LLM_Q&A_with_Open_Source_Hugging_Face_Models.ipynb

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            "Fri Mar 31 14:43:09 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   54C    P8    11W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m264.6/264.6 KB\u001b[0m \u001b[31m28.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.1/49.1 KB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for llama-index (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "! pip install -q langchain transformers sentence_transformers llama-index"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from llama_index import SimpleDirectoryReader, LangchainEmbedding, GPTListIndex,GPTSimpleVectorIndex, PromptHelper\n",
        "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
        "from llama_index import LLMPredictor, ServiceContext\n",
        "import torch\n",
        "from langchain.llms.base import LLM\n",
        "from transformers import pipeline\n"
      ],
      "metadata": {
        "id": "aGCX8IBekbNd"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "class customLLM(LLM):\n",
        "    model_name = \"google/flan-t5-large\"\n",
        "    pipeline = pipeline(\"text2text-generation\", model=model_name, device=0, model_kwargs={\"torch_dtype\":torch.bfloat16})\n",
        "\n",
        "    def _call(self, prompt, stop=None):\n",
        "        return self.pipeline(prompt, max_length=9999)[0][\"generated_text\"]\n",
        " \n",
        "    def _identifying_params(self):\n",
        "        return {\"name_of_model\": self.model_name}\n",
        "\n",
        "    def _llm_type(self):\n",
        "        return \"custom\"\n",
        "\n",
        "\n",
        "llm_predictor = LLMPredictor(llm=customLLM())\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353,
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        "id": "nVcWX65LkfG6",
        "outputId": "ecea03b1-3037-4a38-d917-ce5f5c1f3335"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/662 [00:00<?, ?B/s]"
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/3.13G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "63066e9dd0ae43239d18119f19229599"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "09a3ff8b85034a32ae564d1674f186fb"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e8db44277dfb46c5861f11987e598c35"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "10aa72e95d194badbae38d90dec15531"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "88b7e7c94d034154998f1570e7284866"
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        {
          "output_type": "display_data",
          "data": {
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            ],
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              "version_major": 2,
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    {
      "cell_type": "code",
      "source": [
        "hfemb = HuggingFaceEmbeddings()\n",
        "embed_model = LangchainEmbedding(hfemb)"
      ],
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    {
      "cell_type": "code",
      "source": [
        "text1 = \"\"\"The use of NLP in the realm of financial technology is broad and complex, with applications\n",
        "ranging from sentiment analysis and named entity recognition to question answering. Large\n",
        "Language Models (LLMs) have been shown to be effective on a variety of tasks; however, no\n",
        "LLM specialized for the financial domain has been reported in literature. In this work, we\n",
        "present BloombergGPT, a 50 billion parameter language model that is trained on a wide\n",
        "range of financial data. We construct a 363 billion token dataset based on Bloomberg’s\n",
        "extensive data sources, perhaps the largest domain-specific dataset yet, augmented with 345\n",
        "billion tokens from general purpose datasets. We validate BloombergGPT on standard\n",
        "LLM benchmarks, open financial benchmarks, and a suite of internal benchmarks that most\n",
        "accurately reflect our intended usage. Our mixed dataset training leads to a model that\n",
        "outperforms existing models on financial tasks by significant margins without sacrificing\n",
        "performance on general LLM benchmarks. Additionally, we explain our modeling choices,\n",
        "training process, and evaluation methodology. As a next step, we plan to release training\n",
        "logs (Chronicles) detailing our experience in training BloombergGPT.\n",
        "Snap Inc., the creator of Snapchat, introduced My AI for Snapchat+ this week. The experimental feature is running on ChatGPT API. My AI offers Snapchatters a friendly, customizable chatbot at their fingertips that offers recommendations, and can even write a haiku for friends in seconds. Snapchat, where communication and messaging is a daily behavior, has 750 million monthly Snapchatters.\n",
