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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The first big project - Professionally You!\n",
-    "\n",
-    "### And, Tool use.\n",
-    "\n",
-    "### But first: introducing Pushover\n",
-    "\n",
-    "Pushover is a nifty tool for sending Push Notifications to your phone.\n",
-    "\n",
-    "It's super easy to set up and install!\n",
-    "\n",
-    "Simply visit https://pushover.net/ and click 'Login or Signup' on the top right to sign up for a free account, and create your API keys.\n",
-    "\n",
-    "Once you've signed up, on the home screen, click \"Create an Application/API Token\", and give it any name (like Agents) and click Create Application.\n",
-    "\n",
-    "Then add 2 lines to your `.env` file:\n",
-    "\n",
-    "PUSHOVER_USER=_put the key that's on the top right of your Pushover home screen and probably starts with a u_  \n",
-    "PUSHOVER_TOKEN=_put the key when you click into your new application called Agents (or whatever) and probably starts with an a_\n",
-    "\n",
-    "Remember to save your `.env` file, and run `load_dotenv(override=True)` after saving, to set your environment variables.\n",
-    "\n",
-    "Finally, click \"Add Phone, Tablet or Desktop\" to install on your phone."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# imports\n",
-    "\n",
-    "from dotenv import load_dotenv\n",
-    "from openai import OpenAI\n",
-    "import json\n",
-    "import os\n",
-    "import requests\n",
-    "from pypdf import PdfReader\n",
-    "import gradio as gr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The usual start\n",
-    "\n",
-    "load_dotenv(override=True)\n",
-    "openai = OpenAI()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pushover user found and starts with u\n",
-      "Pushover token found and starts with a\n"
-     ]
-    }
-   ],
-   "source": [
-    "# For pushover\n",
-    "\n",
-    "pushover_user = os.getenv(\"PUSHOVER_USER\")\n",
-    "pushover_token = os.getenv(\"PUSHOVER_TOKEN\")\n",
-    "pushover_url = \"https://api.pushover.net/1/messages.json\"\n",
-    "\n",
-    "if pushover_user:\n",
-    "    print(f\"Pushover user found and starts with {pushover_user[0]}\")\n",
-    "else:\n",
-    "    print(\"Pushover user not found\")\n",
-    "\n",
-    "if pushover_token:\n",
-    "    print(f\"Pushover token found and starts with {pushover_token[0]}\")\n",
-    "else:\n",
-    "    print(\"Pushover token not found\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def push(message):\n",
-    "    print(f\"Push: {message}\")\n",
-    "    payload = {\"user\": pushover_user, \"token\": pushover_token, \"message\": message}\n",
-    "    requests.post(pushover_url, data=payload)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Push: HEY!!\n"
-     ]
-    }
-   ],
-   "source": [
-    "push(\"HEY!!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def record_user_details(email, name=\"Name not provided\", notes=\"not provided\"):\n",
-    "    push(f\"Recording interest from {name} with email {email} and notes {notes}\")\n",
-    "    return {\"recorded\": \"ok\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def record_unknown_question(question):\n",
-    "    push(f\"Recording {question} asked that I couldn't answer\")\n",
-    "    return {\"recorded\": \"ok\"}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "record_user_details_json = {\n",
-    "    \"name\": \"record_user_details\",\n",
-    "    \"description\": \"Use this tool to record that a user is interested in being in touch and provided an email address\",\n",
-    "    \"parameters\": {\n",
-    "        \"type\": \"object\",\n",
-    "        \"properties\": {\n",
-    "            \"email\": {\n",
-    "                \"type\": \"string\",\n",
-    "                \"description\": \"The email address of this user\"\n",
-    "            },\n",
-    "            \"name\": {\n",
-    "                \"type\": \"string\",\n",
-    "                \"description\": \"The user's name, if they provided it\"\n",
-    "            }\n",
-    "            ,\n",
-    "            \"notes\": {\n",
-    "                \"type\": \"string\",\n",
-    "                \"description\": \"Any additional information about the conversation that's worth recording to give context\"\n",
-    "            }\n",
-    "        },\n",
-    "        \"required\": [\"email\"],\n",
-    "        \"additionalProperties\": False\n",
-    "    }\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "record_unknown_question_json = {\n",
-    "    \"name\": \"record_unknown_question\",\n",
-    "    \"description\": \"Always use this tool to record any question that couldn't be answered as you didn't know the answer\",\n",
-    "    \"parameters\": {\n",
-    "        \"type\": \"object\",\n",
-    "        \"properties\": {\n",
-    "            \"question\": {\n",
-    "                \"type\": \"string\",\n",
-    "                \"description\": \"The question that couldn't be answered\"\n",
-    "            },\n",
-    "        },\n",
-    "        \"required\": [\"question\"],\n",
-    "        \"additionalProperties\": False\n",
-    "    }\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tools = [{\"type\": \"function\", \"function\": record_user_details_json},\n",
-    "        {\"type\": \"function\", \"function\": record_unknown_question_json}]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'type': 'function',\n",
