{ "cells": [ { "cell_type": "markdown", "id": "db8736a7-ed94-441c-9556-831fa57b5a10", "metadata": {}, "source": [ "# The Product Pricer Continued...\n", "\n", "## Testing Gemini-1.5-pro model" ] }, { "cell_type": "code", "execution_count": null, "id": "681c717b-4c24-4ac3-a5f3-3c5881d6e70a", "metadata": {}, "outputs": [], "source": [ "import os\n", "import re\n", "from dotenv import load_dotenv\n", "import matplotlib.pyplot as plt\n", "import pickle\n", "import google.generativeai as google_genai\n", "import time" ] }, { "cell_type": "code", "execution_count": null, "id": "21a3833e-4093-43b0-8f7b-839c50b911ea", "metadata": {}, "outputs": [], "source": [ "from items import Item\n", "from testing import Tester " ] }, { "cell_type": "code", "execution_count": null, "id": "36d05bdc-0155-4c72-a7ee-aa4e614ffd3c", "metadata": {}, "outputs": [], "source": [ "# environment\n", "load_dotenv()\n", "os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY', 'your-key-if-not-using-env')" ] }, { "cell_type": "code", "execution_count": null, "id": "b0a6fb86-74a4-403c-ab25-6db2d74e9d2b", "metadata": {}, "outputs": [], "source": [ "google_genai.configure()" ] }, { "cell_type": "code", "execution_count": null, "id": "c830ed3e-24ee-4af6-a07b-a1bfdcd39278", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "id": "5c9b05f4-c9eb-462c-8d86-de9140a2d985", "metadata": {}, "outputs": [], "source": [ "# Load in the pickle files that are located in the `pickled_dataset` folder\n", "with open('train.pkl', 'rb') as file:\n", " train = pickle.load(file)\n", "\n", "with open('test.pkl', 'rb') as file:\n", " test = pickle.load(file)" ] }, { "cell_type": "code", "execution_count": null, "id": "fc5c807b-c14c-458e-8cca-32bc0cc5b7c3", "metadata": {}, "outputs": [], "source": [ "# Function to create the messages format required for Gemini 1.5 Pro\n", "# This function prepares the system and user messages in the format expected by Gemini models.\n", "def gemini_messages_for(item):\n", " system_message = \"You estimate prices of items. Reply only with the price, no explanation\"\n", " \n", " # Modify the test prompt by removing \"to the nearest dollar\" and \"Price is $\"\n", " # This ensures that the model receives a cleaner, simpler prompt.\n", " user_prompt = item.test_prompt().replace(\" to the nearest dollar\", \"\").replace(\"\\n\\nPrice is $\", \"\")\n", "\n", " # Reformat messages to Gemini’s expected format: messages = [{'role':'user', 'parts': ['hello']}]\n", " return [\n", " {\"role\": \"system\", \"parts\": [system_message]}, # System-level instruction\n", " {\"role\": \"user\", \"parts\": [user_prompt]}, # User's query\n", " {\"role\": \"model\", \"parts\": [\"Price is $\"]} # Assistant's expected prefix for response\n", " ]" ] }, { "cell_type": "code", "execution_count": null, "id": "d6da66bb-bc4b-49ad-9224-a388470ef20b", "metadata": {}, "outputs": [], "source": [ "# Example usage of the gemini_messages_for function\n", "gemini_messages_for(test[0]) # Generate message structure for the first test item" ] }, { "cell_type": "code", "execution_count": null, "id": "b1af1888-f94a-4106-b0d8-8a70939eec4e", "metadata": {}, "outputs": [], "source": [ "# Utility function to extract the numerical price from a given string\n", "# This function removes currency symbols and commas, then extracts the first number found.\n", "def get_price(s):\n", " s = s.replace('$', '').replace(',', '') # Remove currency symbols and formatting\n", " match = re.search(r\"[-+]?\\d*\\.