Building Innovative Application 3: Video to Text Prompting

Video-to-text prompting extends image-to-text prompting to the temporal dimension. It uses videos as input to guide large language models (LLMs) in generating textual descriptions, summaries, or answers related to the video content. Instead of a single image, a sequence of frames or encoded video features is used.

This allows the model to capture motion, events, and temporal relationships within the video.

The video demonstrates how to use the Gemini API within Google Colab to analyze and process a video. We first install the necessary libraries and set up the API key. Next, we download a video file and upload it to the Gemini API for processing.

The core functionality of the code lies in its interaction with the Gemini model. We send various prompts, such as summarizing the video, explaining a process within the video, and transcribing the video & audio. The model processes these prompts in conjunction with the uploaded video to generate intelligent responses. These responses are then displayed to us as user.

Overall, this video provides a simple example of leveraging the Gemini API for advanced video analysis and understanding within the Google Colab environment.

It showcases the model & capabilities in summarizing, explaining, and transcribing video content based on our prompts.