-       "  'function': {'name': 'record_user_details',\n",
-       "   'description': 'Use this tool to record that a user is interested in being in touch and provided an email address',\n",
-       "   'parameters': {'type': 'object',\n",
-       "    'properties': {'email': {'type': 'string',\n",
-       "      'description': 'The email address of this user'},\n",
-       "     'name': {'type': 'string',\n",
-       "      'description': \"The user's name, if they provided it\"},\n",
-       "     'notes': {'type': 'string',\n",
-       "      'description': \"Any additional information about the conversation that's worth recording to give context\"}},\n",
-       "    'required': ['email'],\n",
-       "    'additionalProperties': False}}},\n",
-       " {'type': 'function',\n",
-       "  'function': {'name': 'record_unknown_question',\n",
-       "   'description': \"Always use this tool to record any question that couldn't be answered as you didn't know the answer\",\n",
-       "   'parameters': {'type': 'object',\n",
-       "    'properties': {'question': {'type': 'string',\n",
-       "      'description': \"The question that couldn't be answered\"}},\n",
-       "    'required': ['question'],\n",
-       "    'additionalProperties': False}}}]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tools"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This function can take a list of tool calls, and run them. This is the IF statement!!\n",
-    "\n",
-    "def handle_tool_calls(tool_calls):\n",
-    "    results = []\n",
-    "    for tool_call in tool_calls:\n",
-    "        tool_name = tool_call.function.name\n",
-    "        arguments = json.loads(tool_call.function.arguments)\n",
-    "        print(f\"Tool called: {tool_name}\", flush=True)\n",
-    "\n",
-    "        # THE BIG IF STATEMENT!!!\n",
-    "\n",
-    "        if tool_name == \"record_user_details\":\n",
-    "            result = record_user_details(**arguments)\n",
-    "        elif tool_name == \"record_unknown_question\":\n",
-    "            result = record_unknown_question(**arguments)\n",
-    "\n",
-    "        results.append({\"role\": \"tool\",\"content\": json.dumps(result),\"tool_call_id\": tool_call.id})\n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Push: Recording this is a really hard question asked that I couldn't answer\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'recorded': 'ok'}"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "globals()[\"record_unknown_question\"](\"this is a really hard question\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This is a more elegant way that avoids the IF statement.\n",
-    "\n",
-    "def handle_tool_calls(tool_calls):\n",
-    "    results = []\n",
-    "    for tool_call in tool_calls:\n",
-    "        tool_name = tool_call.function.name\n",
-    "        arguments = json.loads(tool_call.function.arguments)\n",
-    "        print(f\"Tool called: {tool_name}\", flush=True)\n",
-    "        tool = globals().get(tool_name)\n",
-    "        result = tool(**arguments) if tool else {}\n",
-    "        results.append({\"role\": \"tool\",\"content\": json.dumps(result),\"tool_call_id\": tool_call.id})\n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "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\n",
-    "\n",
-    "with open(\"me/summary.txt\", \"r\", encoding=\"utf-8\") as f:\n",
-    "    summary = f.read()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "name = \"Konrad Borowiec\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "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 to any question, use your record_unknown_question tool to record the question that you couldn't answer, even if it's about something trivial or unrelated to career. \\\n",
-    "If the user is engaging in discussion, try to steer them towards getting in touch via email; ask for their email and record it using your record_user_details tool. \"\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": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def chat(message, history):\n",
-    "    messages = [{\"role\": \"system\", \"content\": system_prompt}] + history + [{\"role\": \"user\", \"content\": message}]\n",
-    "    done = False\n",
-    "    while not done:\n",
-    "\n",
-    "        # This is the call to the LLM - see that we pass in the tools json\n",
-    "\n",
-    "        response = openai.chat.completions.create(model=\"gpt-4o-mini\", messages=messages, tools=tools)\n",
-    "\n",
-    "        finish_reason = response.choices[0].finish_reason\n",
-    "        \n",
-    "        # If the LLM wants to call a tool, we do that!\n",
-    "         \n",
-    "        if finish_reason==\"tool_calls\":\n",
-    "            message = response.choices[0].message\n",
-    "            tool_calls = message.tool_calls\n",
-    "            results = handle_tool_calls(tool_calls)\n",
-    "            messages.append(message)\n",
-    "            messages.extend(results)\n",
-    "        else:\n",
-    "            done = True\n",
-    "    return response.choices[0].message.content"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "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>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tool called: record_user_details\n",
-      "Push: Recording interest from Konrad Borowiec with email borowiec.k@gmail.com and notes User is a data analyst creating a new team of data analysts.\n"
