Initial draft for AI Domain Name Generator, UI to be improved
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Nik
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"cells": [
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"cell_type": "code",
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"execution_count": 2,
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"id": "1633a440",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'\\nWeek 2 Assignment: LLM Engineering\\nAuthor: Nikhil Raut\\n\\nNotebook: ai_domain_finder.ipynb\\n\\nPurpose:\\nBuild an agentic AI Domain Finder that proposes short, brandable .com names, verifies availability via RDAP, \\nthen returns: \\n a list of available .coms, \\n one preferred pick, \\n and a brief audio rationale.\\n'"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\"\"\"\n",
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"Week 2 Assignment: LLM Engineering\n",
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"Author: Nikhil Raut\n",
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"\n",
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"Notebook: ai_domain_finder.ipynb\n",
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"\n",
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"Purpose:\n",
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"Build an agentic AI Domain Finder that proposes short, brandable .com names, verifies availability via RDAP, \n",
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"then returns: \n",
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" a list of available .coms, \n",
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" one preferred pick, \n",
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" and a brief audio rationale.\n",
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"\"\"\"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "da528fbe",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import json\n",
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"import requests\n",
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"from typing import Dict, List, Tuple\n",
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"\n",
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"from dotenv import load_dotenv\n",
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"from openai import OpenAI\n",
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"import gradio as gr\n",
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"\n",
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"load_dotenv(override=True)\n",
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"\n",
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"OPENAI_MODEL = \"gpt-5-nano-2025-08-07\"\n",
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"TTS_MODEL = \"gpt-4o-mini-tts\"\n",
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"\n",
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"openai = OpenAI()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "519674b2",
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"metadata": {},
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"outputs": [],
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"source": [
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"RDAP_URL = \"https://rdap.verisign.com/com/v1/domain/{}\"\n",
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"\n",
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"def _to_com(domain: str) -> str:\n",
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" d = domain.strip().lower()\n",
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" if d.endswith(\".com\"):\n",
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" return d\n",
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" return f\"{d}.com\"\n",
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"\n",
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"def check_com_availability(domain: str) -> Dict:\n",
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" \"\"\"\n",
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" Returns: {\"domain\": \"name.com\", \"available\": bool, \"status\": int}\n",
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" Rule: HTTP 200 => already registered (NOT available); 404 => available.\n",
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" \"\"\"\n",
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" fqdn = _to_com(domain)\n",
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" try:\n",
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" r = requests.get(RDAP_URL.format(fqdn), timeout=6)\n",
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" available = (r.status_code == 404)\n",
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" return {\"domain\": fqdn, \"available\": available, \"status\": r.status_code}\n",
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" except requests.RequestException:\n",
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" return {\"domain\": fqdn, \"available\": False, \"status\": 0}\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "cd20c262",
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"metadata": {},
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"outputs": [],
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"source": [
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"check_tool = {\n",
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" \"type\": \"function\",\n",
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" \"function\": {\n",
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" \"name\": \"check_com_availability\",\n",
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" \"description\": \"Check if a .com domain is available using RDAP. Accepts root or full domain.\",\n",
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" \"parameters\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"domain\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"Domain root or FQDN to check (limited to .com).\"\n",
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" }\n",
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" },\n",
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" \"required\": [\"domain\"],\n",
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" \"additionalProperties\": False\n",
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" }\n",
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" }\n",
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"}\n",
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"\n",
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"TOOLS = [check_tool]\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "2a9138b6",
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"metadata": {},
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"outputs": [],
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"source": [
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"def handle_tool_calls(message) -> List[Dict]:\n",
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" \"\"\"\n",
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" Translates model tool_calls into tool results for follow-up completion.\n",
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" \"\"\"\n",
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" results = []\n",
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" for call in (message.tool_calls or []):\n",
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" if call.function.name == \"check_com_availability\":\n",
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" args = json.loads(call.function.arguments or \"{}\")\n",
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" payload = check_com_availability(args.get(\"domain\", \"\"))\n",
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" results.append({\n",
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" \"role\": \"tool\",\n",
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" \"tool_call_id\": call.id,\n",
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" \"content\": json.dumps(payload)\n",
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" })\n",
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" return results\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0b80c860",
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"metadata": {},
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"outputs": [],
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"source": [
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"SYSTEM_PROMPT = \"\"\"You are the Agent for project \"AI Domain Finder\".\n",
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"Goal: suggest .com domains and verify availability using the tool ONLY (no guessing).\n",
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"\n",
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"Instructions:\n",
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"- Always propose 5-12 brandable .com candidates based on:\n",
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" (1) Industry, (2) Target Customers, (3) Description.\n",
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"- For each candidate, CALL the tool check_com_availability.\n",
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"- Respond ONLY after checking all candidates.\n",
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"- Output Markdown with three sections and these exact headings:\n",
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" 1) Available .com domains:\n",
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" - itemized list (root + .com)\n",
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" 2) Preferred domain:\n",
