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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a6b2b70",
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   "source": [
    "import gradio as gr\n",
    "\n",
    "from buster.chatbot import Chatbot, ChatbotConfig\n",
    "\n",
    "hf_transformers_cfg = ChatbotConfig(\n",
    "    documents_file=\"../data/document_embeddings_hf_transformers.tar.gz\",\n",
    "    unknown_prompt=\"This doesn't seem to be related to the huggingface library. I am not sure how to answer.\",\n",
    "    embedding_model=\"text-embedding-ada-002\",\n",
    "    top_k=3,\n",
    "    thresh=0.7,\n",
    "    max_chars=3000,\n",
    "    completion_kwargs={\n",
    "        \"engine\": \"text-davinci-003\",\n",
    "        \"max_tokens\": 500,\n",
    "    },\n",
    "    separator=\"<br>\",\n",
    "    link_format=\"markdown\",\n",
    "    text_after_response=\"I'm a bot 🤖 trained to answer huggingface 🤗 transformers questions. My answers aren't always perfect.\",\n",
    "    text_before_prompt=\"\"\"You are a slack chatbot assistant answering technical questions about huggingface transformers, a library to train transformers in python.\n",
    "    Make sure to format your answers in Markdown format, including code block and snippets.\n",
    "    Do not include any links to urls or hyperlinks in your answers.\n",
    "\n",
    "    If you do not know the answer to a question, or if it is completely irrelevant to the library usage, simply reply with:\n",
    "\n",
    "    'This doesn't seem to be related to the huggingface library.'\n",
    "\n",
    "    For example:\n",
    "\n",
    "    What is the meaning of life for huggingface?\n",
    "\n",
    "    This doesn't seem to be related to the huggingface library.\n",
    "\n",
    "    Now answer the following question:\n",
    "    \"\"\",\n",
    ")\n",
    "hf_transformers_chatbot = Chatbot(hf_transformers_cfg)\n",
    "\n",
    "def chat(question, history):\n",
    "    history = history or []\n",
    "    answer = hf_transformers_chatbot.process_input(question)\n",
    "\n",
    "    history.append((question, answer))\n",
    "    print(history)\n",
    "    return history, history\n",
    "\n",
    "\n",
    "\n",
    "block = gr.Blocks(css=\".gradio-container {background-color: lightgray}\")\n",
    "\n",
    "with block:\n",
    "    with gr.Row():\n",
    "        gr.Markdown(\"<h3><center>Buster 🤖: A Question-Answering Bot for Huggingface 🤗 Transformers </center></h3>\")\n",
    "\n",
    "\n",
    "    chatbot = gr.Chatbot()\n",
    "\n",
    "    with gr.Row():\n",
    "        message = gr.Textbox(\n",
    "            label=\"What's your question?\",\n",
    "            placeholder=\"What kind of model should I use for sentiment analysis?\",\n",
    "            lines=1,\n",
    "        )\n",
    "        submit = gr.Button(value=\"Send\", variant=\"secondary\").style(full_width=False)\n",
    "\n",
    "    gr.Examples(\n",
    "        examples=[\n",
    "            \"What kind of models should I use for images and text?\",\n",
    "            \"When should I finetune a model vs. training it form scratch?\",\n",
    "            \"How can I deploy my trained huggingface model?\",\n",
    "            \"Can you give me some python code to quickly finetune a model on my sentiment analysis dataset?\",\n",
    "        ],\n",
    "        inputs=message,\n",
    "    )\n",
    "\n",
    "    gr.Markdown(\n",
    "    \"\"\"This simple application uses GPT to search the huggingface 🤗 transformers docs and answer questions.\n",
    "    For more info on huggingface transformers view the [full documentation.](https://huggingface.co/docs/transformers/index).\"\"\" \n",
    "    )\n",
    "\n",
    "\n",
    "    gr.HTML(\n",
    "        \"️<center> Created with ❤️ by @jerpint and @hadrienbertrand\"\n",
    "    )\n",
    "\n",
    "    state = gr.State()\n",
    "    agent_state = gr.State()\n",
    "\n",
    "    submit.click(chat, inputs=[message, state], outputs=[chatbot, state])\n",
    "    message.submit(chat, inputs=[message, state], outputs=[chatbot, state])\n",
    "\n",
    "\n",
    "block.launch(debug=True)"
   ]
  }
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