Delete 4_lab4.ipynb

4_lab4.ipynb

@@ -1,445 +0,0 @@
-{
- "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 sign up for a free account, and create your API keys.\n",
-    "\n",
-    "As student Ron pointed out (thank you Ron!) there are actually 2 tokens to create in Pushover:  \n",
-    "1. The User token which you get from the home page of Pushover\n",
-    "2. The Application token which you get by going to https://pushover.net/apps/build and creating an app   \n",
-    "\n",
-    "(This is so you could choose to organize your push notifications into different apps in the future.)\n",
-    "\n",
-    "\n",
-    "Add to your `.env` file:\n",
-    "```\n",
-    "PUSHOVER_USER=put_your_user_token_here\n",
-    "PUSHOVER_TOKEN=put_the_application_level_token_here\n",
-    "```\n",
-    "\n",
-    "And install the Pushover app on your phone."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "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": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The usual start\n",
-    "\n",
-    "load_dotenv(override=True)\n",
-    "openai = OpenAI()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "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\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "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": null,
-   "metadata": {},
-   "outputs": [],
-   "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": 4,
-   "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": 5,
-   "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": 6,
-   "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": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tools = [{\"type\": \"function\", \"function\": record_user_details_json},\n",
-    "        {\"type\": \"function\", \"function\": record_unknown_question_json}]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "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": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "globals()[\"record_unknown_question\"](\"this is a really hard question\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "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": 4,
-   "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()\n",
-    "\n",
-    "name = \"Ed Donner\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "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": 28,
-   "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": null,
-   "metadata": {},
-   "outputs": [],
-   "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. Thank you student Robert M for improving these instructions.\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!  \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.\n",
-    "3. Take this token and add it to your .env file: `HF_TOKEN=hf_xxx` and see note below if this token doesn't seem to get picked up during deployment  \n",
-    "4. From the 1_foundations folder, enter: `uv run gradio deploy` and if for some reason this still wants you to enter your HF token, then interrupt it with ctrl+c and run this instead: `uv run dotenv -f ../.env run -- uv run gradio deploy` which forces your keys to all be set as environment variables   \n",
-    "5. 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",
-    "#### Extra note about the HuggingFace token\n",
-    "\n",
-    "A couple of students have mentioned the HuggingFace doesn't detect their token, even though it's in the .env file. Here are things to try:   \n",
-    "1. Restart Cursor   \n",
-    "2. Rerun load_dotenv(override=True) and use a new terminal (the + button on the top right of the Terminal)   \n",
-    "3. In the Terminal, run this before the gradio deploy: `$env:HF_TOKEN = \"hf_XXXX\"`  \n",
-    "Thank you James and Martins for these tips.  \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, delete the space, etc.\n",
-    "\n",
-    "#### And now you should be deployed!\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": ".venv",
-   "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": "3.12.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}