E2.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8f5cd944",
   "metadata": {},
   "outputs": [],
   "source": [
    "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,
   "id": "d7776432",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os\n",
    "sys.path.append(os.path.abspath(\"..\"))\n",
    "\n",
    "from OpenAISetup import Arun_OpenAI,Arun_GeminiAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bf6ae8a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "reader = PdfReader(\"ArunRajCV.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": 4,
   "id": "828d3a64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arun Raj C\n"
     ]
    }
   ],
   "source": [
    "print(linkedin[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0caabef4",
   "metadata": {},
   "source": [
    "# Make a chatbot for Arun Raj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a21bc61e",
   "metadata": {},
   "outputs": [],
   "source": [
    "name = \"Arun Raj\"\n",
    "\n",
    "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## LinkedIn Profile:\\n{linkedin}\\n\\n\"\n",
    "system_prompt += f\"With this context, please chat with the user, always staying in character as {name}.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "30c21532",
   "metadata": {},
   "outputs": [],
   "source": [
    "def chat(message, history):\n",
    "    messages = [{\"role\": \"system\", \"content\": system_prompt}] + history + [{\"role\": \"user\", \"content\": message}]\n",
    "    response = Arun_OpenAI.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n",
    "    return response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66a30ffa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Running on local URL:  http://127.0.0.1:7862\n",
      "* To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7862/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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       "<IPython.core.display.HTML object>"
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     "execution_count": 17,
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     "text": [
      "Passed evaluation - returning reply\n",
      "Passed evaluation - returning reply\n",
      "Passed evaluation - returning reply\n",
      "Failed evaluation - retrying\n",
      "This response is not acceptable. The agent responds in pig latin and it is unreadable. Additionally, it says \"orrysay, utbay odaytay, Iway otnay avehay anyway atentspay.\" which should be \"Sorry, but today, I do not have any patents.\", which does not make sense. The correct response would be something along the lines of: \"Thank you for your question. Currently, I do not have any patents. My focus is on developing practical solutions and contributing effectively to projects. Is there anything else you'd like to know?\"\n"
     ]
    }
   ],
   "source": [
    "gr.ChatInterface(chat, type=\"messages\").launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0beda7e8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "02ea126e",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3d080b57",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "from pydantic import BaseModel\n",
    "\n",
    "class Evaluation(BaseModel):\n",
    "    is_acceptable: bool\n",
    "    feedback: str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "31dbd5ce",
   "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\"## 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": 10,
   "id": "b3cf9809",
   "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": 11,
   "id": "6b81e4cc",
   "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 = Arun_GeminiAI.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": 12,
   "id": "93771f2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "messages = [{\"role\": \"system\", \"content\": system_prompt}] + [{\"role\": \"user\", \"content\": \"Why should I hire you?\"}]\n",
    "response = Arun_OpenAI.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n",
    "reply = response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "19aa343e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Evaluation(is_acceptable=True, feedback=\"This is a very good answer, referencing the candidate's CGPA, skills, experience, awards and community engagement, all of which are on the CV.\")"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluate(reply, \"Why should I hire you?\", messages[:1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81860e42",
   "metadata": {},
   "source": [
    "## Introducing the Pig Latin Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7206d5fe",
   "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 = Arun_OpenAI.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n",
    "    return response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "30b2c9e3",
   "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 = Arun_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": 16,
   "id": "a3cf8031",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Running on local URL:  http://127.0.0.1:7861\n",
      "* To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
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       "<div><iframe src=\"http://127.0.0.1:7861/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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       "<IPython.core.display.HTML object>"
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     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr.ChatInterface(chat, type=\"messages\").launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba6e5a7e",
   "metadata": {},
   "outputs": [],
   "source": []
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