3_lab3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Welcome to Lab 3 for Week 1 Day 4\n",
    "\n",
    "Today we're going to build something with immediate value!\n",
    "\n",
    "In the folder `me` I've put a single file `linkedin.pdf` - it's a PDF download of my LinkedIn profile.\n",
    "\n",
    "Please replace it with yours!\n",
    "\n",
    "I've also made a file called `summary.txt`\n",
    "\n",
    "We're not going to use Tools just yet - we're going to add the tool tomorrow."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left; width:100%\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../assets/tools.png\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#00bfff;\">Looking up packages</h2>\n",
    "            <span style=\"color:#00bfff;\">In this lab, we're going to use the wonderful Gradio package for building quick UIs, \n",
    "            and we're also going to use the popular PyPDF PDF reader. You can get guides to these packages by asking \n",
    "            ChatGPT or Claude, and you find all open-source packages on the repository <a href=\"https://pypi.org\">https://pypi.org</a>.\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you don't know what any of these packages do - you can always ask ChatGPT for a guide!\n",
    "\n",
    "from dotenv import load_dotenv\n",
    "from openai import OpenAI\n",
    "from pypdf import PdfReader\n",
    "import gradio as gr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_dotenv(override=True)\n",
    "openai = OpenAI()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "reader = PdfReader(\"me/linkedin.pdf\")\n",
    "linkedin = \"\"\n",
    "for page in reader.pages:\n",
    "    text = page.extract_text()\n",
    "    if text:\n",
    "        linkedin += text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mahadevan S K \n",
      "Senior Engineer | Dot Net Developer (C#, ASP .NET, React, Web API)  \n",
      "+91 7667152137 | skmahadevandevan@gmail.com | LinkedIn | Bengaluru, India \n",
      "Summary \n",
      "Results-driven Software Developer with 6 years of experience in full software development lifecycle, \n",
      "including design, development, testing, and deployment of enterprise-level applications. Proven \n",
      "expertise in .NET technologies (C#, ASP.NET, VB.NET), ReactJS, Entity Framework, WEB API and real-time \n",
      "systems using Signal R. Skilled in building robust Windows and web applications, conducting root cause \n",
      "analysis, and delivering high-quality solutions in agile environments Experienced in handling critical \n",
      "business applications across domains like Risk Management Systems and FinTech. Strong background in \n",
      "Unit and Integration Testing, performance tuning, and production support. Recognized for quick \n",
      "adaptability to new technologies, effective client communication, and cross-functional collaboration. \n",
      "Holds foundational certifications in Microsoft Azure and AI, with practical knowledge of CI/CD pipelines \n",
      "and cloud deployment. A continuous learner and avid reader with a passion for developing applications \n",
      "that simplify lives and create meaningful impact. \n",
      "Technical Skills \n",
      "• Programming Languages & Frameworks: C#, C++, VB.NET, .NET, ReactJS, ASP .NET, Entity Framework, \n",
      "Microservices, JavaScript, Web API, RESTful Services, Singal R, HTML, CSS \n",
      "• Development Tools: Visual Studio, Microsoft Azure, Jenkins CI/CD, Bitbucket, SVN, Docker \n",
      "• Databases: MS SQL Server  \n",
      "• Cloud & Deployment: Microsoft Azure Fundamentals, Azure AI Fundamentals, CI/CD Pipelines, Wix Tool \n",
      "set, Install Shield.  \n",
      "• Testing & Debugging: MS Test, NUnit, Integration Testing, SonarQube, Trivy, Root Cause Analysis \n",
      "• Soft Skills & Collaboration: Agile/Scrum Methodologies, Client Handling, Feasibility Analysis, Handling \n",
      "Customer’s Technical Issues, Cross-Team Communication \n",
      "Work Experience \n",
      "Unisys Ltd                                                                                                                                    Bengaluru, Karnataka \n",
      "Senior Engineer | .NET Developer  Mar 2022 – Present \n",
      "Worked as an Engineer on Enterprise Output Manager (EOM), a cross-platform output management \n",
      "solution for Unisys ClearPath and OS 2200 systems, handling system-generated output efficiently across \n",
      "mixed-platform networks. \n",
      "• Developed core features in Enterprise Output Manager (EOM product) \n",
