Upload gradio_chatbot_app.ipynb

gradio_chatbot_app.ipynb

@@ -0,0 +1,632 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QyqHXpfzNTi5",
+        "outputId": "9612f9b9-1b51-4469-e50c-d0a0ffdc8e9c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "  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"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install gradio==3.41.0 transformers==4.32.0 langchain==0.0.273 -Uqqq\n",
+        "!pip install accelerate==0.12.0 bitsandbytes==0.41.1 einops==0.7.0 peft==0.5.0 -Uqqq"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7uflzy2_OAjL"
+      },
+      "outputs": [],
+      "source": [
+        "import gradio as gr\n",
+        "import torch\n",
+        "import re, os, warnings\n",
+        "from langchain import PromptTemplate, LLMChain\n",
+        "from langchain.llms.base import LLM\n",
+        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, GenerationConfig\n",
+        "from peft import LoraConfig, get_peft_model, PeftConfig, PeftModel\n",
+        "warnings.filterwarnings(\"ignore\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OYx4Jyh2OV17"
+      },
+      "outputs": [],
+      "source": [
+        "# initialize and load PEFT model and tokenizer\n",
+        "def init_model_and_tokenizer(PEFT_MODEL):\n",
+        "  config = PeftConfig.from_pretrained(PEFT_MODEL)\n",
+        "  bnb_config = BitsAndBytesConfig(\n",
+        "    load_in_4bit=True,\n",
+        "    bnb_4bit_quant_type=\"nf4\",\n",
+        "    bnb_4bit_use_double_quant=True,\n",
+        "    bnb_4bit_compute_dtype=torch.float16,\n",
+        "  )\n",
+        "\n",
+        "  peft_base_model = AutoModelForCausalLM.from_pretrained(\n",
+        "    config.base_model_name_or_path,\n",
+        "    return_dict=True,\n",
+        "    quantization_config=bnb_config,\n",
+        "    device_map=\"auto\",\n",
+        "    trust_remote_code=True,\n",
+        "  )\n",
+        "\n",
+        "  peft_model = PeftModel.from_pretrained(peft_base_model, PEFT_MODEL)\n",
+        "\n",
+        "  peft_tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
+        "  peft_tokenizer.pad_token = peft_tokenizer.eos_token\n",
+        "\n",
+        "  return peft_model, peft_tokenizer"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Azg2zEW2OkP7"
+      },
+      "outputs": [],
+      "source": [
+        "# custom LLM chain to generate answer from PEFT model for each query\n",
+        "def init_llm_chain(peft_model, peft_tokenizer):\n",
+        "    class CustomLLM(LLM):\n",
+        "        def _call(self, prompt: str, stop=None, run_manager=None) -> str:\n",
+        "            device = \"cuda:0\"\n",
+        "            peft_encoding = peft_tokenizer(prompt, return_tensors=\"pt\").to(device)\n",
+        "            peft_outputs = peft_model.generate(input_ids=peft_encoding.input_ids, generation_config=GenerationConfig(max_new_tokens=256, pad_token_id = peft_tokenizer.eos_token_id, \\\n",
+        "                                                                                                                     eos_token_id = peft_tokenizer.eos_token_id, attention_mask = peft_encoding.attention_mask, \\\n",
+        "                                                                                                                     temperature=0.4, top_p=0.6, repetition_penalty=1.3, num_return_sequences=1,))\n",
+        "            peft_text_output = peft_tokenizer.decode(peft_outputs[0], skip_special_tokens=True)\n",
+        "            return peft_text_output\n",
+        "\n",
+        "        @property\n",
+        "        def _llm_type(self) -> str:\n",
+        "            return \"custom\"\n",
+        "\n",
+        "    llm = CustomLLM()\n",
+        "\n",
+        "    template = \"\"\"Answer the following question truthfully.\n",
+        "    If you don't know the answer, respond 'Sorry, I don't know the answer to this question.'.\n",
+        "    If the question is too complex, respond 'Kindly, consult a psychiatrist for further queries.'.\n",
+        "\n",
+        "    Example Format:\n",
+        "    <HUMAN>: question here\n",
+        "    <ASSISTANT>: answer here\n",
+        "\n",
+        "    Begin!\n",
+        "\n",
