AI is evolving at lightning speed, and one of the most exciting developments right now is Gemini’s multimodal capabilities. Among the top large language models (LLMs), Gemini currently stands out as the only one that can process and analyze full-length videos directly in a prompt.
In this article, we’ll explore how Gemini AI can handle videos, how you can interact with it using the Gemini API, and how to build a simple but powerful Python application that analyzes security footage using this cutting-edge technology.
Step 1: Accessing the Gemini Playground
To get started with Gemini AI:
- Go to Google AI Studio.
- Click on Gemini API.
- Open the Gemini Playground.
- Sign in with your Google account or create one if you don’t have it.
The Gemini Playground allows you to test prompts and experiment with different inputs—text, images, audio, and even video.
Step 2: Uploading and Processing a Video
Once inside the playground:
- Click the “+” icon to upload a file.
- Choose Upload to Drive and select a video from your machine.
Gemini will process the video. For reference:
- A 30-minute video equals approximately 525,000 tokens.
- Gemini Flash can handle about 1 hour of video.
- Gemini 1.5 Pro can process up to 2 million tokens, or around 2 hours of video.
You can also specify a system instruction, like:
“Answer the user’s questions based on the provided video.”
You can then query Gemini about the video’s contents—both visual and audio.
Step 3: Asking Questions About the Video
You can now test Gemini’s capabilities. For instance, asking:
What image is visible when the speaker discusses generating descriptions of images on web pages for accessibility?
Gemini may return:
“A black smartphone displaying a colorful landscape photo. Text ‘audio descriptions’ is visible above, with playback buttons below.”
This demonstrates that Gemini can synchronize the video’s visual elements with its audio transcript, and provide context-aware answers.
Step 4: Comparing Flash and Pro Models
We tested both Gemini Flash and 1.5 Pro:
- Flash is quicker and cheaper—roughly 40.9 seconds to process.
- Pro takes longer but provides more nuanced, detailed responses.
Despite the performance difference, Flash held up impressively, especially considering it’s about 10% the cost of Pro.
Step 5: Building a Python Application Using Gemini API
Now, let’s take it up a notch—building an application using Python and Gemini AI.
a. Setup the Environment
- Open VS Code.
- Create a file:
video_ai.py. - In Google AI Studio, click Get Code at the top right.
- Copy the code snippet into your Python file.
