DeltaVR3DModelGeneration/3d-generation-pipeline/cloudflare_API_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1dc6faae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import base64\n",
    "import requests\n",
    "from dotenv import load_dotenv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3107275",
   "metadata": {},
   "outputs": [],
   "source": [
    "load_dotenv()\n",
    "\n",
    "ACCOUNT_ID = os.environ[\"CF_ACCOUNT_ID\"]\n",
    "API_TOKEN = os.environ[\"CF_API_TOKEN\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "999adf95",
   "metadata": {},
   "source": [
    "## Text to image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40b35163",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: output.jpg  (263282 bytes)\n",
      "Saved: image9.jpg  (263282 bytes)\n"
     ]
    }
   ],
   "source": [
    "MODEL = \"@cf/black-forest-labs/flux-1-schnell\"\n",
    "URL = f\"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/{MODEL}\"\n",
    "\n",
    "payload = {\n",
    "    \"prompt\": \"a slightly curved broadsword with a fancy golden crossguard\",\n",
    "}\n",
    "\n",
    "headers = {\n",
    "    \"Authorization\": f\"Bearer {API_TOKEN}\",\n",
    "    \"Content-Type\": \"application/json\",\n",
    "}\n",
    "\n",
    "resp = requests.post(URL, json=payload, headers=headers, timeout=60)\n",
    "resp.raise_for_status()\n",
    "\n",
    "data = resp.json()\n",
    "b64 = data[\"result\"][\"image\"]\n",
    "if not b64:\n",
    "    raise RuntimeError(f\"Unexpected response structure: {data}\")\n",
    "\n",
    "img_bytes = base64.b64decode(b64)\n",
    "\n",
    "out_path = \"output.jpg\"\n",
    "with open(out_path, \"wb\") as f:\n",
    "    f.write(img_bytes)\n",
    "\n",
    "print(f\"Saved: {out_path}  ({len(img_bytes)} bytes)\")\n",
    "\n",
    "b64 = data[\"result\"][\"image\"]\n",
    "if not b64:\n",
    "    raise RuntimeError(f\"Unexpected response structure: {data}\")\n",
    "\n",
    "img_bytes = base64.b64decode(b64)\n",
    "\n",
    "out_path = \"image9.jpg\"\n",
    "with open(out_path, \"wb\") as f:\n",
    "    f.write(img_bytes)\n",
    "\n",
    "print(f\"Saved: {out_path}  ({len(img_bytes)} bytes)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14a874c4",
   "metadata": {},
   "source": [
    "## Text prompt refinement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "485f6f46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"dark wooden battleaxe with bronze blade\"\n"
     ]
    }
   ],
   "source": [
    "MODEL = \"@cf/meta/llama-3.2-3b-instruct\"\n",
    "URL = f\"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/{MODEL}\"\n",
    "\n",
    "instructions = \"\"\"\n",
    "User is talking about some object. Your task is to generate a short and concise description of it. Use only user's own words, keep it as short as possible.\n",
    "Example:\n",
    "User: 'Umm, okay, I would like a really cool sword, with for example a bright orange crossguard. And also it should be slightly curved.'\n",
    "You: 'a slightly curved sword with bright orange crossguard'\n",
    "\"\"\"\n",
    "prompt = \"Umm, alright, can you please give me an epic battleaxe? It should have a dark wooden shaft and bronze blade.\"\n",
    "\n",
    "response = requests.post(URL,\n",
    "  headers={\"Authorization\": f\"Bearer {API_TOKEN}\"},\n",
    "  json={\n",
    "    \"messages\": [\n",
    "      {\"role\": \"system\", \"content\": instructions},\n",
    "      {\"role\": \"user\", \"content\": prompt}\n",
    "    ]\n",
    "  }\n",
    ")\n",
    "data = response.json()\n",
    "result_text = data[\"result\"][\"response\"]\n",
    "print(result_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76fa21f0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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