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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "2c8fa27a",
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr\n",
"import json\n",
"import re\n",
"from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline\n",
"import torch"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4466940f",
"metadata": {},
"outputs": [],
"source": [
"model_name = \"unsloth/DeepSeek-R1-Distill-Qwen-1.5B\"\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" model_name,\n",
" torch_dtype=torch.float16,\n",
" device_map=\"auto\",\n",
" low_cpu_mem_usage=True\n",
")\n",
"\n",
"chat = pipeline(\n",
" \"text-generation\",\n",
" model=model,\n",
" tokenizer=tokenizer,\n",
" max_length=512,\n",
" temperature=0.7,\n",
" do_sample=True,\n",
" device=0\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eeb02955",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = \"\"\"You are a helpful assistant guiding a user through the Boston Public Schools registration process.\n",
"You are given:\n",
"1. The user's most recent message\n",
"2. The current known registration info (`info`) — provided as a JSON object\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c801c862",
"metadata": {},
"outputs": [],
"source": [
"def extract_response_and_update(text):\n",
" think = re.search(r\"<think>(.*?)</think>\", text, re.DOTALL)\n",
" resp = re.search(r\"<response>(.*?)</response>\", text, re.DOTALL)\n",
" upd = re.search(r\"<update>(.*?)</update>\", text, re.DOTALL)\n",
"\n",
" out_text = resp.group(1).strip() if resp else text\n",
" try:\n",
" update = json.loads(upd.group(1)) if upd else {}\n",
" except json.JSONDecodeError:\n",
" update = {}\n",
" return out_text, update\n",
"\n",
"info = {\n",
" \"location\": None,\n",
" \"school\": None,\n",
" \"child\": {\n",
" \"name\": None,\n",
" \"age\": None,\n",
" \"grade\": None,\n",
" \"special_needs\": None,\n",
" \"transferring\": None\n",
" },\n",
" \"residency_docs\": []\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c3dfb0c4",
"metadata": {},
"outputs": [],
"source": [
"def chat_fn(user_message, chat_history):\n",
" full = system_prompt + \"\\n<|user|>\\n\" + user_message + \"\\n<|assistant|>\"\n",
" raw = chat(full)[0][\"generated_text\"].strip()\n",
" resp_text, update = extract_response_and_update(raw)\n",
"\n",
" def merge(existing, upd):\n",
" for k, v in upd.items():\n",
" if isinstance(v, dict) and k in existing:\n",
" merge(existing[k], v)\n",
" else:\n",
" existing[k] = v\n",
" merge(info, update)\n",
"\n",
" chat_history = chat_history or []\n",
" chat_history.append((user_message, resp_text))\n",
" return chat_history, chat_history"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8b676221",
"metadata": {},
"outputs": [],
"source": [
"demo = gr.ChatInterface(\n",
" fn=chat_fn,\n",
" title=\"Boston School Choice\",\n",
" description=\"Ask me anything about Boston Public Schools registration\",\n",
")\n",
"\n",
"demo.launch(inline=True)"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
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