Model_6.ipynb

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                  "User: ¿Qué es un portal de datos ITAS?\n",
                  "Bot:  Un portal de datos ITAS es una aplicación web que ofrece la funcionalidad extensiva de la mesa del comerciante y mezcla la extensa experiencia de la industria del comercio con la tecnología más actualizada y una experiencia para el usuario mejorada. Permite acceder a la información de las entidades comerciales y compañías, mostrar información activa en las tabas de actividad, y proporciona vistas de datos personalizables.\n"
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.9/57.9 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m105.6/105.6 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.3/67.3 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m698.9/698.9 kB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.8/50.8 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m341.4/341.4 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m130.2/130.2 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\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",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "lida 0.0.10 requires python-multipart, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "#install required packages\n",
        "!pip install -q transformers peft  accelerate bitsandbytes safetensors sentencepiece streamlit chromadb langchain sentence-transformers sacremoses pypdf\n",
        "\n",
        "# fixing unicode error in google colab\n",
        "import locale\n",
        "locale.getpreferredencoding = lambda: \"UTF-8\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline, MarianMTModel, MarianTokenizer\n",
        "from langchain.vectorstores import Chroma\n",
        "from langchain.embeddings import HuggingFaceEmbeddings\n",
        "from langchain.chains import ConversationalRetrievalChain\n",
        "from langchain.memory import ConversationBufferMemory\n",
        "from langchain.llms import HuggingFacePipeline\n",
        "from langchain.document_loaders import PyPDFDirectoryLoader, TextLoader\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from ipywidgets import widgets\n",
        "from IPython.display import display\n",
        "from google.colab import drive"
      ],
      "metadata": {
        "id": "UDOIKTl_waMQ"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Chatbot:\n",
        "    def __init__(self, translation_model_name, source_language_code, target_language_code, model, tokenizer, retriever, llm, chat_history):\n",
        "        self.translation_model = MarianMTModel.from_pretrained(translation_model_name)\n",
        "        self.translation_tokenizer = MarianTokenizer.from_pretrained(translation_model_name)\n",
        "        self.source_language_code = source_language_code\n",
        "        self.target_language_code = target_language_code\n",
        "\n",
        "        self.model = model\n",
        "        self.tokenizer = tokenizer\n",
        "        self.retriever = retriever\n",
        "        self.llm = llm\n",
        "        self.chat_history = chat_history\n",
        "\n",
        "    def translate(self, text):\n",
        "        inputs = self.translation_tokenizer(text, return_tensors=\"pt\", truncation=True)\n",
        "        translation = self.translation_model.generate(**inputs)\n",
        "        translated_text = self.translation_tokenizer.batch_decode(translation, skip_special_tokens=True)[0]\n",
        "        return translated_text\n",
        "\n",
        "    def create_conversation(self, query):\n",
        "        try:\n",
        "            memory = ConversationBufferMemory(\n",
        "                memory_key='chat_history',\n",
        "                return_messages=False\n",
        "            )\n",
        "            qa_chain = ConversationalRetrievalChain.from_llm(\n",
        "                llm=self.llm,\n",
        "                retriever=self.retriever,\n",
        "                memory=memory,\n",
        "                get_chat_history=lambda h: h,\n",
        "            )\n",
        "\n",
        "            result = qa_chain({'question': query, 'chat_history': self.chat_history})\n",
        "            self.chat_history.append((query, result['answer']))\n",
        "            return '', self.chat_history\n",
        "\n",
        "        except Exception as e:\n",
        "            self.chat_history.append((query, e))\n",
        "            return '', self.chat_history\n",
        "\n",
        "    def on_submit_button_click(self, b):\n",
        "        query = self.query_input.value\n",
        "        use_spanish = self.spanish_checkbox.value\n",
        "\n",
        "        if use_spanish:\n",