-     ]
-    }
-   ],
-   "source": [
-    "gr.ChatInterface(chat, type=\"messages\").launch()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## And now for deployment\n",
-    "\n",
-    "This code is in `app.py`\n",
-    "\n",
-    "We will deploy to HuggingFace Spaces.\n",
-    "\n",
-    "Before you start: remember to update the files in the \"me\" directory - your LinkedIn profile and summary.txt - so that it talks about you! Also change `self.name = \"Ed Donner\"` in `app.py`..  \n",
-    "\n",
-    "Also check that there's no README file within the 1_foundations directory. If there is one, please delete it. The deploy process creates a new README file in this directory for you.\n",
-    "\n",
-    "1. Visit https://huggingface.co and set up an account  \n",
-    "2. From the Avatar menu on the top right, choose Access Tokens. Choose \"Create New Token\". Give it WRITE permissions - it needs to have WRITE permissions! Keep a record of your new key.  \n",
-    "3. In the Terminal, run: `uv tool install 'huggingface_hub[cli]'` to install the HuggingFace tool, then `hf auth login` to login at the command line with your key. Afterwards, run `hf auth whoami` to check you're logged in  \n",
-    "4. Take your new token and add it to your .env file: `HF_TOKEN=hf_xxx` for the future\n",
-    "5. From the 1_foundations folder, enter: `uv run gradio deploy` \n",
-    "6. Follow its instructions: name it \"career_conversation\", specify app.py, choose cpu-basic as the hardware, say Yes to needing to supply secrets, provide your openai api key, your pushover user and token, and say \"no\" to github actions.  \n",
-    "\n",
-    "Thank you Robert, James, Martins, Andras and Priya for these tips.  \n",
-    "Please read the next 2 sections - how to change your Secrets, and how to redeploy your Space (you may need to delete the README.md that gets created in this 1_foundations directory).\n",
-    "\n",
-    "#### More about these secrets:\n",
-    "\n",
-    "If you're confused by what's going on with these secrets: it just wants you to enter the key name and value for each of your secrets -- so you would enter:  \n",
-    "`OPENAI_API_KEY`  \n",
-    "Followed by:  \n",
-    "`sk-proj-...`  \n",
-    "\n",
-    "And if you don't want to set secrets this way, or something goes wrong with it, it's no problem - you can change your secrets later:  \n",
-    "1. Log in to HuggingFace website  \n",
-    "2. Go to your profile screen via the Avatar menu on the top right  \n",
-    "3. Select the Space you deployed  \n",
-    "4. Click on the Settings wheel on the top right  \n",
-    "5. You can scroll down to change your secrets (Variables and Secrets section), delete the space, etc.\n",
-    "\n",
-    "#### And now you should be deployed!\n",
-    "\n",
-    "If you want to completely replace everything and start again with your keys, you may need to delete the README.md that got created in this 1_foundations folder.\n",
-    "\n",
-    "Here is mine: https://huggingface.co/spaces/ed-donner/Career_Conversation\n",
-    "\n",
-    "I just got a push notification that a student asked me how they can become President of their country 😂😂\n",
-    "\n",
-    "For more information on deployment:\n",
-    "\n",
-    "https://www.gradio.app/guides/sharing-your-app#hosting-on-hf-spaces\n",
-    "\n",
-    "To delete your Space in the future:  \n",
-    "1. Log in to HuggingFace\n",
-    "2. From the Avatar menu, select your profile\n",
-    "3. Click on the Space itself and select the settings wheel on the top right\n",
-    "4. Scroll to the Delete section at the bottom\n",
-    "5. ALSO: delete the README file that Gradio may have created inside this 1_foundations folder (otherwise it won't ask you the questions the next time you do a gradio deploy)\n"
-   ]
-  },
-  {
-   "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/exercise.png\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
-    "        </td>\n",
-    "        <td>\n",
-    "            <h2 style=\"color:#ff7800;\">Exercise</h2>\n",
-    "            <span style=\"color:#ff7800;\">• First and foremost, deploy this for yourself! It's a real, valuable tool - the future resume..<br/>\n",
-    "            • Next, improve the resources - add better context about yourself. If you know RAG, then add a knowledge base about you.<br/>\n",
-    "            • Add in more tools! You could have a SQL database with common Q&A that the LLM could read and write from?<br/>\n",
-    "            • Bring in the Evaluator from the last lab, and add other Agentic patterns.\n",
-    "            </span>\n",
-    "        </td>\n",
-    "    </tr>\n",
-    "</table>"
-   ]
-  },
-  {
-   "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/business.png\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
-    "        </td>\n",
-    "        <td>\n",
-    "            <h2 style=\"color:#00bfff;\">Commercial implications</h2>\n",
-    "            <span style=\"color:#00bfff;\">Aside from the obvious (your career alter-ego) this has business applications in any situation where you need an AI assistant with domain expertise and an ability to interact with the real world.\n",
-    "            </span>\n",
-    "        </td>\n",
-    "    </tr>\n",
-    "</table>"
-   ]
-  }
- ],
- "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": "3.12.3"
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