        "\n",
        "\n",
        "Quizlet Q-Chat, UI screenshot\n",
        "\n",
        "Play video\n",
        "Quizlet Q-Chat\n",
        "\n",
        "Quizlet is a global learning platform with more than 60 million students using it to study, practice and master whatever they’re learning. Quizlet has worked with OpenAI for the last three years, leveraging GPT-3 across multiple use cases, including vocabulary learning and practice tests. With the launch of ChatGPT API, Quizlet is introducing Q-Chat, a fully-adaptive AI tutor that engages students with adaptive questions based on relevant study materials delivered through a fun chat experience.\n",
        "\n",
        "\n",
        "Instacart’s Ask Instacart, UI screenshot\n",
        "Instacart’s Ask Instacart\n",
        "\n",
        "Instacart is augmenting the Instacart app to enable customers to ask about food and get inspirational, shoppable answers. This uses ChatGPT alongside Instacart’s own AI and product data from their 75,000+ retail partner store locations to help customers discover ideas for open-ended shopping goals, such as “How do I make great fish tacos?” or “What’s a healthy lunch for my kids?” Instacart plans to launch “Ask Instacart” later this year.\n",
        "\n",
        "\n",
        "\n",
        "Play video\n",
        "Shopify’s Shop app\n",
        "\n",
        "Shop, Shopify’s consumer app, is used by 100 million shoppers to find and engage with the products and brands they love. ChatGPT API is used to power Shop’s new shopping assistant. When shoppers search for products, the shopping assistant makes personalized recommendations based on their requests. Shop’s new AI-powered shopping assistant will streamline in-app shopping by scanning millions of products to quickly find what buyers are looking for—or help them discover something new.\n",
        "\n",
        "\n",
        "The Speak App, UI screenshot\n",
        "\n",
        "Play video\n",
        "The Speak app\n",
        "\n",
        "Speak is an AI-powered language learning app focused on building the best path to spoken fluency. They’re the fastest-growing English app in South Korea, and are already using the Whisper API to power a new AI speaking companion product, and rapidly bring it to the rest of the globe. Whisper’s human-level accuracy for language learners of every level unlocks true open-ended conversational practice and highly accurate feedback.\n",
        "\n",
        "ChatGPT API\n",
        "Model: The ChatGPT model family we are releasing today, gpt-3.5-turbo, is the same model used in the ChatGPT product. It is priced at $0.002 per 1k tokens, which is 10x cheaper than our existing GPT-3.5 models. It’s also our best model for many non-chat use cases—we’ve seen early testers migrate from text-davinci-003 to gpt-3.5-turbo with only a small amount of adjustment needed to their prompts.\n",
        "\n",
        "\n",
        "API: Traditionally, GPT models consume unstructured text, which is represented to the model as a sequence of “tokens.” ChatGPT models instead consume a sequence of messages together with metadata. (For the curious: under the hood, the input is still rendered to the model as a sequence of “tokens” for the model to consume; the raw format used by the model is a new format called Chat Markup Language (“ChatML”).)\n",
        "\n",
        "We’ve created a new endpoint to interact with our ChatGPT models:\n",
        "\n",
        "Request\n",
        "Response\n",
        "Python bindings\n",
        "curl https://api.openai.com/v1/chat/completions\n",
        "  -H \"Authorization: Bearer $OPENAI_API_KEY\"\n",
        "  -H \"Content-Type: application/json\"\n",
        "  -d '{\n",
        "  \"model\": \"gpt-3.5-turbo\",\n",
        "  \"messages\": [{\"role\": \"user\", \"content\": \"What is the OpenAI mission?\"}]\n",
        "}'\n",
        "To learn more about the ChatGPT API, visit our Chat guide.\n",
        "\n",
        "ChatGPT upgrades\n",
        "We are constantly improving our ChatGPT models, and want to make these enhancements available to developers as well. Developers who use the gpt-3.5-turbo model will always get our recommended stable model, while still having the flexibility to opt for a specific model version. For example, today we’re releasing gpt-3.5-turbo-0301, which will be supported through at least June 1st, and we’ll update gpt-3.5-turbo to a new stable release in April. The models page will provide switchover updates.\n",
        "\n",
        "Dedicated instances\n",
        "We are also now offering dedicated instances for users who want deeper control over the specific model version and system performance. By default, requests are run on compute infrastructure shared with other users, who pay per request. Our API runs on Azure, and with dedicated instances, developers will pay by time period for an allocation of compute infrastructure that’s reserved for serving their requests.\n",
