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Copy AIS templates from cookbook. (#635)
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https: //github.com/google-gemini/cookbook/tree/e2623e8587f3d4ae13df65f415a3337c7f7ac97e/templates
Change-Id: Ic50b279a769d2fe2263b1dee41541d7c950c0cfa
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MarkDaoust authored Dec 20, 2024
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332 changes: 332 additions & 0 deletions docs/ais-templates/aistudio_gemini_prompt_freeform.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "Tce3stUlHN0L"
},
"source": [
"##### Copyright 2023 Google LLC"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "tuOe1ymfHZPu"
},
"outputs": [],
"source": [
"# @title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FKwyTRdwB8aW"
},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RXInneX6xx7c"
},
"outputs": [],
"source": [
"!pip install -U -q \"google-generativeai>=0.8.2\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "kWIuwKG2_oWE"
},
"outputs": [],
"source": [
"# import necessary modules.\n",
"import base64\n",
"import copy\n",
"import json\n",
"import pathlib\n",
"import requests\n",
"\n",
"\n",
"import PIL.Image\n",
"import IPython.display\n",
"from IPython.display import Markdown\n",
"\n",
"try:\n",
" # The SDK will automatically read it from the GOOGLE_API_KEY environment variable.\n",
" # In Colab get the key from Colab-secrets (\"🔑\" in the left panel).\n",
" import os\n",
" from google.colab import userdata\n",
"\n",
" os.environ[\"GOOGLE_API_KEY\"] = userdata.get(\"GOOGLE_API_KEY\")\n",
"except ImportError:\n",
" pass\n",
"\n",
"import google.generativeai as genai\n",
"\n",
"# Parse the arguments\n",
"\n",
"model = \"gemini-1.5-flash\" # @param {isTemplate: true}\n",
"contents_b64 = \"W3sicGFydHMiOiBbeyJ0ZXh0IjogIldoYXQncyBpbiB0aGlzIHBpY3R1cmU/In0sIHsiZmlsZV9kYXRhIjogeyJ1cmwiOiAiaHR0cHM6Ly9zdG9yYWdlLmdvb2dsZWFwaXMuY29tL2dlbmVyYXRpdmVhaS1kb3dubG9hZHMvaW1hZ2VzL3Njb25lcy5qcGciLCAibWltZV90eXBlIjogImltYWdlL2pwZWcifX1dfV0=\" # @param {isTemplate: true}\n",
"generation_config_b64 = \"e30=\" # @param {isTemplate: true}\n",
"safety_settings_b64 = \"e30=\" # @param {isTemplate: true}\n",
"\n",
"gais_contents = json.loads(base64.b64decode(contents_b64))\n",
"\n",
"generation_config = json.loads(base64.b64decode(generation_config_b64))\n",
"safety_settings = json.loads(base64.b64decode(safety_settings_b64))\n",
"\n",
"stream = False\n",
"\n",
"# Convert and upload the files\n",
"\n",
"tempfiles = pathlib.Path(f\"tempfiles\")\n",
"tempfiles.mkdir(parents=True, exist_ok=True)\n",
"\n",
"\n",
"drive = None\n",
"def upload_file_data(file_data, index):\n",
" \"\"\"Upload files to the Files API.\n",
"\n",
" For each file, Google AI Studio either sent:\n",
" - a Google Drive ID,\n",
" - a URL,\n",
" - a file path, or\n",
" - The raw bytes (`inline_data`).\n",
"\n",
" The API only understands `inline_data` or it's Files API.\n",
" This code, uploads files to the files API where the API can access them.\n",
" \"\"\"\n",
"\n",
" mime_type = file_data[\"mime_type\"]\n",
" if drive_id := file_data.pop(\"drive_id\", None):\n",
" if drive is None:\n",
" from google.colab import drive\n",
" drive.mount(\"/gdrive\")\n",
"\n",
" path = next(\n",
" pathlib.Path(f\"/gdrive/.shortcut-targets-by-id/{drive_id}\").glob(\"*\")\n",
" )\n",
" print(\"Uploading:\", str(path))\n",
" file_info = genai.upload_file(path=path, mime_type=mime_type)\n",
" file_data[\"file_uri\"] = file_info.uri\n",
" return\n",
"\n",
" if url := file_data.pop(\"url\", None):\n",
" response = requests.get(url)\n",
" data = response.content\n",
" name = url.split(\"/\")[-1]\n",
" path = tempfiles / str(index)\n",
" path.write_bytes(data)\n",
" print(\"Uploading:\", url)\n",
" file_info = genai.upload_file(path, display_name=name, mime_type=mime_type)\n",
" file_data[\"file_uri\"] = file_info.uri\n",
" return\n",
"\n",
" if name := file_data.get(\"filename\", None):\n",
" if not pathlib.Path(name).exists():\n",
" raise IOError(\n",
" f\"local file: `{name}` does not exist. You can upload files \"\n",
" 'to Colab using the file manager (\"📁 Files\" in the left '\n",
" \"toolbar)\"\n",
" )\n",
" file_info = genai.upload_file(path, display_name=name, mime_type=mime_type)\n",
