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Added a Smart Shopping Assistant with fine-tuned pricer model. #2065

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Added a Smart Shopping Assistant with fine-tuned pricer model. #2065

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296 changes: 296 additions & 0 deletions week6/community-contributions/norbert-wakanda/smart_shopping_assistant.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "4f658b25",
"metadata": {},
"source": [
"# 🛒 Smart Shopping Assistant\n",
"1. An AI agent where users paste a product description.\n",
"2. Estimates a fair price using a fine-tuned pricing model.\n",
"3. Finds real listings via a web search tool.\n",
"Compares listing prices with the estimated price.\n",
"4. Returns a verdict: **Overpriced**, **Fair**, or **A Bargain**."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b78e7cb6",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import re\n",
"import json\n",
"from typing import List, Optional\n",
"\n",
"import gradio as gr\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"from langchain_anthropic import ChatAnthropic\n",
"from langchain_community.tools import DuckDuckGoSearchRun\n",
"from langchain_core.tools import tool\n",
"from langchain_core.messages import SystemMessage, HumanMessage, AIMessage, ToolMessage, BaseMessage"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3af032c1",
"metadata": {},
"outputs": [],
"source": [
"load_dotenv(override=True)\n",
"\n",
"FINE_TUNED_MODEL = \"ft:gpt-4.1-nano-2025-04-14:personal:pricer:DFJJ866N\"\n",
"AGENT_MODEL = \"claude-haiku-4-5\"\n",
"\n",
"openai_client = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n",
"llm = ChatAnthropic(model=AGENT_MODEL, temperature=0.0, max_tokens=1800)\n",
"web_search_tool = DuckDuckGoSearchRun()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "bd7d6b4d",
"metadata": {},
"outputs": [],
"source": [
"@tool\n",
"def estimate_price(product_description: str) -> str:\n",
" \"\"\"Estimate the fair market price of a product using the fine-tuned pricing model.\n",
" Pass the full product description or title and details.\"\"\"\n",
" response = openai_client.chat.completions.create(\n",
" model=FINE_TUNED_MODEL,\n",
" messages=[\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": (\n",
" \"Estimate the price of this product. Respond with the price, no explanation\\n\\n\"\n",
" + product_description\n",
" ),\n",
" }\n",
" ],\n",
" max_tokens=20,\n",
" temperature=0.0,\n",
" )\n",
" return response.choices[0].message.content.strip()\n",
"\n",
"\n",
"@tool\n",
"def web_search(query: str) -> str:\n",
" \"\"\"Search the web for real product listings, current prices, and availability.\"\"\"\n",
" return web_search_tool.run(query)\n",
"\n",
"\n",
"@tool\n",
"def compare_prices(estimated_price: float, listing_price: float) -> str:\n",
" \"\"\"Compare the model's estimated fair price against a real listing price and return a deal verdict.\n",
" Both prices should be plain numbers in USD (e.g. 49.99).\"\"\"\n",
" if listing_price <= 0 or estimated_price <= 0:\n",
" return \"Unable to compare: one or both prices are invalid.\"\n",
"\n",
" ratio = listing_price / estimated_price\n",
" diff = listing_price - estimated_price\n",
" direction = \"above\" if diff > 0 else \"below\"\n",
" pct = abs((ratio - 1) * 100)\n",
"\n",
" if ratio <= 0.80:\n",
" verdict = \"Great deal! The listing price is significantly below fair market value.\"\n",
" elif ratio <= 0.95:\n",
" verdict = \"Good deal. The listing price is slightly below fair market value.\"\n",
" elif ratio <= 1.05:\n",
" verdict = \"Fair price. The listing matches the estimated market value.\"\n",
" elif ratio <= 1.20:\n",
" verdict = \"Slightly overpriced. You may find it cheaper elsewhere.\"\n",
" else:\n",
" verdict = \"Overpriced. Consider looking for a better deal.\"\n",
"\n",
" return (\n",
" f\"Estimated fair price: ${estimated_price:.2f}\\n\"\n",
" f\"Listing price: ${listing_price:.2f}\\n\"\n",
" f\"Difference: ${abs(diff):.2f} ({pct:.1f}% {direction} estimate)\\n\"\n",
" f\"Verdict: {verdict}\"\n",
" )\n",
"\n",
"\n",
"tools = [estimate_price, web_search, compare_prices]\n",
"agent_llm = llm.bind_tools(tools)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ca68dfbf",
"metadata": {},
"outputs": [],
"source": [
"SYSTEM_PROMPT = \"\"\"\n",
"You are a Smart Shopping Assistant. Your job is to help users decide if a product is worth buying at a given price.\n",
"\n",
"When a user describes a product or asks about a deal, follow these steps in order:\n",
"1. Call `estimate_price` with the product description to get the fair market value.\n",
"2. Call `web_search` to find real current listings and prices for that product.\n",