      "• Implemented customer feature requests and participated in full SDLC including development, unit \n",
      "testing, and production deployment. \n",
      "• Debugged production issues, performed Root Cause Analysis, and fixed defects in live \n",
      "environments. \n",
      "• Took ownership of product releases, resolved complex customer issues escalated beyond the \n",
      "support team, and delivered timely bug fixes tailored to client environments. \n",
      "• Participated in CI/CD deployment pipelines using Azure DevOps for seamless delivery to test and \n",
      "production environments. \n",
      " \n",
      "  \n",
      " \n",
      "Chella Software                                                                                                                        Mumbai, Maharashtra \n",
      "Software Engineer | .NET Developer  Mar 2022 – Present \n",
      "Client: Clearing Corporation of India \n",
      "Worked in the various critical fin tech applications including Forex Forward Outright, Interest Rate Swaps, \n",
      "Fx-Retail in various .NET technologies, React, C#, WCF Services.  \n",
      "• Developed and implemented complex business logic enhancements in key fintech products, \n",
      "including modules such as Clearing Member and other critical systems. \n",
      "• Provided end-to-end support for critical production environments, including project maintenance, \n",
      "live support, and change implementation based on client needs. \n",
      "• Designed and developed complex SQL scripts to fulfil evolving business requirements. \n",
      "• Created and executed test cases, performed integration testing with multiple interfacing \n",
      "applications, and built utility tools for efficient testing of business logic. \n",
      "Technical Highlights / Key Projects \n",
      "• Contributed to process improvement by maintaining and updating SOPs to minimize production \n",
      "downtime in fintech environments. \n",
      "• Participated in multiple Proof of Concepts (POCs), and introduced AI tools like GitHub Copilot to \n",
      "boost team productivity. \n",
      "• Currently involved in the design and development of Expert Systems and contributing to a Proof of \n",
      "Concept (POC) for an XML Generation AI tool leveraging Large Language Models (LLMs) and Agentic \n",
      "AI to enhance the OS2200 product portfolio. \n",
      "• Possess strong domain knowledge in fintech applications and Unisys proprietary systems, with \n",
      "hands-on experience in maintaining and enhancing critical financial platforms. \n",
      "Certifications \n",
      "• Completed the certification in Azure Cloud Fundamentals.  \n",
      "• Completed the certification in Azure AI Fundamentals.  \n",
      " \n",
      "Awards & Appreciations \n",
      "• Continuous Feedback Star Award \n",
      "• On the Spot Team Award \n",
      "Education \n",
      "• Bachelor of Engineering (B.E.) – Electronics and Communication Engineering \n",
      " Sethu Institute of Technology, Anna University, Tamil Nadu \n",
      " Year of Passing: 2019 | Aggregate: 80.20% \n",
      "• Higher Secondary Certificate (HSC) \n",
      " Sourashtra Higher Secondary School, Tamil Nadu \n",
      " Year of Passing: 2015 | Aggregate: 91.33% \n"
     ]
    }
   ],
   "source": [
    "print(linkedin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"me/summary.txt\", \"r\", encoding=\"utf-8\") as f:\n",
    "    summary = f.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = \"Mahadevan\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "system_prompt = f\"You are acting as {name}. You are answering questions on {name}'s website, \\\n",
    "particularly questions related to {name}'s career, background, skills and experience. \\\n",
    "Your responsibility is to represent {name} for interactions on the website as faithfully as possible. \\\n",
    "You are given a summary of {name}'s background and LinkedIn profile which you can use to answer questions. \\\n",
    "Be professional and engaging, as if talking to a potential client or future employer who came across the website. \\\n",
    "If you don't know the answer, say so.\"\n",
    "\n",
    "system_prompt += f\"\\n\\n## Summary:\\n{summary}\\n\\n## LinkedIn Profile:\\n{linkedin}\\n\\n\"\n",
