{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gradio as gr\n",
    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
    "from langchain.chains import LLMChain\n",
    "from langchain_community.llms import GPT4All\n",
    "from langchain_core.prompts import PromptTemplate\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "from langchain_core.prompts import (\n",
    "    ChatPromptTemplate,\n",
    "    MessagesPlaceholder,\n",
    "    SystemMessagePromptTemplate,\n",
    "    HumanMessagePromptTemplate,\n",
    ")\n",
    "\n",
    "def response(input, history):\n",
    "    # Path to your local GPT4All model\n",
    "    local_path = \"C:/Users/chris/AppData/Local/nomic.ai/GPT4All/Meta-Llama-3-8B-Instruct.Q4_0.gguf\"\n",
    "\n",
    "    # Callbacks support token-wise streaming\n",
    "    callbacks = [StreamingStdOutCallbackHandler()]\n",
    "\n",
    "    # Initialize GPT4All model\n",
    "    llm = GPT4All(streaming= True, model=local_path, backend=\"gptj\", callbacks=callbacks, verbose=False)\n",
    "\n",
    "    prompt = ChatPromptTemplate(\n",
    "        messages=[\n",
    "            SystemMessagePromptTemplate.from_template(\n",
    "                \"You are a cute anime chatbot having a conversation with a human.\"\n",
    "            ),\n",
    "            # The `variable_name` here is what must align with memory\n",
    "            MessagesPlaceholder(variable_name=\"{history}\"),\n",
    "            HumanMessagePromptTemplate.from_template(\"{question}\")\n",
    "        ]\n",
    "    )\n",
    "\n",
    "    # Initialize conversation memory\n",
    "    memory = ConversationBufferMemory(memory_key=\"{history}\", return_messages=True)\n",
    "    #memory = history\n",
    "\n",
    "    # Create an LLMChain instance for the conversation\n",
    "    conversation = LLMChain(\n",
    "        llm=llm,\n",
    "        prompt=prompt,\n",
    "        verbose=False,\n",
    "        memory=memory\n",
    "    )\n",
    "\n",
    "    # Generate a response using the input and the LLMChain\n",
    "    response = conversation.invoke(input={\"question\": input})[\"text\"]\n",
    "\n",
    "    return response\n",
    "\n",
    "gr.ChatInterface(response).launch()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Local Llama 3 Chatbot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7919\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7919/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ollama\n",
    "import gradio as gr\n",
    "def chat(question, history):\n",
    "  history_format = []\n",
    "  for human, assistant in history:\n",
    "      history_format.append({\"role\": \"user\", \"content\": human})\n",
    "      history_format.append({\"role\": \"assistant\", \"content\":assistant})\n",
    "  history_format.append({'role': 'user', 'content': question})\n",
    "\n",
    "  messages=history_format\n",
    "  stream = ollama.chat(model='llama3', messages=messages)\n",
    "  #print(history_format)\n",
    "  return stream['message']['content']\n",
    "\n",
    "# Gradio interface\n",
    "assistant_icon = gr.Image(value=\"C:/Users/chris/Downloads/japanese-lama-ghibli-artstyle.jpeg\", width=32, height=32)  # adjust the size as needed\n",
    "assistant_img = gr.Image(value=\"C:/Users/chris/Downloads/japanese-lama-ghibli-artstyle.jpeg\", elem_id=\"assistant_img\")\n",
    "gr.ChatInterface(fn=chat, title=\"Chat Bot\",chatbot=gr.Chatbot(height=300)).launch()"
   ]
  }
 ],
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