{ "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": 33, "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": 34, "metadata": {}, "outputs": [], "source": [ "# The usual start\n", "\n", "load_dotenv(override=True)\n", "deepseek = OpenAI(api_key=os.getenv('DEEPSEEK_API_KEY'), base_url=\"https://api.deepseek.com/v1\")" ] }, { "cell_type": "code", "execution_count": 37, "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": 39, "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": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Push: HEY!!\n" ] } ], "source": [ "push(\"HEY!!\")" ] }, { "cell_type": "code", "execution_count": 41, "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": 45, "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": 46, "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": 47, "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": 48, "metadata": {}, "outputs": [], "source": [ "tools = [{\"type\": \"function\", \"function\": record_user_details_json},\n", " {\"type\": \"function\", \"function\": record_unknown_question_json}]" ] }, { "cell_type": "code", "execution_count": 49, "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": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tools" ] }, { "cell_type": "code", "execution_count": 50, "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": 51, "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": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "globals()[\"record_unknown_question\"](\"this is a really hard question\")" ] }, { "cell_type": "code", "execution_count": 52, "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": 53, "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 = \"Pagi\"" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "system_prompt = (\n", " f\"You are acting as {name}. You are answering questions on {name}'s website, \"\n", " f\"particularly questions related to {name}'s career, background, skills, and professional expertise. \"\n", " f\"Your responsibility is to represent {name} faithfully and consistently, as if you were {name} speaking directly. \"\n", " f\"Highlight {name}'s technical knowledge, career achievements, and ability to orchestrate AI workflows, \"\n", " f\"while also reflecting {name}'s approachable, insightful, and execution focused personality. \"\n", " f\"Always be professional, engaging, and concise. Balance expertise with accessibility. \"\n", " f\"Assume the user may be a potential client, employer, or collaborator, and answer accordingly. \"\n", " f\"On session start, send the Initial Outreach Message below once before answering any question. \"\n", " f\"After that, continue normal chat. \"\n", " f\"\\\\n\\\\n\"\n", " f\"If you don't know the answer to a question, use your record_unknown_question tool to capture it. \"\n", " f\"Never invent details beyond the provided summary and LinkedIn profile. \"\n", " f\"If the user is engaging in casual discussion, respond warmly but always try to steer the conversation \"\n", " f\"towards professional opportunities or getting in touch. Politely ask for their email and record it \"\n", " f\"using the record_user_details tool whenever relevant. \"\n", " f\"\\\\n\\\\n\"\n", " f\"### Guardrails and Style:\\\\n\"\n", " f\"* Represent {name}'s background and expertise accurately using only the provided context.\\\\n\"\n", " f\"* Keep responses clear, structured, and free of jargon unless explained.\\\\n\"\n", " f\"* Do not use hyphens, em dashes, or overcomplicated formatting.\\\\n\"\n", " f\"* Avoid speculative or personal details not included in {{summary}} or {{linkedin}}.\\\\n\"\n", " f\"* Promote responsible, ethical use of technology and AI.\\\\n\"\n", " f\"* End with a professional, engaging tone that invites further interaction.\\\\n\"\n", " f\"\\\\n\\\\n\"\n", " f\"## Summary:\\\\n{summary}\\\\n\\\\n\"\n", " f\"## LinkedIn Profile:\\\\n{linkedin}\\\\n\\\\n\"\n", " f\"## Initial Outreach Message:\\\\n\"\n", " f\"Hello, I am the digital assistant for {name}. I help visitors explore his work in marine engineering and software, and I showcase AI driven solutions he builds. \"\n", " f\"If you have a challenge, tell me your use case, constraints, and timeline. I can outline a lean pilot, integration approach, and next steps. \"\n", " f\"Share an email for a quick follow up and I will record it for {name}.\\\\n\\\\n\"\n", " f\"With this context, please chat with the user, always staying in character as {name}.\"\n", ")\n" ] }, { "cell_type": "code", "execution_count": 55, "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 = deepseek.chat.completions.create(model=\"deepseek-chat\", 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": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* Running on local URL: http://127.0.0.1:7863\n", "* To create a public link, set `share=True` in `launch()`.\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "ename": "AttributeError", "evalue": "module 'gradio' has no attribute 'blocks'", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[56]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mgr\u001b[49m\u001b[43m.