" file_data[\"file_uri\"] = file_info.uri\n",
" return\n",
"\n",
" if \"inline_data\" in file_data:\n",
" return\n",
"\n",
" raise ValueError(\"Either `drive_id`, `url` or `inline_data` must be provided.\")\n",
"\n",
"\n",
"contents = copy.deepcopy(gais_contents)\n",
"\n",
"index = 0\n",
"for content in contents:\n",
" for n, part in enumerate(content[\"parts\"]):\n",
" if file_data := part.get(\"file_data\", None):\n",
" upload_file_data(file_data, index)\n",
" index += 1\n",
"\n",
"import json\n",
"print(json.dumps(contents, indent=4))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "E7zAD69vE92b"
},
"source": [
"## Call `generate_content`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LB2LxPmAB95V"
},
"outputs": [],
"source": [
"from IPython.display import display\n",
"from IPython.display import Markdown\n",
"\n",
"# Call the model and print the response.\n",
"gemini = genai.GenerativeModel(model_name=model)\n",
"\n",
"response = gemini.generate_content(\n",
" contents,\n",
" generation_config=generation_config,\n",
" safety_settings=safety_settings,\n",
" stream=stream,\n",
")\n",
"\n",
"display(Markdown(response.text))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9c9d345e9868"
},
"source": [
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/docs\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />Docs on ai.google.dev</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://github.com/google-gemini/cookbook/blob/main/quickstarts\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />More notebooks in the Cookbook</a>\n",
" </td>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "F91AeeGO1ncU"
},
"source": [
"## [optional] Show the conversation\n",
"\n",
"This section displays the conversation received from Google AI Studio."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "yoL3p3KPylFW"
},
"outputs": [],
"source": [
"# @title Show the conversation, in colab.\n",
"import mimetypes\n",
"\n",
"def show_file(file_data):\n",
" mime_type = file_data[\"mime_type\"]\n",
"\n",
" if drive_id := file_data.get(\"drive_id\", None):\n",
" path = next(\n",
" pathlib.Path(f\"/gdrive/.shortcut-targets-by-id/{drive_id}\").glob(\"*\")\n",
" )\n",
" name = path\n",
" # data = path.read_bytes()\n",
" kwargs = {\"filename\": path}\n",
" elif url := file_data.get(\"url\", None):\n",
" name = url\n",
" kwargs = {\"url\": url}\n",
" # response = requests.get(url)\n",
" # data = response.content\n",
" elif data := file_data.get(\"inline_data\", None):\n",
" name = None\n",
" kwargs = {\"data\": data}\n",
" elif name := file_data.get(\"filename\", None):\n",
" if not pathlib.Path(name).exists():\n",
" raise IOError(\n",
" f\"local file: `{name}` does not exist. You can upload files to \"\n",
" 'Colab using the file manager (\"📁 Files\"in the left toolbar)'\n",
" )\n",
" else:\n",
" raise ValueError(\"Either `drive_id`, `url` or `inline_data` must be provided.\")\n",
"\n",
" print(f\"File:\\n name: {name}\\n mime_type: {mime_type}\\n\")\n",
" return\n",
"\n",
" format = mimetypes.guess_extension(mime_type).strip(\".\")\n",
" if mime_type.startswith(\"image/\"):\n",
" image = IPython.display.Image(**kwargs, width=256)\n",
" IPython.display.display(image)\n",
" print()\n",
" return\n",
"\n",
" if mime_type.startswith(\"audio/\"):\n",
" if len(data) < 2**12:\n",
" audio = IPython.display.Audio(**kwargs)\n",
" IPython.display.display(audio)\n",
" print()\n",
" return\n",
"\n",
" if mime_type.startswith(\"video/\"):\n",
" if len(data) < 2**12:\n",
" audio = IPython.display.Video(**kwargs, mimetype=mime_type)\n",
" IPython.display.display(audio)\n",
" print()\n",
" return\n",
"\n",
" print(f\"File:\\n name: {name}\\n mime_type: {mime_type}\\n\")\n",
"\n",
"\n",
"for content in gais_contents:\n",
" if role := content.get(\"role\", None):\n",
" print(\"Role:\", role, \"\\n\")\n",
"\n",
" for n, part in enumerate(content[\"parts\"]):\n",
" if text := part.get(\"text\", None):\n",
" print(text, \"\\n\")\n",
"\n",
" elif file_data := part.get(\"file_data\", None):\n",
" show_file(file_data)\n",
"\n",
" print(\"-\" * 80, \"\\n\")"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [
"Tce3stUlHN0L"
],
"name": "aistudio_gemini_prompt_freeform.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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