\\d+|\\d+\", s) # Regular expression to find a number\n", " return float(match.group()) if match else 0 # Convert matched value to float, return 0 if no match" ] }, { "cell_type": "code", "execution_count": null, "id": "a053c1a9-f86e-427c-a6be-ed8ec7bd63a5", "metadata": {}, "outputs": [], "source": [ "# Example usage of get_price function\n", "get_price(\"The price is roughly $99.99 because blah blah\") # Expected output: 99.99" ] }, { "cell_type": "code", "execution_count": null, "id": "34a88e34-1719-4d08-adbe-adb69dfe5e83", "metadata": {}, "outputs": [], "source": [ "# Function to get the estimated price using Gemini 1.5 Pro\n", "def gemini_1_point_5_pro(item):\n", " messages = gemini_messages_for(item) # Generate messages for the model\n", " system_message = messages[0]['parts'][0] # Extract system-level instruction\n", " user_messages = messages[1:] # Remove system message from messages list\n", " \n", " # Initialize Gemini 1.5 Pro model with system instruction\n", " gemini = google_genai.GenerativeModel(\n", " model_name=\"gemini-1.5-pro\",\n", " system_instruction=system_message\n", " )\n", "\n", " # Generate response using Gemini API\n", " response = gemini.generate_content(\n", " contents=user_messages,\n", " generation_config=google_genai.GenerationConfig(max_output_tokens=5)\n", " )\n", "\n", " # Extract text response and convert to numerical price\n", " return get_price(response.text)" ] }, { "cell_type": "code", "execution_count": null, "id": "d89b10bb-8ebb-42ef-9146-f6e64e6849f9", "metadata": {}, "outputs": [], "source": [ "# Example usage:\n", "gemini_1_point_5_pro(test[0])" ] }, { "cell_type": "code", "execution_count": null, "id": "89ad07e6-a28a-4625-b61e-d2ce12d440fc", "metadata": {}, "outputs": [], "source": [ "# Retrieve the actual price of the test item (for comparison)\n", "test[0].price # Output: 374.41" ] }, { "cell_type": "code", "execution_count": null, "id": "384f28e5-e51f-4cd3-8d74-30a8275530db", "metadata": {}, "outputs": [], "source": [ "# Test the function for gemini-1.5 pro using the Tester framework\n", "Tester.test(gemini_1_point_5_pro, test)" ] }, { "cell_type": "markdown", "id": "9b627291-b02e-48dd-9130-703498135ddf", "metadata": {}, "source": [ "## Five, Gemini-2.0-flash" ] }, { "cell_type": "code", "execution_count": null, "id": "0ee393a9-7afd-404f-92f2-a64bb4d5fb8b", "metadata": {}, "outputs": [], "source": [ "# Function to get the estimated price using Gemini-2.0-flash-exp\n", "def gemini_2_point_0_flash_exp(item):\n", " messages = gemini_messages_for(item) # Generate messages for the model\n", " system_message = messages[0]['parts'][0] # Extract system-level instruction\n", " user_messages = messages[1:] # Remove system message from messages list\n", " \n", " # Initialize Gemini-2.0-flash-exp model with system instruction\n", " gemini = google_genai.GenerativeModel(\n", " model_name=\"gemini-2.0-flash-exp\",\n", " system_instruction=system_message\n", " )\n", "\n", " # Adding a delay to avoid hitting the API rate limit and getting a \"ResourceExhausted: 429\" error\n", " time.sleep(5)\n", " \n", " # Generate response using Gemini API\n", " response = gemini.generate_content(\n", " contents=user_messages,\n", " generation_config=google_genai.GenerationConfig(max_output_tokens=5)\n", " )\n", "\n", " # Extract text response and convert to numerical price\n", " return get_price(response.text)" ] }, { "cell_type": "code", "execution_count": null, "id": "203dc6f1-309e-46eb-9957-e06eed803cc8", "metadata": {}, "outputs": [], "source": [ "# Example usage:\n", "gemini_2_point_0_flash_exp(test[0]) " ] }, { "cell_type": "code", "execution_count": null, "id": "a844df09-d347-40b9-bb79-006ec4160aab", "metadata": {}, "outputs": [], "source": [ "# Retrieve the actual price of the test item (for comparison)\n", "test[0].price # Output: 374.41" ] }, { "cell_type": "code", "execution_count": null, "id": "500b45c7-e5c1-44f2-95c9-1c3c06365339", "metadata": {}, "outputs": [], "source": [ "# Test the function for gemini-2.0-flash-exp using the Tester framework\n", "Tester.test(gemini_2_point_0_flash_exp, test)" ] }, { "cell_type": "code", "execution_count": null, "id": "746b2d12-ba92-48e2-9065-c9a108d1593b", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 5 }