"3. Extract a specific listing price from the web search results.\n",
"4. Call `compare_prices` with the estimated price and the listing price (both as plain numbers).\n",
"5. Summarise your findings clearly: the estimated value, what real listings show, and a deal recommendation.\n",
"\n",
"If the user just asks a general shopping question (not about a specific product), answer directly without calling tools.\n",
"Be concise, helpful, and friendly.\n",
"\"\"\"\n",
"\n",
"\n",
"def handle_tool_call(tool_call: dict) -> ToolMessage:\n",
" \"\"\"Execute one tool call and return a LangChain ToolMessage.\"\"\"\n",
" tool_name = tool_call.get(\"name\", \"\")\n",
" tool_args = tool_call.get(\"args\", {})\n",
" tool_call_id = tool_call.get(\"id\", \"\")\n",
"\n",
" if tool_name == \"estimate_price\":\n",
" content = estimate_price.invoke(tool_args)\n",
" elif tool_name == \"web_search\":\n",
" content = web_search.invoke(tool_args)\n",
" elif tool_name == \"compare_prices\":\n",
" content = compare_prices.invoke(tool_args)\n",
" else:\n",
" content = f\"Unknown tool: {tool_name}\"\n",
"\n",
" return ToolMessage(content=str(content), tool_call_id=tool_call_id)\n",
"\n",
"\n",
"def convert_history_to_messages(history: List[dict]) -> List[BaseMessage]:\n",
" \"\"\"Convert Gradio chat history into LangChain message objects.\"\"\"\n",
" messages: List[BaseMessage] = []\n",
" for item in history:\n",
" role = item.get(\"role\", \"\")\n",
" content = str(item.get(\"content\", \"\"))\n",
" if role == \"user\":\n",
" messages.append(HumanMessage(content=content))\n",
" elif role == \"assistant\":\n",
" messages.append(AIMessage(content=content))\n",
" return messages\n",
"\n",
"\n",
"def run_agent(user_text: str, history: Optional[List[dict]] = None) -> str:\n",
" \"\"\"Run the tool-calling agent loop and return the final assistant response.\"\"\"\n",
" if history is None:\n",
" history = []\n",
"\n",
" messages: List[BaseMessage] = [SystemMessage(content=SYSTEM_PROMPT.strip())]\n",
" messages.extend(convert_history_to_messages(history))\n",
" messages.append(HumanMessage(content=user_text))\n",
"\n",
" for _ in range(6):\n",
" ai_message = agent_llm.invoke(messages)\n",
" messages.append(ai_message)\n",
"\n",
" if not ai_message.tool_calls:\n",
" return (\n",
" ai_message.content\n",
" if isinstance(ai_message.content, str)\n",
" else str(ai_message.content)\n",
" )\n",
"\n",
" for tool_call in ai_message.tool_calls:\n",
" messages.append(handle_tool_call(tool_call))\n",
"\n",
" return \"I could not complete the request after several steps. Please try rephrasing.\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b8082ac9",
"metadata": {},
"outputs": [],
"source": [
"def chat_handler(user_text: str, history: List[dict]):\n",
" \"\"\"Handle a chat turn: run the agent and update history.\"\"\"\n",
" if history is None:\n",
" history = []\n",
"\n",
" user_text = (user_text or \"\").strip()\n",
" if not user_text:\n",
" return history, \"\"\n",
"\n",
" response = run_agent(user_text, history)\n",
" updated_history = history + [\n",
" {\"role\": \"user\", \"content\": user_text},\n",
" {\"role\": \"assistant\", \"content\": response},\n",
" ]\n",
" return updated_history, \"\"\n",
"\n",
"\n",
"def build_gradio_app() -> gr.Blocks:\n",
" \"\"\"Build the Gradio UI for the Smart Shopping Assistant.\"\"\"\n",
" with gr.Blocks(title=\"Smart Shopping Assistant\") as demo:\n",
" gr.Markdown(\"# Smart Shopping Assistant\")\n",
" gr.Markdown(\n",
" \"Describe a product you're considering buying. \"\n",
" \"The assistant will estimate its fair market value, search for real listings, \"\n",
" \"and tell you if it's a good deal.\"\n",
" )\n",
" gr.Markdown(\n",
" \"**Example:** *Is a Sony WH-1000XM5 for $280 a good deal?* \"\n",
" \"or *What's a fair price for an Apple Watch Series 9?*\"\n",
" )\n",
"\n",
" chatbot = gr.Chatbot(label=\"Shopping Assistant\", height=450)\n",
" user_input = gr.Textbox(\n",
" label=\"Your question\",\n",
" placeholder=\"Describe a product or ask about a price...\",\n",
" )\n",
" send_btn = gr.Button(\"Send\", variant=\"primary\")\n",
"\n",
" send_btn.click(\n",
" fn=chat_handler,\n",
" inputs=[user_input, chatbot],\n",
" outputs=[chatbot, user_input],\n",
" )\n",
" user_input.submit(\n",
" fn=chat_handler,\n",
" inputs=[user_input, chatbot],\n",
" outputs=[chatbot, user_input],\n",
" )\n",
"\n",
" return demo\n",
"\n",
"\n",
"demo_app = build_gradio_app()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "912addb9",
"metadata": {},
"outputs": [],
"source": [
"demo_app.launch(share=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "llms2",
"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": "3.13.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}