+        "    <HUMAN>: {query}\n",
+        "    <ASSISTANT>:\"\"\"\n",
+        "\n",
+        "    prompt = PromptTemplate(template=template, input_variables=[\"query\"])\n",
+        "    llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
+        "\n",
+        "    return llm_chain"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "10h_KVGilk2J"
+      },
+      "outputs": [],
+      "source": [
+        "def user(user_message, history):\n",
+        "  return \"\", history + [[user_message, None]]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QeFE1qZnluMm"
+      },
+      "outputs": [],
+      "source": [
+        "def bot(history):\n",
+        "  if len(history) >= 2:\n",
+        "    query = history[-2][0] + \"\\n\" + history[-2][1] + \"\\nHere, is the next QUESTION: \" + history[-1][0]\n",
+        "  else:\n",
+        "    query = history[-1][0]\n",
+        "\n",
+        "  bot_message = llm_chain.run(query)\n",
+        "  bot_message = post_process_chat(bot_message)\n",
+        "\n",
+        "  history[-1][1] = \"\"\n",
+        "  history[-1][1] += bot_message\n",
+        "  return history"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "aae3uAD5lyXN"
+      },
+      "outputs": [],
+      "source": [
+        "def post_process_chat(bot_message):\n",
+        "  try:\n",
+        "    bot_message = re.findall(r\"<ASSISTANT>:.*?Begin!\", bot_message, re.DOTALL)[1]\n",
+        "  except IndexError:\n",
+        "    pass\n",
+        "\n",
+        "  bot_message = re.split(r'<ASSISTANT>\\:?\\s?', bot_message)[-1].split(\"Begin!\")[0]\n",
+        "\n",
+        "  bot_message = re.sub(r\"^(.*?\\.)(?=\\n|$)\", r\"\\1\", bot_message, flags=re.DOTALL)\n",
+        "  try:\n",
+        "    bot_message = re.search(r\"(.*\\.)\", bot_message, re.DOTALL).group(1)\n",
+        "  except AttributeError:\n",
+        "    pass\n",
+        "\n",
+        "  bot_message = re.sub(r\"\\n\\d.$\", \"\", bot_message)\n",
+        "  bot_message = re.split(r\"(Goodbye|Take care|Best Wishes)\", bot_message, flags=re.IGNORECASE)[0].strip()\n",
+        "  bot_message = bot_message.replace(\"\\n\\n\", \"\\n\")\n",
+        "\n",
+        "  return bot_message"
+      ]
+    },
+    {
+      "cell_type": "code",
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+        "outputId": "671b81ea-4789-4555-e96a-c69797cb6a13"
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+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "34b559e7dcc245d59cf63059f89854e1",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Loading checkpoint shards:   0%|          | 0/8 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "model = \"heliosbrahma/falcon-7b-sharded-bf16-finetuned-mental-health-conversational\"\n",
+        "peft_model, peft_tokenizer = init_model_and_tokenizer(PEFT_MODEL = model)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7v4BVOyxQeik"
+      },
+      "outputs": [],
+      "source": [
+        "with gr.Blocks() as demo:\n",
+        "    gr.HTML(\"\"\"<h1>Welcome to Mental Health Conversational AI</h1>\"\"\")\n",
+        "    gr.Markdown(\n",
+        "        \"\"\"Chatbot specifically designed to provide psychoeducation, offer non-judgemental and empathetic support, self-assessment and monitoring.<br>\n",
+        "        Get instant response for any mental health related queries. If the chatbot seems you need external support, then it will respond appropriately.<br>\"\"\"\n",
+        "    )\n",
+        "\n",
+        "    chatbot = gr.Chatbot()\n",
+        "    query = gr.Textbox(label=\"Type your query here, then press 'enter' and scroll up for response\")\n",
+        "    clear = gr.Button(value=\"Clear Chat History!\")\n",
+        "    clear.style(size=\"sm\")\n",
+        "\n",
+        "    llm_chain = init_llm_chain(peft_model, peft_tokenizer)\n",
+        "\n",
+        "    query.submit(user, [query, chatbot], [query, chatbot], queue=False).then(bot, chatbot, chatbot)\n",
+        "    clear.click(lambda: None, None, chatbot, queue=False)\n",
+        "\n",
+        "demo.queue().launch()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bl3UYt3dUF6H"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
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