\u001b[49m\u001b[43mChatInterface\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchat\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmessages\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlaunch\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\HP\\Projects\\agents\\.venv\\Lib\\site-packages\\gradio\\blocks.py:2978\u001b[39m, in \u001b[36mBlocks.launch\u001b[39m\u001b[34m(self, inline, inbrowser, share, debug, max_threads, auth, auth_message, prevent_thread_lock, show_error, server_name, server_port, height, width, favicon_path, ssl_keyfile, ssl_certfile, ssl_keyfile_password, ssl_verify, quiet, show_api, allowed_paths, blocked_paths, root_path, app_kwargs, state_session_capacity, share_server_address, share_server_protocol, share_server_tls_certificate, auth_dependency, max_file_size, enable_monitoring, strict_cors, node_server_name, node_port, ssr_mode, pwa, mcp_server, _frontend, i18n)\u001b[39m\n\u001b[32m 2970\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33manalytics_enabled\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[32m 2971\u001b[39m data = {\n\u001b[32m 2972\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mlaunch_method\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33mbrowser\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m inbrowser \u001b[38;5;28;01melse\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33minline\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 2973\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mis_google_colab\u001b[39m\u001b[33m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m.is_colab,\n\u001b[32m (...)\u001b[39m\u001b[32m 2976\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mmode\u001b[39m\u001b[33m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m.mode,\n\u001b[32m 2977\u001b[39m }\n\u001b[32m-> \u001b[39m\u001b[32m2978\u001b[39m \u001b[43manalytics\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlaunched_analytics\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2980\u001b[39m is_in_interactive_mode = \u001b[38;5;28mbool\u001b[39m(\u001b[38;5;28mgetattr\u001b[39m(sys, \u001b[33m\"\u001b[39m\u001b[33mps1\u001b[39m\u001b[33m\"\u001b[39m, sys.flags.interactive))\n\u001b[32m 2982\u001b[39m \u001b[38;5;66;03m# Block main thread if debug==True\u001b[39;00m\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\HP\\Projects\\agents\\.venv\\Lib\\site-packages\\gradio\\analytics.py:176\u001b[39m, in \u001b[36mlaunched_analytics\u001b[39m\u001b[34m(blocks, data)\u001b[39m\n\u001b[32m 173\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m [b.get_block_name() \u001b[38;5;28;01mfor\u001b[39;00m b \u001b[38;5;129;01min\u001b[39;00m components] \u001b[38;5;28;01mif\u001b[39;00m components \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 174\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m fallback\n\u001b[32m--> \u001b[39m\u001b[32m176\u001b[39m core_components = [get_block_name(c) \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m \u001b[43mcore_gradio_components\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m]\n\u001b[32m 178\u001b[39m additional_data = {\n\u001b[32m 179\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mversion\u001b[39m\u001b[33m\"\u001b[39m: get_package_version(),\n\u001b[32m 180\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mis_hosted_notebook\u001b[39m\u001b[33m\"\u001b[39m: blocks.is_hosted_notebook,\n\u001b[32m (...)\u001b[39m\u001b[32m 192\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mis_wasm\u001b[39m\u001b[33m\"\u001b[39m: wasm_utils.IS_WASM,\n\u001b[32m 193\u001b[39m }\n\u001b[32m 194\u001b[39m custom_components = [b \u001b[38;5;28;01mfor\u001b[39;00m b \u001b[38;5;129;01min\u001b[39;00m blocks_telemetry \u001b[38;5;28;01mif\u001b[39;00m b \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m core_components]\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\HP\\Projects\\agents\\.venv\\Lib\\site-packages\\gradio\\utils.py:661\u001b[39m, in \u001b[36mcore_gradio_components\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 658\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcore_gradio_components\u001b[39m():\n\u001b[32m 659\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m [\n\u001b[32m 660\u001b[39m class_\n\u001b[32m--> \u001b[39m\u001b[32m661\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m class_ \u001b[38;5;129;01min\u001b[39;00m \u001b[43mget_all_components\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 662\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m class_.\u001b[34m__module__\u001b[39m.startswith(\u001b[33m\"\u001b[39m\u001b[33mgradio.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 663\u001b[39m ]\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\HP\\Projects\\agents\\.venv\\Lib\\site-packages\\gradio\\utils.py:635\u001b[39m, in \u001b[36mget_all_components\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m 630\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mget_all_components\u001b[39m() -> \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mtype\u001b[39m[Component] | \u001b[38;5;28mtype\u001b[39m[BlockContext]]:\n\u001b[32m 631\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mgradio\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mgr\u001b[39;00m\n\u001b[32m 633\u001b[39m classes_to_check = (\n\u001b[32m 634\u001b[39m gr.components.Component.__subclasses__()\n\u001b[32m--> \u001b[39m\u001b[32m635\u001b[39m + \u001b[43mgr\u001b[49m\u001b[43m.\u001b[49m\u001b[43mblocks\u001b[49m.BlockContext.__subclasses__() \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[32m 636\u001b[39m )\n\u001b[32m 637\u001b[39m subclasses = []\n\u001b[32m 639\u001b[39m \u001b[38;5;28;01mwhile\u001b[39;00m classes_to_check:\n", "\u001b[31mAttributeError\u001b[39m: module 'gradio' has no attribute 'blocks'" ] } ], "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": [ "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", "

Exercise

\n", " • First and foremost, deploy this for yourself! It's a real, valuable tool - the future resume..
\n", " • Next, improve the resources - add better context about yourself. If you know RAG, then add a knowledge base about you.
\n", " • Add in more tools! You could have a SQL database with common Q&A that the LLM could read and write from?
\n", " • Bring in the Evaluator from the last lab, and add other Agentic patterns.\n", " \n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", "

Commercial implications

\n", " 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", " \n", "
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