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‎docs/ais-templates/aistudio_gemini_prompt_freeform.ipynb

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

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