        "\n",
        "Developers get full control over the instance’s load (higher load improves throughput but makes each request slower), the option to enable features such as longer context limits, and the ability to pin the model snapshot.\n",
        "\n",
        "Dedicated instances can make economic sense for developers running beyond ~450M tokens per day. Additionally, it enables directly optimizing a developer’s workload against hardware performance, which can dramatically reduce costs relative to shared infrastructure. For dedicated instance inquiries, contact us.\n",
        "\n",
        "Whisper API\n",
        "Whisper, the speech-to-text model we open-sourced in September 2022, has received immense praise from the developer community but can also be hard to run. We’ve now made the large-v2 model available through our API, which gives convenient on-demand access priced at $0.006 / minute. In addition, our highly-optimized serving stack ensures faster performance compared to other services.\n",
        "\n",
        "Whisper API is available through our transcriptions (transcribes in source language) or translations (transcribes into English) endpoints, and accepts a variety of formats (m4a, mp3, mp4, mpeg, mpga, wav, webm):\n",
        "\n",
        "Request\n",
        "Response\n",
        "Python bindings\n",
        "curl https://api.openai.com/v1/audio/transcriptions \\\n",
        "  -H \"Authorization: Bearer $OPENAI_API_KEY\" \\\n",
        "  -H \"Content-Type: multipart/form-data\" \\\n",
        "  -F model=\"whisper-1\" \\\n",
        "  -F file=\"@/path/to/file/openai.mp3\"\n",
        "To learn more about the Whisper API, visit our Speech to Text guide.\n",
        "\n",
        "Developer focus\n",
        "Over the past six months, we’ve been collecting feedback from our API customers to understand how we can better serve them. We’ve made concrete changes, such as:\n",
        "\n",
        "Data submitted through the API is no longer used for service improvements (including model training) unless the organization opts in\n",
        "Implementing a default 30-day data retention policy for API users, with options for stricter retention depending on user needs.\n",
        "Removing our pre-launch review (unlocked by improving our automated monitoring)\n",
        "Improving developer documentation\n",
        "Simplifying our Terms of Service and Usage Policies, including terms around data ownership: users own the input and output of the models.\n",
        "For the past two months our uptime has not met our own expectations nor that of our users. Our engineering team’s top priority is now stability of production use cases—we know that ensuring AI benefits all of humanity requires being a reliable service provider. Please hold us accountable for improved uptime over the upcoming months!\n",
        "\n",
        "We believe that AI can provide incredible opportunities and economic empowerment to everyone, and the best way to achieve that is to allow everyone to build with it. We hope that the changes we announced today will lead to numerous applications that everyone can benefit from. Start building next-generation apps powered by ChatGPT & Whisper.\"\"\""
      ],
      "metadata": {
        "id": "-R7tALDKn1X2"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#documents = SimpleDirectoryReader('data').load_data()\n",
        "\n",
        "from llama_index import Document\n",
        "\n",
        "text_list = [text1]\n",
        "\n",
        "documents = [Document(t) for t in text_list]"
      ],
      "metadata": {
        "id": "DFM8PBkZpsaI"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # set number of output tokens\n",
        "# num_output = 500\n",
        "# # set maximum input size\n",
        "# max_input_size = 512\n",
        "# # set maximum chunk overlap\n",
        "# max_chunk_overlap = 15\n",
        "\n",
        "\n",
        "# prompt_helper = PromptHelper(max_input_size, num_output, max_chunk_overlap)"
      ],
      "metadata": {
        "id": "RrBFPZf8pvu_"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#index = GPTSimpleVectorIndex(documents, embed_model=embed_model, llm_predictor=llm_predictor)\n",
        "\n",
        "#index = GPTListIndex(documents, embed_model=embed_model, llm_predictor=llm_predictor)\n",
        "\n",
        "#index.save_to_disk('index.json')\n",
        "\n",
        "service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, embed_model=embed_model)\n",
        "index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "B2oE7bPfpyif"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import logging\n",
        "\n",
        "logging.getLogger().setLevel(logging.CRITICAL)"
      ],
      "metadata": {