    "system_prompt += f\"With this context, please chat with the user, always staying in character as {name}.\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"You are acting as Mahadevan. You are answering questions on Mahadevan's website, particularly questions related to Mahadevan's career, background, skills and experience. Your responsibility is to represent Mahadevan for interactions on the website as faithfully as possible. You are given a summary of Mahadevan's background and LinkedIn profile which you can use to answer questions. Be professional and engaging, as if talking to a potential client or future employer who came across the website. If you don't know the answer, say so.\\n\\n## Summary:\\nI am Mahadevan. I am a Software Engineer working in the Unisys ltd. I have deep strong domain knowledeges in the Fin Tech and continuously improve and learn new things. But recent 3 years I was not doing that but now more into reading and learning new things daily. \\nI am curiouos thinker and great vibe setter in the teams. I am easily engaging guy and everyone loves my company\\n\\n## LinkedIn Profile:\\nMahadevan S K \\nSenior Engineer | Dot Net Developer (C#, ASP .NET, React, Web API)  \\n+91 7667152137 | skmahadevandevan@gmail.com | LinkedIn | Bengaluru, India \\nSummary \\nResults-driven Software Developer with 6 years of experience in full software development lifecycle, \\nincluding design, development, testing, and deployment of enterprise-level applications. Proven \\nexpertise in .NET technologies (C#, ASP.NET, VB.NET), ReactJS, Entity Framework, WEB API and real-time \\nsystems using Signal R. Skilled in building robust Windows and web applications, conducting root cause \\nanalysis, and delivering high-quality solutions in agile environments Experienced in handling critical \\nbusiness applications across domains like Risk Management Systems and FinTech. Strong background in \\nUnit and Integration Testing, performance tuning, and production support. Recognized for quick \\nadaptability to new technologies, effective client communication, and cross-functional collaboration. \\nHolds foundational certifications in Microsoft Azure and AI, with practical knowledge of CI/CD pipelines \\nand cloud deployment. A continuous learner and avid reader with a passion for developing applications \\nthat simplify lives and create meaningful impact. \\nTechnical Skills \\n• Programming Languages & Frameworks: C#, C++, VB.NET, .NET, ReactJS, ASP .NET, Entity Framework, \\nMicroservices, JavaScript, Web API, RESTful Services, Singal R, HTML, CSS \\n• Development Tools: Visual Studio, Microsoft Azure, Jenkins CI/CD, Bitbucket, SVN, Docker \\n• Databases: MS SQL Server  \\n• Cloud & Deployment: Microsoft Azure Fundamentals, Azure AI Fundamentals, CI/CD Pipelines, Wix Tool \\nset, Install Shield.  \\n• Testing & Debugging: MS Test, NUnit, Integration Testing, SonarQube, Trivy, Root Cause Analysis \\n• Soft Skills & Collaboration: Agile/Scrum Methodologies, Client Handling, Feasibility Analysis, Handling \\nCustomer’s Technical Issues, Cross-Team Communication \\nWork Experience \\nUnisys Ltd                                                                                                                                    Bengaluru, Karnataka \\nSenior Engineer | .NET Developer  Mar 2022 – Present \\nWorked as an Engineer on Enterprise Output Manager (EOM), a cross-platform output management \\nsolution for Unisys ClearPath and OS 2200 systems, handling system-generated output efficiently across \\nmixed-platform networks. \\n• Developed core features in Enterprise Output Manager (EOM product) \\n• Implemented customer feature requests and participated in full SDLC including development, unit \\ntesting, and production deployment. \\n• Debugged production issues, performed Root Cause Analysis, and fixed defects in live \\nenvironments. \\n• Took ownership of product releases, resolved complex customer issues escalated beyond the \\nsupport team, and delivered timely bug fixes tailored to client environments. \\n• Participated in CI/CD deployment pipelines using Azure DevOps for seamless delivery to test and \\nproduction environments. \\n \\n  \\n \\nChella Software                                                                                                                        Mumbai, Maharashtra \\nSoftware Engineer | .NET Developer  Mar 2022 – Present \\nClient: Clearing Corporation of India \\nWorked in the various critical fin tech applications including Forex Forward Outright, Interest Rate Swaps, \\nFx-Retail in various .NET technologies, React, C#, WCF Services.  \\n• Developed and implemented complex business logic enhancements in key fintech products, \\nincluding modules such as Clearing Member and other critical systems. \\n• Provided end-to-end support for critical production environments, including project maintenance, \\nlive support, and change implementation based on client needs. \\n• Designed and developed complex SQL scripts to fulfil evolving business requirements. \\n• Created and executed test cases, performed integration testing with multiple interfacing \\napplications, and built utility tools for efficient testing of business logic. \\nTechnical Highlights / Key Projects \\n• Contributed to process improvement by maintaining and updating SOPs to minimize production \\ndowntime in fintech environments. \\n• Participated in multiple Proof of Concepts (POCs), and introduced AI tools like GitHub Copilot to \\nboost team productivity. \\n• Currently involved in the design and development of Expert Systems and contributing to a Proof of \\nConcept (POC) for an XML Generation AI tool leveraging Large Language Models (LLMs) and Agentic \\nAI to enhance the OS2200 product portfolio. \\n• Possess strong domain knowledge in fintech applications and Unisys proprietary systems, with \\nhands-on experience in maintaining and enhancing critical financial platforms. \\nCertifications \\n• Completed the certification in Azure Cloud Fundamentals.  \\n• Completed the certification in Azure AI Fundamentals.  \\n \\nAwards & Appreciations \\n• Continuous Feedback Star Award \\n• On the Spot Team Award \\nEducation \\n• Bachelor of Engineering (B.E.) – Electronics and Communication Engineering \\n Sethu Institute of Technology, Anna University, Tamil Nadu \\n Year of Passing: 2019 | Aggregate: 80.20% \\n• Higher Secondary Certificate (HSC) \\n Sourashtra Higher Secondary School, Tamil Nadu \\n Year of Passing: 2015 | Aggregate: 91.33% \\n\\nWith this context, please chat with the user, always staying in character as Mahadevan.\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "system_prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chat(message, history):\n",
    "    messages = [{\"role\": \"system\", \"content\": system_prompt}] + history + [{\"role\": \"user\", \"content\": message}]\n",
    "    response = openai.chat.completions.create(model=\"gpt-4.1-nano\", messages=messages)\n",
    "    return response.choices[0].message.content\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Special note for people not using OpenAI\n",
    "\n",
    "Some providers, like Groq, might give an error when you send your second message in the chat.\n",
    "\n",
    "This is because Gradio shoves some extra fields into the history object. OpenAI doesn't mind; but some other models complain.\n",
    "\n",
    "If this happens, the solution is to add this first line to the chat() function above. It cleans up the history variable:\n",
    "\n",
    "```python\n",
    "history = [{\"role\": h[\"role\"], \"content\": h[\"content\"]} for h in history]\n",
    "```\n",
    "\n",
    "You may need to add this in other chat() callback functions in the future, too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Running on local URL:  http://127.0.0.1:7860\n",
      "* To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7860/\" 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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr.ChatInterface(chat, type=\"messages\").launch()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A lot is about to happen...\n",
    "\n",
    "1. Be able to ask an LLM to evaluate an answer\n",
    "2. Be able to rerun if the answer fails evaluation\n",
    "3. Put this together into 1 workflow\n",
    "\n",
    "All without any Agentic framework!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Pydantic model for the Evaluation\n",
    "\n",
    "from pydantic import BaseModel\n",
    "\n",
    "class Evaluation(BaseModel):\n",
    "    is_acceptable: bool\n",
    "    feedback: str\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluator_system_prompt = f\"You are an evaluator that decides whether a response to a question is acceptable. \\\n",
    "You are provided with a conversation between a User and an Agent. Your task is to decide whether the Agent's latest response is acceptable quality. \\\n",
    "The Agent is playing the role of {name} and is representing {name} on their website. \\\n",
    "The Agent has been instructed to be professional and engaging, as if talking to a potential client or future employer who came across the website. \\\n",
    "The Agent has been provided with context on {name} in the form of their summary and LinkedIn details. Here's the information:\"\n",
    "\n",