        "            translated_query = self.translate(query)\n",
        "        else:\n",
        "            translated_query = query\n",
        "\n",
        "        response, self.chat_history = self.create_conversation(translated_query)\n",
        "\n",
        "        if use_spanish:\n",
        "            translated_response = self.translate(response)\n",
        "        else:\n",
        "            translated_response = response\n",
        "\n",
        "        with self.response_output:\n",
        "            print(translated_response)\n",
        "\n",
        "        self.chat_history_output.clear_output(wait=True)\n",
        "        with self.chat_history_output:\n",
        "            for entry in self.chat_history:\n",
        "                print(f\"User: {entry[0]}\")\n",
        "                print(f\"Bot: {entry[1]}\")\n",
        "\n",
        "    def display_widgets(self):\n",
        "        self.query_input = widgets.Text(description=\"Your message:\")\n",
        "        self.response_output = widgets.Output()\n",
        "        self.chat_history_output = widgets.Output()\n",
        "\n",
        "        self.spanish_checkbox = widgets.Checkbox(value=False, description=\"Use Spanish\")\n",
        "\n",
        "        self.submit_button = widgets.Button(description=\"Send\")\n",
        "        self.submit_button.on_click(self.on_submit_button_click)\n",
        "\n",
        "        display(self.query_input, self.spanish_checkbox, self.submit_button, self.response_output, self.chat_history_output)\n"
      ],
      "metadata": {
        "id": "TbqJd46Wwg-t"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class ChatbotTraining:\n",
        "    def __init__(self, translation_model_name, source_language_code, target_language_code, model_name, folder_path, embedding_model_name):\n",
        "        self.translation_model = MarianMTModel.from_pretrained(translation_model_name)\n",
        "        self.translation_tokenizer = MarianTokenizer.from_pretrained(translation_model_name)\n",
        "        self.source_language_code = source_language_code\n",
        "        self.target_language_code = target_language_code\n",
        "\n",
        "        self.model = self.load_quantized_model(model_name)\n",
        "        self.tokenizer = self.initialize_tokenizer(model_name)\n",
        "\n",
        "        drive.mount('/content/drive')\n",
        "        self.folder_path = folder_path\n",
        "        self.documents = self.load_documents()\n",
        "\n",
        "        self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)\n",
        "        self.all_splits = self.text_splitter.split_documents(self.documents)\n",
        "\n",
        "        self.embedding_model_name = embedding_model_name\n",
        "        self.model_kwargs = {\"device\": \"cuda\"}\n",
        "        self.embeddings = HuggingFaceEmbeddings(model_name=self.embedding_model_name, model_kwargs=self.model_kwargs)\n",
        "\n",
        "        self.vectordb = Chroma.from_documents(documents=self.all_splits, embedding=self.embeddings, persist_directory=\"chroma_db\")\n",
        "        self.retriever = self.vectordb.as_retriever()\n",
        "\n",
        "        self.pipeline = pipeline(\n",
        "            \"text-generation\",\n",
        "            model=self.model,\n",
        "            tokenizer=self.tokenizer,\n",
        "            use_cache=True,\n",
        "            device_map=\"auto\",\n",
        "            max_length=2048,\n",
        "            do_sample=True,\n",
        "            top_k=5,\n",
        "            num_return_sequences=1,\n",
        "            eos_token_id=self.tokenizer.eos_token_id,\n",
        "            pad_token_id=self.tokenizer.eos_token_id,\n",
        "        )\n",
        "\n",
        "        self.llm = HuggingFacePipeline(pipeline=self.pipeline)\n",
        "\n",
        "    def load_quantized_model(self, model_name):\n",
        "        bnb_config = BitsAndBytesConfig(\n",
        "            load_in_4bit=True,\n",
        "            bnb_4bit_use_double_quant=True,\n",
        "            bnb_4bit_quant_type=\"nf4\",\n",
        "            bnb_4bit_compute_dtype=torch.bfloat16\n",
        "        )\n",
        "\n",
        "        model = AutoModelForCausalLM.from_pretrained(\n",
        "            model_name,\n",
        "            load_in_4bit=True,\n",
        "            torch_dtype=torch.bfloat16,\n",
        "            quantization_config=bnb_config\n",
        "        )\n",
        "        return model\n",
        "\n",
        "    def initialize_tokenizer(self, model_name):\n",
        "        tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "        tokenizer.bos_token_id = 1\n",
