c3d41a5eeaa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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            "\u001b[?25hBuilding wheels for collected packages: ffmpy\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Attempting uninstall: tomlkit\n",
            "    Found existing installation: tomlkit 0.13.0\n",
            "    Uninstalling tomlkit-0.13.0:\n",
            "      Successfully uninstalled tomlkit-0.13.0\n",
            "Successfully installed aiofiles-23.2.1 dnspython-2.6.1 email_validator-2.2.0 fastapi-0.111.1 fastapi-cli-0.0.4 ffmpy-0.3.2 gradio-4.39.0 gradio-client-1.1.1 h11-0.14.0 httpcore-1.0.5 httptools-0.6.1 httpx-0.27.0 orjson-3.10.6 pydub-0.25.1 python-dotenv-1.0.1 python-multipart-0.0.9 ruff-0.5.5 semantic-version-2.10.0 starlette-0.37.2 tomlkit-0.12.0 uvicorn-0.30.3 uvloop-0.19.0 watchfiles-0.22.0 websockets-11.0.3\n"
          ]
        }
      ],
      "source": [
        "pip install gradio"
      ]
    },
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      },
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          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://ba4d71231399ca2b18.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
        },
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              "<div><iframe src=\"https://ba4d71231399ca2b18.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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      ],
      "source": [
        "# Importações necessárias\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "import torch\n",
        "import pandas as pd\n",
        "import gradio as gr\n",
        "\n",
        "# Carrega o tokenizador e o modelo\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/DialoGPT-medium\")\n",
        "model = AutoModelForCausalLM.from_pretrained(\"microsoft/DialoGPT-medium\")\n",
        "\n",
        "# Criando um dataframe com status de pedidos\n",
        "dados_pedidos = {\n",
        "    \"numero_pedido\": [\"12345\", \"67890\", \"11121\", \"22232\"],\n",
        "    \"status\": [\"Shipped\", \"Processing\", \"Delivered\", \"Cancelled\"]\n",
        "}\n",
        "df_status_pedidos = pd.DataFrame(dados_pedidos)\n",
        "\n",
        "# Função para verificar o status do pedido\n",
        "def verificar_status_pedido(numero_pedido):\n",
        "  try:\n",
        "    status = df_status_pedidos[df_status_pedidos['numero_pedido'] == numero_pedido]['status'].iloc[0]\n",
        "\n",
        "    return f'The status of your order {numero_pedido} is: {status}'\n",
        "\n",
        "  except:\n",
        "    return 'Order number not found. Please check and try again'\n",
        "\n",
        "# Lista de palavras-chave para o status\n",
        "palavras_chave_status = ['order', 'order status', 'status of my order', 'check my order', 'track my order', 'order update']\n",
        "\n",
        "def responder(input_usuario, ids_historico_chat):\n",
        "  if any(keyword in input_usuario.lower() for keyword in palavras_chave_status):\n",
        "    return 'Could you please enter you order number?', ids_historico_chat\n",
        "  else:\n",
        "    novo_usuario_input_ids = tokenizer.encode(input_usuario + tokenizer.eos_token, return_tensors='pt')\n",
        "\n",
        "    if ids_historico_chat is not None:\n",
        "      bot_input_ids = torch.cat([ids_historico_chat, novo_usuario_input_ids ], dim=-1)\n",
        "\n",
        "    else:\n",
        "      bot_input_ids = novo_usuario_input_ids\n",
        "\n",
        "    ids_historico_chat = model.generate(\n",
        "        bot_input_ids,\n",
        "        max_length=1000,\n",
        "        pad_token_id=tokenizer.eos_token_id\n",
        "    )\n",
        "    resposta = tokenizer.decode(ids_historico_chat[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)\n",
        "\n",
        "  return resposta, ids_historico_chat\n",
        "\n",
        "# Criando interface\n",
        "with gr.Blocks() as app:\n",
        "  chatbot = gr.Chatbot()\n",
        "  msg = gr.Textbox(placeholder='Type your message here...')\n",
        "\n",
        "  estado = gr.State(None)\n",
        "  aguardando_numero_pedido = gr.State(False)\n",
        "\n",
        "  def processar_entrada(input_usuario, historico, ids_historico_chat, aguardando_numero_pedido):\n",
        "    if aguardando_numero_pedido:\n",
        "      resposta = verificar_status_pedido(input_usuario)\n",
        "      aguardando_numero_pedido = False\n",
        "    else:\n",
        "      resposta, ids_historico_chat = responder(input_usuario, ids_historico_chat)\n",
        "      if resposta == 'Could you please enter you order number?':\n",
        "        aguardando_numero_pedido = True\n",
        "\n",
        "    historico.append((input_usuario, resposta))\n",
        "    return historico, ids_historico_chat, aguardando_numero_pedido, \"\"\n",
        "\n",
        "\n",
        "  msg.submit(\n",
        "      processar_entrada,\n",
        "      [msg, chatbot, estado, aguardando_numero_pedido],\n",
        "      [chatbot, estado, aguardando_numero_pedido, msg]\n",
        "  )\n",
        "\n",
        "  app.launch(share=Tru)\n",
        "\n"
      ]
    }
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