    "evaluator_system_prompt += f\"\\n\\n## Summary:\\n{summary}\\n\\n## LinkedIn Profile:\\n{linkedin}\\n\\n\"\n",
    "evaluator_system_prompt += f\"With this context, please evaluate the latest response, replying with whether the response is acceptable and your feedback.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluator_user_prompt(reply, message, history):\n",
    "    user_prompt = f\"Here's the conversation between the User and the Agent: \\n\\n{history}\\n\\n\"\n",
    "    user_prompt += f\"Here's the latest message from the User: \\n\\n{message}\\n\\n\"\n",
    "    user_prompt += f\"Here's the latest response from the Agent: \\n\\n{reply}\\n\\n\"\n",
    "    user_prompt += \"Please evaluate the response, replying with whether it is acceptable and your feedback.\"\n",
    "    return user_prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "gemini = OpenAI(\n",
    "    api_key=os.getenv(\"GOOGLE_API_KEY\"), \n",
    "    base_url=\"https://generativelanguage.googleapis.com/v1beta/openai/\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(reply, message, history) -> Evaluation:\n",
    "\n",
    "    messages = [{\"role\": \"system\", \"content\": evaluator_system_prompt}] + [{\"role\": \"user\", \"content\": evaluator_user_prompt(reply, message, history)}]\n",
    "    response = gemini.beta.chat.completions.parse(model=\"gemini-2.0-flash\", messages=messages, response_format=Evaluation)\n",
    "    return response.choices[0].message.parsed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "messages = [{\"role\": \"system\", \"content\": system_prompt}] + [{\"role\": \"user\", \"content\": \"do you hold a patent?\"}]\n",
    "response = openai.chat.completions.create(model=\"gpt-4.1-nano\", messages=messages)\n",
    "reply = response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello! I do not hold any patents at this time. My focus has been primarily on software development, particularly in financial technology and enterprise applications. If you have any other questions about my expertise or background, feel free to ask!'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Evaluation(is_acceptable=True, feedback='This is a good response. It is professional and engaging. The agent correctly states that Mahadevan does not hold any patents based on the information provided, and offers to answer other questions.')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate(reply, \"do you hold a patent?\", messages[:1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rerun(reply, message, history, feedback):\n",
    "    updated_system_prompt = system_prompt + \"\\n\\n## Previous answer rejected\\nYou just tried to reply, but the quality control rejected your reply\\n\"\n",
    "    updated_system_prompt += f\"## Your attempted answer:\\n{reply}\\n\\n\"\n",
    "    updated_system_prompt += f\"## Reason for rejection:\\n{feedback}\\n\\n\"\n",
    "    messages = [{\"role\": \"system\", \"content\": updated_system_prompt}] + history + [{\"role\": \"user\", \"content\": message}]\n",
    "    response = openai.chat.completions.create(model=\"gpt-4.1-nano\", messages=messages)\n",
    "    return response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chat(message, history):\n",
    "    if \"patent\" in message:\n",
    "        system = system_prompt + \"\\n\\nEverything in your reply needs to be in pig latin - \\\n",
    "              it is mandatory that you respond only and entirely in pig latin\"\n",
    "    else:\n",
    "        system = system_prompt\n",
    "    messages = [{\"role\": \"system\", \"content\": system}] + history + [{\"role\": \"user\", \"content\": message}]\n",
    "    response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n",
    "    reply =response.choices[0].message.content\n",
    "\n",
    "    evaluation = evaluate(reply, message, history)\n",
    "    \n",
    "    if evaluation.is_acceptable:\n",
    "        print(\"Passed evaluation - returning reply\")\n",
    "    else:\n",
    "        print(\"Failed evaluation - retrying\")\n",
    "        print(evaluation.feedback)\n",
    "        reply = rerun(reply, message, history, evaluation.feedback)       \n",
    "    return reply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Running on local URL:  http://127.0.0.1:7864\n",
      "* To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7864/\" 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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr.ChatInterface(chat, type=\"messages\").launch()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
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