        "        return tokenizer\n",
        "\n",
        "    def load_documents(self):\n",
        "        loader = PyPDFDirectoryLoader(self.folder_path)\n",
        "        return loader.load()"
      ],
      "metadata": {
        "id": "l2OO6NCbwnDF"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Usage Example:\n",
        "translation_model_name = \"Helsinki-NLP/opus-mt-en-es\"\n",
        "source_language_code = \"es\"\n",
        "target_language_code = \"en\"\n",
        "model_name = \"anakin87/zephyr-7b-alpha-sharded\"\n",
        "folder_path = '/content/drive/MyDrive/chatbot_data/'\n",
        "embedding_model_name = \"sentence-transformers/all-mpnet-base-v2\""
      ],
      "metadata": {
        "id": "euqM3md6wuvb"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chatbot_training = ChatbotTraining(translation_model_name, source_language_code, target_language_code, model_name, folder_path, embedding_model_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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            "8270933ffb674a6eb4c74156c807f419"
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        "id": "ZfSiOCrtwyQD",
        "outputId": "f687bd51-0f81-4b3d-d50d-c79b47c7a72d"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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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          "text": [
            "Mounted at /content/drive\n"
          ]
        },
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    {
      "cell_type": "code",
      "source": [
        "# Create chatbot instances\n",
        "chatbot_1 = Chatbot(translation_model_name, source_language_code, target_language_code,\n",
        "                   chatbot_training.model, chatbot_training.tokenizer,\n",
        "                   chatbot_training.retriever, chatbot_training.llm, [])\n"
      ],
      "metadata": {
        "id": "1S0yIST6w14p"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "chatbot_2 = Chatbot(translation_model_name, source_language_code, target_language_code,\n",
        "                   chatbot_training.model, chatbot_training.tokenizer,\n",
        "                   chatbot_training.retriever, chatbot_training.llm, [])"
      ],
      "metadata": {
        "id": "VZLu-fz5w6VR"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display widgets for chatbots\n",
        "chatbot_1.display_widgets()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339,
          "referenced_widgets": [
            "080e35bf32984c81a9bea2b7994b605f",
            "e367ee83e6ac402c9c70c51b1e54ef5a",
            "811cdadd5f954181a84ad2fa9eb6dad1",
            "9e9704cf4bcd4d26ae00bc0821edbc54",
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            "a80439b57624445ab8a5b6a5359ef956",
            "b1524b0351964ec1a8becdecf966572c",
            "f105772bc25a4336b54ff7559f9d19d2",
            "3842be28ddb34bf2ab7e69b9f4902629",
            "ecaba9a54f31446c892f5fb72873e460",
            "38fed3d5ede547399b5c9d6184573df4",
            "49c6b7b852294f0ca8a7e1dde150fb5b",
            "0f6cba73672d42fe95cbdd160c778968"
          ]
        },
        "id": "4doXH5Kww8pX",
        "outputId": "f22a0464-f9d4-46b8-964a-04a92f64363d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Text(value='', description='Your message:')"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "080e35bf32984c81a9bea2b7994b605f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Checkbox(value=False, description='Use Spanish')"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9e9704cf4bcd4d26ae00bc0821edbc54"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Button(description='Send', style=ButtonStyle())"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b1524b0351964ec1a8becdecf966572c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Output()"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ecaba9a54f31446c892f5fb72873e460"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Output()"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "49c6b7b852294f0ca8a7e1dde150fb5b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n",
            "  warn_deprecated(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1473: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use and modify the model generation configuration (see https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chatbot_2.display_widgets()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232,
          "referenced_widgets": [
            "0e62b09833ff44379121aa6a487476c1",
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            "de7b840d388041cfb1dd34dcff11de18",
            "d4cb0082408748c9b95e1581099707ea",
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