        "id": "qAord7Hdp0Ps"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response = index.query( \"What's the cost of Whisper model?\") \n"
      ],
      "metadata": {
        "id": "U6ei1YUCp2IX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79c5aea9-3e90-4b3f-d5df-cdf43fa89611"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (1997 > 512). Running this sequence through the model will result in indexing errors\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response.response"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "bahxucg0my1z",
        "outputId": "78bad4b0-0ba2-4617-d5b8-c89c74d1b0b6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'$0.006 / minute'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# temp fix for running shell commands on Google Colab\n",
        "\n",
        "import locale\n",
        "def getpreferredencoding(do_setlocale = True):\n",
        "    return \"UTF-8\"\n",
        "locale.getpreferredencoding = getpreferredencoding"
      ],
      "metadata": {
        "id": "Nww8v92EKex6"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install gradio -q"
      ],
      "metadata": {
        "id": "42iQRUlYhbgn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "901a8946-fa81-49be-99ac-df006a807b45"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.7/15.7 MB\u001b[0m \u001b[31m52.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.7/45.7 KB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.1/57.1 KB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.5/71.5 KB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.5/50.5 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[32m106.5/106.5 KB\u001b[0m \u001b[31m12.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m144.1/144.1 KB\u001b[0m \u001b[31m16.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.8/57.8 KB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.9/66.9 KB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.6/69.6 KB\u001b[0m \u001b[31m9.2 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[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m80.6/80.6 KB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr"
      ],
      "metadata": {
        "id": "p92tHG-fhHS_"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "index = None"
      ],
      "metadata": {
        "id": "Nya0xFWjxyQq"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def build_the_bot(input_text):\n",
        "  text_list = [input_text]\n",
        "  documents = [Document(t) for t in text_list]\n",
        "  global index\n",
        "  service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, embed_model=embed_model)\n",
        "  index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)\n",
        "  return('Index saved successfull!!!')"
      ],
      "metadata": {
        "id": "gTjRf-Cok8Ew"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def chat(chat_history, user_input):\n",
        "  \n",
        "  bot_response = index.query(user_input)\n",
        "  #print(bot_response)\n",
        "  response = \"\"\n",
        "  for letter in ''.join(bot_response.response): #[bot_response[i:i+1] for i in range(0, len(bot_response), 1)]:\n",
        "      response += letter + \"\"\n",
        "      yield chat_history + [(user_input, response)]"
      ],
      "metadata": {
        "id": "Vz1Dufj9jyMh"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "with gr.Blocks() as demo:\n",
        "    gr.Markdown('# Q&A Bot with Hugging Face Models')\n",
        "    with gr.Tab(\"Input Text Document\"):\n",
        "        text_input = gr.Textbox()\n",
        "        text_output = gr.Textbox()\n",
        "        text_button = gr.Button(\"Build the Bot!!!\")\n",
        "        text_button.click(build_the_bot, text_input, text_output)\n",
        "    with gr.Tab(\"Knowledge Bot\"):\n",
        "#          inputbox = gr.Textbox(\"Input your text to build a Q&A Bot here.....\")\n",
        "          chatbot = gr.Chatbot()\n",
        "          message = gr.Textbox (\"What is this document about?\")\n",
        "          message.submit(chat, [chatbot, message], chatbot)\n",
        "\n",
        "demo.queue().launch(debug = True)"
      ],
      "metadata": {
        "id": "oCL2RqVZp3AM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 682
        },
        "outputId": "bc5ba8be-cba8-4c8e-c3cf-2676db8f5c96"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. 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://7e34d8e6b2adf10738.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://7e34d8e6b2adf10738.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keyboard interruption in main thread... closing server.\n",
            "Killing tunnel 127.0.0.1:7860 <> https://7e34d8e6b2adf10738.gradio.live\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wwFjGtsrhZKf"
      },
      "execution_count": 29,
      "outputs": []
    }
  ]
}