Mulai 29 April 2025, model Gemini 1.5 Pro dan Gemini 1.5 Flash tidak tersedia di project yang belum pernah menggunakan model ini, termasuk project baru. Untuk mengetahui detailnya, lihat
Versi dan siklus proses model.
Menggunakan Gemini untuk meringkas file video lokal
Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Contoh ini menunjukkan cara menggunakan Gemini untuk meringkas file video lokal.
Contoh kode
Kecuali dinyatakan lain, konten di halaman ini dilisensikan berdasarkan Lisensi Creative Commons Attribution 4.0, sedangkan contoh kode dilisensikan berdasarkan Lisensi Apache 2.0. Untuk mengetahui informasi selengkapnya, lihat Kebijakan Situs Google Developers. Java adalah merek dagang terdaftar dari Oracle dan/atau afiliasinya.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],[],[],[],null,["# Use Gemini to summarize local video file\n\nThis sample demonstrates how to use Gemini to summarize a local video file.\n\nCode sample\n-----------\n\n### Python\n\n\nBefore trying this sample, follow the Python setup instructions in the\n[Vertex AI quickstart using\nclient libraries](/vertex-ai/docs/start/client-libraries).\n\n\nFor more information, see the\n[Vertex AI Python API\nreference documentation](/python/docs/reference/aiplatform/latest).\n\n\nTo authenticate to Vertex AI, set up Application Default Credentials.\nFor more information, see\n\n[Set up authentication for a local development environment](/docs/authentication/set-up-adc-local-dev-environment).\n\n from google import genai\n from google.genai.types import HttpOptions, Part\n\n client = genai.Client(http_options=HttpOptions(api_version=\"v1\"))\n model_id = \"gemini-2.5-flash\"\n\n # Read local video file content\n with open(\"test_data/describe_video_content.mp4\", \"rb\") as fp:\n # Video source: https://storage.googleapis.com/cloud-samples-data/generative-ai/video/describe_video_content.mp4\n video_content = fp.read()\n\n response = client.models.generate_content(\n model=model_id,\n contents=[\n Part.from_text(text=\"hello-world\"),\n Part.from_bytes(data=video_content, mime_type=\"video/mp4\"),\n \"Write a short and engaging blog post based on this video.\",\n ],\n )\n\n print(response.text)\n # Example response:\n # Okay, here's a short and engaging blog post based on the climbing video:\n # **Title: Conquering the Wall: A Glimpse into the World of Indoor Climbing**\n # ...\n\nWhat's next\n-----------\n\n\nTo search and filter code samples for other Google Cloud products, see the\n[Google Cloud sample browser](/docs/samples?product=googlegenaisdk)."]]
