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AI Product Image generator
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "id": "c2feecd4-2a16-4960-a2f1-1eac8b55e6b4", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import openai\n", | |
| "import base64\n", | |
| "import os\n", | |
| "from pathlib import Path\n", | |
| "from dotenv import load_dotenv" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "id": "d1d6f7c8-5a56-4991-9653-7c15b9836d16", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "load_dotenv()\n", | |
| "API_KEY = os.environ.get(\"API_KEY\")\n", | |
| "client = openai.OpenAI(api_key=API_KEY)\n", | |
| "\n", | |
| "def encode_image(image_path):\n", | |
| " with open(image_path, \"rb\") as f:\n", | |
| " return base64.b64encode(f.read()).decode(\"utf-8\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "id": "3370eb29-18b9-4798-85da-f37461da7870", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "prompt = \"\"\"Place this product in a natural lifestyle setting. Warm golden-hour lighting, \n", | |
| "shallow depth of field, editorial style. Product details unchanged. \n", | |
| "Instagram-worthy composition.\"\"\"" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "id": "1a52e8eb-dcd9-417c-994b-1dde6584ddc7", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "✅ Saved to product_studio.png\n", | |
| "📊 Token Usage:\n", | |
| " Input tokens: 388\n", | |
| " Output tokens: 4534\n", | |
| " Total tokens: 4922\n", | |
| "\n", | |
| "💰 Estimated Cost:\n", | |
| " Input: $0.003104\n", | |
| " Output: $0.145088\n", | |
| " Total: $0.148192\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "response = client.images.edit(\n", | |
| " model=\"gpt-image-1.5\",\n", | |
| " image=open(\"product1.jpg\", \"rb\"),\n", | |
| " prompt=prompt,\n", | |
| " size=\"1024x1024\",\n", | |
| " n=1\n", | |
| ")\n", | |
| "image_data = base64.b64decode(response.data[0].b64_json)\n", | |
| "Path(\"product_darkbg.png\").write_bytes(image_data)\n", | |
| "print(\"✅ Saved to product_studio.png\")\n", | |
| "\n", | |
| "usage = response.usage\n", | |
| "print(\"📊 Token Usage:\")\n", | |
| "print(f\" Input tokens: {usage.input_tokens}\")\n", | |
| "print(f\" Output tokens: {usage.output_tokens}\")\n", | |
| "print(f\" Total tokens: {usage.total_tokens}\")\n", | |
| "\n", | |
| "input_cost = (usage.input_tokens / 1_000_000) * 8.00 # $8 per 1M input tokens\n", | |
| "output_cost = (usage.output_tokens / 1_000_000) * 32.00 # $32 per 1M output tokens\n", | |
| "total_cost = input_cost + output_cost\n", | |
| "\n", | |
| "print(f\"\\n💰 Estimated Cost:\")\n", | |
| "print(f\" Input: ${input_cost:.6f}\")\n", | |
| "print(f\" Output: ${output_cost:.6f}\")\n", | |
| "print(f\" Total: ${total_cost:.6f}\")" | |
| ] | |
| } | |
| ], | |
| "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": "3.9.4" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 5 | |
| } |
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