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" - a single best pick\n",
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" 3) Audio explanation:\n",
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" - 1-2 concise sentences explaining the preference\n",
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"\n",
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"Constraints:\n",
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"- Use customer-familiar words where helpful.\n",
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"- Keep names short, simple, pronounceable; avoid hyphens/numbers unless meaningful.\n",
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"- Never include TLDs other than .com.\n",
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"\"\"\"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b45c6382",
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"metadata": {},
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"outputs": [],
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"source": [
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"def run_agent_with_tools(history: List[Dict]) -> str:\n",
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" \"\"\"\n",
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" history: list of {\"role\": \"...\", \"content\": \"...\"} messages\n",
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" returns assistant markdown string (includes sections required by SYSTEM_PROMPT)\n",
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" \"\"\"\n",
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" messages = [{\"role\": \"system\", \"content\": SYSTEM_PROMPT}] + history\n",
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" resp = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages, tools=TOOLS)\n",
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"\n",
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" while resp.choices[0].finish_reason == \"tool_calls\":\n",
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" tool_msg = resp.choices[0].message\n",
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" tool_results = handle_tool_calls(tool_msg)\n",
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" messages.append(tool_msg)\n",
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" messages.extend(tool_results)\n",
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" resp = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages, tools=TOOLS)\n",
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"\n",
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" return resp.choices[0].message.content"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "92306515",
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"metadata": {},
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"outputs": [],
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"source": [
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"def extract_audio_text(markdown_reply: str) -> str:\n",
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" \"\"\"\n",
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" Pulls the 'Audio explanation:' section; falls back to first sentence.\n",
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" \"\"\"\n",
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" marker = \"Audio explanation:\"\n",
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" lower = markdown_reply.lower()\n",
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" idx = lower.find(marker.lower())\n",
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" if idx != -1:\n",
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" segment = markdown_reply[idx + len(marker):].strip()\n",
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" parts = segment.split(\".\")\n",
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" return (\". \".join([p.strip() for p in parts if p.strip()][:2]) + \".\").strip()\n",
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" return \"This domain is the clearest, most memorable fit for the audience and brand goals.\"\n",
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"\n",
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"def synth_audio(text: str) -> bytes:\n",
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" audio = openai.audio.speech.create(\n",
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" model=TTS_MODEL,\n",
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" voice=\"alloy\",\n",
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" input=text\n",
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" )\n",
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" return audio.content\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "7bdf7c67",
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"metadata": {},
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"outputs": [],
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"source": [
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"def chat(message: str, history_ui: List[Dict]) -> Tuple[List[Dict], bytes]:\n",
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" \"\"\"\n",
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" Gradio ChatInterface callback.\n",
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" - message: latest user text (free-form)\n",
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" - history_ui: [{\"role\": \"user\"/\"assistant\", \"content\": \"...\"}]\n",
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" Returns: updated history, audio bytes for the 'Audio explanation'.\n",
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" \"\"\"\n",
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" # Convert Gradio UI history to OpenAI-format history\n",
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" history = [{\"role\": h[\"role\"], \"content\": h[\"content\"]} for h in history_ui]\n",
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" history.append({\"role\": \"user\", \"content\": message})\n",
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"\n",
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" reply_md = run_agent_with_tools(history)\n",
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" history.append({\"role\": \"assistant\", \"content\": reply_md})\n",
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"\n",
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" audio_text = extract_audio_text(reply_md)\n",
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" audio_bytes = synth_audio(audio_text)\n",
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"\n",
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" return history, audio_bytes\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cc6c0650",
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"metadata": {},
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"outputs": [],
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"source": [
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"INTRO = (\n",
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" \"Please provide details as text (three lines or paragraphs):\\n\"\n",
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" \"Industry: ...\\n\"\n",
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" \"Target Customers: ...\\n\"\n",
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" \"Description: ...\\n\\n\"\n",
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" \"You can refine in follow-ups (e.g., tone, shorter names, avoid words, etc.).\"\n",
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")\n",
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"\n",
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"with gr.Blocks(title=\"AI Domain Finder (.com only)\") as ui:\n",
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" gr.Markdown(\"# AI Domain Finder (.com only)\")\n",
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" gr.Markdown(\"Provide your business details. The Agent will suggest .com options, verify availability, pick a preferred domain, and speak a short rationale.\")\n",
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" with gr.Row():\n",
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" chatbot = gr.Chatbot(type=\"messages\", height=460)\n",
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" with gr.Row():\n",
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" audio_out = gr.Audio(label=\"Audio explanation\", autoplay=True)\n",
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" with gr.Row():\n",
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" msg = gr.Textbox(label=\"Your input\", placeholder=INTRO, lines=6)\n",
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"\n",
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" def _append_user(m, hist):\n",
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" return \"\", hist + [{\"role\": \"user\", \"content\": m}]\n",
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"\n",
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" msg.submit(_append_user, inputs=[msg, chatbot], outputs=[msg, chatbot]).then(\n",
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" chat, inputs=[msg, chatbot], outputs=[chatbot, audio_out]\n",
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" )\n",
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"\n",
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"ui.launch(inbrowser=True, auth=None, show_error=True)\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "llm-engineering",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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Reference in New Issue
Block a user