From 24a9e1a606e516aa03db3ded3bffd61bb15235c9 Mon Sep 17 00:00:00 2001 From: Emads Date: Fri, 31 Jan 2025 17:20:06 +0200 Subject: [PATCH] Add contributions to week6 community-contributions --- .../ems_week6_day4_gemini_results.ipynb | 313 ++++++++++++++++++ 1 file changed, 313 insertions(+) create mode 100644 week6/community-contributions/ems_week6_day4_gemini_results.ipynb diff --git a/week6/community-contributions/ems_week6_day4_gemini_results.ipynb b/week6/community-contributions/ems_week6_day4_gemini_results.ipynb new file mode 100644 index 0000000..dd8b448 --- /dev/null +++ b/week6/community-contributions/ems_week6_day4_gemini_results.ipynb @@ -0,0 +1,313 @@ +{ + "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 +}