langchain-app.py

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7863\n",
      "Running on public URL: https://cf43e4871c9bab7308.gradio.live\n",
      "\n",
      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co./spaces)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"https://cf43e4871c9bab7308.gradio.live\" 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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import gradio as gr\n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "_ = load_dotenv(find_dotenv())\n",
    "\n",
    "from langchain.chains import ConversationChain\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "\n",
    "llm = ChatOpenAI(temperature=0.0)\n",
    "memory = ConversationBufferMemory()\n",
    "conversion = ConversationChain(\n",
    "    llm=llm,\n",
    "    memory=memory,\n",
    "    verbose=False\n",
    ")\n",
    "\n",
    "def takeinput(name):\n",
    "    output_str = conversion.predict(input=name)\n",
    "    return output_str\n",
    "\n",
    "demo = gr.Interface(\n",
    "        fn=takeinput,\n",
    "        inputs=[\"text\"],\n",
    "        outputs=[\"text\"]\n",
    ")\n",
    "\n",
    "demo.launch(share=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}